Updated until 2017 - to be added more...
Here you can find the Conference paper abstracts for the time being, please visit my Publications page to get the pdf versions of the papers and pre-prints and/or follow the Link (DOI bookmarks) to get the published journal versions.
Title: Controlled Inverse Diffusion models for Image Restoration and Enhancement
DOI Bookmark: 10.1109/ICETET.2008.69
A new class of variational-PDE based models is proposed for edge preserving image enhancement. Using the equivalence relation between regularization and PDEs we propose a general class of models which restore noisy images with selective enhancement. It contains a controlled inverse diffusion term which sharpens the image. Experiments show the effectiveness of the model with real and MRI images.
Title: Robust M-estimators in Early Computer Vision Problems
ICONTSO-08 (Best paper award).
In this paper we review some well known robust statistics methods applied to early computer vision problems such as denoising and selective smoothing. Digital images are corrupted by noise and pose a real challenge in identifying the objects for further pattern recognition processes. By using a discontinuity adaptive measure function, robust M-estimators control the smoothing selectively and remove outliers corresponding to noisy pixels. Classical functions like Huber’s minmax and Tukey’s bisquare functions are used and their performance are evaluated in real and noisy images. A related M-estimator function is proposed and it has an advantage over other functions in terms of image enhancement and good performance under severe noise levels. A fast optimization using gradient descent scheme is used to restore images effectively. The proposed energy function combines the advantages of smooth quadratic and total variation type regularizing functions and is theoretically well-posed. This facilitates the optimization in finding the global minimizer and makes the algorithm stable. We conclude the paper indicating possible future directions in this field.
Title: The Role of Edge Indicator Functions in Perona-Malik Diffusion Equations
Nonlinear diffusion schemes for image processing were introduced by Perona and Malik (Ref. IEEE Trans. Pattern Anal. Machine. Intell., 12(7), 1990) two decades back. Based on image gradients a special diffusion coefficient function drives the process in an edge preserving and even enhancing way. This PDE based approach combines forward diffusion for flat regions and a type of inverse diffusion for edges and features. In this paper we study this famous PDE from the perspective of the diffusion coefficient functions which are key ingredients for effective results. They act as an edge indicator function to diffuse the image selectively and also introduce the nonlinearity in the PDE. Wrongly classified pixels in one iteration of the PDE can disrupt the restoration process and affect the later iterations and using only gradients for edge estimation can lead to over-locality problem. To avoid these drawbacks modifications are proposed in recent times like spatial regularization, adaptive schemes and coupled PDEs. For comparing these approaches we take real image with known classified edge pixels. The methods are numerically implemented and results reported. These results show the effectiveness of using such modifications and explain the importance of choosing a proper diffusion function.
Title: Edge Detectors Based Anisotropic Diffusion for Enhancement of Digital Images
DOI Bookmark: 10.1109/ICVGIP.2008.68
Using the edge detection techniques we propose a new enhancement scheme for noisy digital images. This uses inhomogeneous anisotropic diffusion scheme via the edge indicator provided by well known edge detection methods. Addition of a fidelity term facilitates the proposed scheme to remove the noise while preserving edges. This method is general in the sense that it can be incorporated into any of the nonlinear anisotropic diffusion methods. Numerical results show the promise of this hybrid technique on real and noisy images.
Title: Nonlinear Partial Differential Equations in Computer Vision
To mimic human vision is the ultimate goal of computer vision. Early vision refers to problems such as image denoising, selective smoothing and segmentation for partitioning the image into meaningful objects. Partial differential equations (PDEs) are fast becoming a versatile tool for solving such computer vision problems. In particular early vision problems can be solved effectively and faster by PDE based schemes than standard techniques presently available. In this paper we present such nonlinear schemes from image restoration and segmentation point of view. We review and indicate a connection between such multiscale models with adaptive neighborhood, statistical, and geometrical approaches. Finally a brief state of the art trends in this area are given. We hope to highlight how ‘classical’ mathematical analysis can help the ‘modern’ subjects like computer vision using this short survey.
Title: Ringing Artifact Reduction in Blind Image Deblurring and Denoising Problems by Regularization Methods
DOI Bookmark: 10.1109/ICAPR.2009.57
Image deblurring and denoising are the main steps in early vision problems. A common problem in deblurring is the ringing artifacts created by trying to restore the unknown point spread function (PSF). The random noise present makes this task even harder. Variational blind deconvolution methods add a smoothness term for the PSF as well as for the unknown image. These methods can amplify the outliers correspond to noisy pixels. To remedy these problems we propose the addition of a first order reaction term which penalizes the deviation in gradients. This reduces the ringing artifact in blind image deconvolution. Numerical results show the effectiveness of this additional term in various blind and semi-blind image deblurring and denoising problems.
Title: Color Image Segmentation Based on Vectorial Multiscale Diffusion with Inter-scale Linking
DOI Bookmark: 10.1007/978-3-642-11164-8_55
We propose a segmentation scheme for digital color images using vectorial multiscale anisotropic diffusion. By integrating the edge information, diffusion based schemes can remove noise effectively and create fine to coarse set of images known as scale-space. Segmentation is performed by effectively tracking edges in an inter-scale manner across this scale space family of images. The regions are connected according to color coherency, and scale relation along time axis of the family is taken into account for the final segmentation result. Fast total variation diffusion and anisotropic diffusion facilitate denoising and create homogenous regions separated by strong edges. They provide a road map for further segmentation with persistent edges and flat regions. The scheme is general in the sense that other anisotropic diffusion schemes can be incorporated depending upon the requirement. Numerical simulations show the advantage of the proposed scheme on noisy color images.
Title: Texture Image Segmentation Using Higher Order Derivatives
A variational segmentation model for images with texture is proposed. It relies on the Chan and Vese model, and incorporates an extended structure tensor of the image, defined by a coupling of the image with its first-order spatial derivatives. The average components of the extended structure tensor is also utilized in the model, by means of its gradient. Moreover, a pre-smoothing of this gradient and the structure tensor is performed, which makes the computations robust to noise. As a result, an effective segmentation model is obtained, which distinguishes and segments objects with texture information. For the numerical approximation of this model, a finite element discretization is used, and a Levenberg-Marquard-Newton type optimization method is applied. An application of the proposed scheme is to segment colonic polyps from human intestinal system images, captured by wireless capsule endoscopy. We present experiments on some synthetic and wireless capsule endoscopic textured images, for evaluation and validation of the proposed model.
Title: Colonic Mucosa Detection in Wireless Capsule Endoscopic Images and Videos
Wireless capsule endoscopy (WCE) provides an inner view of the human intestinal system, and helps physicians to identify abnormalities. Usually the WCE produces a huge amount of data (which, in terms of video recordings and per patient, is about 8 hours and approximately 55000 frames of RGB color data). This means the examiner needs to sift through each video for a long time. Digital image processing techniques can be used effectively to reduce this burden, by making use of automatic algorithms. The inner tubular like structure of the human large intestinal tract consist of two major regions. One is the lumen, the intermediate region where the capsule moves. The other part is the mucosa, which is a mucus-secreting membrane lining the lumen cavities. Abnormal tissues and lesions, like ulcers and polyps, are usually seen as exterior parts of the mucosa, and the lumen is filled with intestinal juices of the human digestive system. Here we apply a variational model for segmenting mucosa regions in WCE images and videos. A fast implementation using the Split-Bregman method is also applied to segment videos in real time. Results on WCE images and videos show that the approach provides reasonable segmentations in an efficient manner.
Title: Weighted Laplacian Differences Based Multispectral Anisotropic Diffusion
DOI Bookmark: 10.1109/IGARSS.2011.6050119
We study a multichannel version of nonlinear diffusion PDE which is used to restore noisy multispectral images. Weighted coupling of interchannel edges is done by utilizing fast total variation for each channel. Anisotropic intrachannel smoothing is included to denoise and preserve edges. Numerical results on noisy multispectral images show the advantage of the proposed hybrid approach.
Title: A Segmentation Model and Application to Endoscopic Images
DOI Bookmark: 10.1007/978-3-642-31298-4_20
In this paper a variational segmentation model is proposed. It is a generalization of the Chan and Vese model, for the scalar and vector-valued cases. It incorporates extra terms, depending on the image gradient, and aims at approximating the smoothed image gradient norm, inside and outside the segmentation curve, by mean constant values. As a result, a flexible model is obtained. It segments, more accurately, any object displaying many oscillations in its interior. In effect, an external contour of the object, as a whole, is achieved, together with internal contours, inside the object. For determining the approximate solution a Levenberg-Marquardt Newton-type optimization method is applied to the finite element discretization of the model. Experiments on in vivo medical endoscopic images (displaying aberrant colonic crypt foci) illustrate the efficacy of this model.
Title: Mucosal Region Detection and 3D Reconstruction in Wireless Capsule Endoscopy Videos Using Active Contours
DOI Bookmark: 10.1109/EMBC.2012.6346847
Wireless capsule endoscopy (WCE) provides an inner view of the human digestive system. The inner tubular like structure of the intestinal tract consists of two major regions: lumen - intermediate region where the capsule moves, mucosa - membrane lining the lumen cavities. We study the use of the Split Bregman version of the extended active contour model of Chan and Vese for segmenting mucosal regions in WCE videos. Utilizing this segmentation we obtain a 3D reconstruction of the mucosal tissues using a near source perspective shape-from-shading (SfS) technique. Numerical results indicate that the active contour based segmentation provides better segmentations compared to previous methods and in turn gives better 3D reconstructions of mucosal regions.
Title: Texture Image Segmentation with Smooth Gradients and Local Information
DOI Bookmark: 10.1201/b12753-21
We study a region based segmentation scheme for images with textures based on the gradient information weighted by local image intensity histograms. It relies on the Chan and Vese model without any edge-detectors, and incorporates a new input term, deﬁned by the product of the smoothed gradient and a local histogram of pixel intensity measure of the input image. Segmentation of images with texture objects is performed by effectively differentiating regions displaying different textural information via the local histogram features. A fast numerical scheme based on the dual formulation of the energy minimization is considered. The performance of the proposed scheme is tested on different natural images which contain texture objects.
Title: Segmentation of Breast Cancer Tissue Microarrays for Computer-Aided Diagnosis in Pathology
Computer-Aided Diagnosis (CAD) systems for pathologists can act as an intelligent digital assistant supporting automated grading and morphometric-based discovery of tissue features that are important in cancer diagnosis and patient prognosis. Automated image segmentation is an essential component of computer-based grading in CAD. We describe a novel tissue segmentation algorithm using local feature-based active contours in a globally convex formulation. Preliminary results using the Stanford Tissue MicroArray database shows promising stromal/epithelial superpixel segmentation.
Title: Multichannel Texture Image Segmentation using Weighted Feature Fitting based Variational Active Contours
DOI Bookmark: 10.1145/2425333.2425411
We study an efficient texture image segmentation model for multichannel images using a local feature fitting based active contours scheme. Using a chromaticity-brightness decomposition, we propose a flexible segmentation approach using multi-channel texture and intensity in a globally convex continuous optimization framework. We make use of local feature histogram based weights with the smoothed gradients from the brightness channel and localized fitting for the chromaticity channels. A fast numerical implementation is described using an efficient dual minimization formulation and experimental results on synthetic and real color images indicate the superior performance of the proposed method compared to related approaches. The novel contributions include the use of local feature density functions in the context of a luminance-chromaticity decomposition combined with a globally convex active contour variational method to capture texture variations for image segmentation.
Title: Robust Filtering based Segmentation and Analysis of Dura Mater Vessels using Epifluorescence Microscopy
We show that an adaptive robust filtering based image segmentation model can be used for microvasculature network detection and quantitative characterization in epifluorescence-based high resolution images. Inhomogeneous fluorescence contrast due to the variable binding properties of the lectin marker and leakage hampers accurate vessel segmentation. We use a robust adaptive filtering approach to remove noise and reduce inhomogeneities without destroying small scale vascular structures. An adaptive variance based thresholding method combined with morphological filtering yields an effective detection and segmentation of the vascular network suitable for medial axis estimation. Quantitative parameters of the microvascular network geometry, including curvature, tortuosity, branch segments and branch angles are computed using post segmentation-based medial axis tracing. Experiments using epifluorescence-based high resolution images of porcine and murine microvasculature demonstrates the effectiveness of the proposed approach for quantifying morphological properties of vascular networks.
Title: Robust Periocular Recognition by Fusing Local to Holistic Sparse Representations
DOI Bookmark: 10.1145/2523514.2523540
Sparse representations have been advocated as a relevant advance in biometrics research. In this paper a new algorithm for fusion at the data level of sparse representations is proposed, each one obtained from image patches. The main novelties are two-fold: 1) a dictionary fusion scheme is formalised, using the L1 minimization with the gradient projection method; 2) the proposed representation and classification method does not require the non-overlapping condition of image patches from where individual dictionaries are obtained. In the experiments, we focused in the recognition of periocular images and obtained independent dictionaries for the eye, eyebrow and skin regions, that were subsequently fused. Results obtained in the publicly available UBIRIS.v2 data set show consistent improvements in the recognition effectiveness when compared to state-of-the-art related representation and classification techniques.
Title: Feature Preserving Anisotropic Diffusion For Image Restoration
DOI Bookmark: 10.1109/NCVPRIPG.2013.6776250
Anisotropic diffusion based schemes are widely used in image smoothing and noise removal. Typically, the partial differential equation (PDE) used is based on computing image gradients or isotropically smoothed version of the gradient image. To improve the denoising capability of such nonlinear anisotropic diffusion schemes, we introduce a multi-direction based discretization along with a selection strategy for choosing the best direction of possible edge pixels. This strategy avoids the directionality based bias which can over-smooth features that are not aligned with the coordinate axis. The proposed hybrid discretization scheme helps in preserving multi-scale features present in the images via selective smoothing of the PDE. Experimental results indicate such an adaptive modification provides improved restoration results on noisy images.
Title: Automatic Contrast Parameter Estimation in Anisotropic Diffusion for Image Restoration
DOI Bookmark: 10.1007/978-3-319-12580-0_20
Anisotropic diffusion is used widely in image processing for edge preserving filtering and image smoothing tasks. One of the important class of such model is by Perona and Malik (PM) who used a gradient based diffusion to drive smoothing along edges and not across it. The contrast parameter used in the PM method needs to be carefully chosen to obtain optimal denoising results. Here we consider a local histogram based cumulative distribution approach for selecting this parameter in a data adaptive way so as to avoid manual tuning. We use spatial smoothing based diffusion coefficient along with adaptive contrast parameter estimation for obtaining better edge maps. Moreover, experimental results indicate that this adaptive scheme performs well for a variety of noisy images and comparison results indicate we obtain better peak signal to noise ratio and structural similarity scores with respect to fixed constant parameter values.
Title: Feature Fusion and Label Propagation for Textured Object Video Segmentation
DOI Bookmark: 10.1117/12.2052983
We study an efficient texture segmentation model for multichannel videos using a local feature fitting based active contours scheme. We propose a flexible motion segmentation approach using features computed from texture, intensity components in a globally convex continuous optimization and fusion framework. A fast numerical implementation is described using an efficient dual minimization formulation and experimental results on synthetic and real color videos indicate the superior performance of the proposed method compared to related approaches. The novel contributions include the use of local feature density functions in the context of a luminance-chromaticity decomposition combined with a globally convex active contour variational method to capture texture variations for video object segmentation.
Title: Multi-focus Image Fusion Using Epifluorescence Microscopy for Robust Vascular Segmentation
DOI Bookmark: 10.1109/EMBC.2014.6944682
Automatic segmentation of three-dimensional microvascular structures is needed for quantifying morphological changes to blood vessels during development, disease and treatment processes. Single focus two-dimensional epifluorescent imagery lead to unsatisfactory segmentations due to multiple out of focus vessel regions that have blurred edge structures and lack of detail. Additional segmentation challenges include varying contrast levels due to diffusivity of the lectin stain, leakage out of vessels and fine morphological vessel structure. We propose an approach for vessel segmentation that combines multi-focus image fusion with robust adaptive filtering. The robust adaptive filtering scheme handles noise without destroying small structures, while multi-focus image fusion considerably improves segmentation quality by deblurring out-of-focus regions through incorporating 3D structure information from multiple focus steps. Experiments using epifluorescence images of mice dura mater show an average of 30.4% improvement compared to single focus microvasculature segmentation.
Title: Analyzing the Effects of On-board Sensor Imprecision in WAMI Bundle Adjustment
Camera pose estimation has been explored for the past few decades but still remains an active topic with the prevalence of new sensors and platforms. Among the existing pose estimation methods, Bundle Adjustment (BA) based approaches are robust providing reasonable results even when only partial information is available. BA refers to simultaneously refining the pose of N-cameras and 3D structure of scene points subject to a set of projective constraints such that an appropriate error measure is minimized. Normally, in BA after extracting salient features and establishing correspondences, an estimate of the camera rotation and translation, together known as the camera pose, is obtained using either fundamental or homography matrix estimation. These initial estimates are then used in a triangulation step where corresponding features are geometrically fused to obtain an initial estimate for the 3D point cloud reconstruction of the scene structure. The crucial part of BA is the optimization and refinement steps, given the initial estimates. Unlike general BA utilized in other computer vision tasks where there is often no sensor metadata, in BA for Wide Area Motion Imagery (WAMI) noisy camera pose measurements from on-board sensors are available. This enables us to develop efficient streamlined BA algorithms exploiting sensor and platform geometries and flight paths. We show that the fundamental matrix or homography transformation estimation step can be bypassed, but errors in the metadata due to noisy sensor measurements and adverse operating environments, must be taken into account. In this paper, we analyze the effects of measurement noise in position (from GPS) and rotation (from IMU) sensors on BA results, in terms of accuracy and robustness of the recovered camera parameters using a simulation testbed. We also investigate how matching errors in a sequence of corresponding features used to perform 3D triangulation can affect overall precision. The impact on the robustness of camera pose estimation for N-view BA in the context of large scale WAMI-based 3D reconstruction is discussed.
Title: Elastic Body Spline based Image Segmentation
DOI Bookmark: 10.1109/ICIP.2014.7025888
Elastic body splines (EBS) belonging to the family of 3D splines were recently introduced to capture tissue deformations within a physical model-based approach for non-rigid biomedical image registration. EBS model the displacement of points in a 3D homogeneous isotropic elastic body subject to forces. We propose a novel extension of using elastic body splines for learning driven figure-ground segmentation. The task of interactive image segmentation, with user provided foreground-background labeled seeds or samples, is formulated as learning an interpolating pixel classification function that is then used to assign labels for all unlabeled pixels in the image. The spline function we chose to model the supervised pixel classifier is the Gaussian elastic body spline (GEBS) which can use sparse scribbles from the user and has a closed form solution enabling a fast on-line implementation. Experimental results demonstrate the applicability of the GEBS approach for image segmentation. The GEBS method for interactive foreground image labeling shows promise and outperforms a previous approach using the thin-plate spline model.
Title: Image Inpainting with Modified F-transform
DOI Bookmark: 10.1007/978-3-319-20294-5_73
Restoring damaged images is an important problem in image processing and has been studied for applications such as inpainting missing regions, art restoration. In this work, we consider a modified (fuzzy transform) F-transform for restoration of damages such as holes, scratches. By utilizing weights calculated from known image regions using local variance from patches, we modify the classical F-transform to handle the missing regions effectively with edge preservation and local smoothness. Comparison with interpolation - nearest neighbor, bilinear and modern inpainting - Navier - Stokes, fast-marching methods illustrate that by using our proposed modified F-transform we obtain better results.
Title: Image Restoration with Fuzzy Coefficient Driven Anisotropic Diffusion
DOI Bookmark: 10.1007/978-3-319-20294-5_13
Nonlinear anisotropic diffusion is widely used in image processing and computer vision for various problems. One of the basic and important problem is that of restoring noisy images and diffusion filters are an important class of denoising methods. In this work, we proposed a fuzzy diffusion coefficient which takes into account local pixel variability for better denoising and selective smoothing of edges. By using smoothed gradients along with the fuzzy diffusion coefficient function we obtain edge preserving restoration of noisy images. Experimental results on standard test images and real medical data illustrate that the proposed fuzzy diffusion improves over traditional filters in terms of structural similarity and signal to noise ratio.
Title: Automatic Contrast Enhancement for Wireless Capsule Endoscopy Videos with Spectral Optimal Contrast-tone Mapping
DOI Bookmark: 10.1007/978-81-322-2205-7_23
Wireless capsule endoscopy (WCE) is a revolutionary imaging method for visualizing gastrointestinal tract in patients. Each exam of a patient creates large-scale color video data typically in hours and automatic computer aided diagnosis (CAD) are of important in alleviating the strain on expert gastroenterologists. In this work we consider an automatic contrast enhancement method for WCE videos by using an extension of the recently proposed optimal contrast-tone mapping (OCTM) to color images. By utilizing the transformation of each RGB color from of the endoscopy video to the spectral color space La*b* and utilizing the OCTM on the intensity channel alone we obtain our spectral OCTM (SOCTM) approach. Experimental results comparing histogram equalization, anisotropic diffusion and original OCTM show that our enhancement works well without creating saturation artifacts in real WCE imagery.
Title: Automatic Image Segmentation for Video Capsule Endoscopy
DOI Bookmark: 10.1007/978-981-287-260-9_7
Video capsule endoscopy (VCE) has proven to be a pain-free imaging technique of gastrointestinal (GI) tract and provides continuous stream of color imagery. Due to the amount of images captured automatic computer-aided diagnostic (CAD) methods are required to reduce the burden of gastroenterologists. In this work, we propose a fast and efficient method for obtaining segmentations of VCE images automatically without manual supervision. We utilize an efficient active contour without edges model which accounts for topological changes of the mucosal surface when the capsule moves through the GT tract. Comparison with related image segmentation methods indicate we obtain better results in terms of agreement with expert ground-truth boundary markings.
Title: Color Image Restoration with Fuzzy Gaussian Mixture Model Driven Nonlocal Filter
DOI Bookmark: 10.1007/978-3-319-26123-2_13
Color image denoising is one of the classical image processing problem and various techniques have been explored over the years. Recently, nonlocal means (NLM) filter is proven to obtain good results for denoising Gaussian noise corrupted digital images using weighted mean among similar patches. In this paper, we consider fuzzy Gaussian mixture model (GMM) based NLM method for removing mixed Gaussian and impulse noise. By computing an automatic homogeneity map we identify impulse noise locations and utilize an adaptive patch size. Experimental results on mixed noise affected color images show that our scheme performs better than NLM, anisotropic diffusion and GMM-NLM over di.erent noise levels. Comparison with respect to structural similarity, color image difference, and peak signal to noise ratio error metrics are undertaken and our scheme performs well overall without generating color artifacts.
Title: Multi-scale Anisotropic Filtering of Fluorescence Microscopy for Denoising Microvasculature
DOI Bookmark: 10.1109/ISBI.2015.7163930
Fluorescence microscopy images are plagued by noise and removing it without blurring vascular structures is an important step in automatic image analysis. The application of interest here is to automatically extract the structural components of the microvascular system with accuracy from images acquired by fluorescence microscopy. A robust denoising process is necessary in order to extract accurate biological information. For this purpose, we consider here a multi scale tensor anisotropic diffusion model which progressively switches the amount of smoothing and preserves vessel boundaries accurately. Based on a coherency enhancing flow with planar confidence measure fused with 3D structure information, our method integrates multiple scales for microvasculature preservation and noise removal from background membrane. Experimental results on simulated synthetic images and epi-fluorescence images show the advantage of our improvement over other related diffusion filters. We further show that the proposed multi-scale integration approach improves denoising accuracy of different tensor diffusion methods to obtain better microvasculature segmentation.
Title: On Fuzzification of Color Spaces for Medical Decision Support in Video Capsule Endoscopy
Advances in image and video processing algorithms and the availability of computational resources has paved the way for real-time medical decision support systems to be a reality. Video capsule endoscopy is a direct imaging method for gastrointestinal regions and produces large scale color video data. Fuzzification of color spaces can improve contextual description based tasks that are required in medical decision support. We consider abnormalities detection in video capsule endoscopy using fuzzy sets and logic theory on different colorspaces. Application in retrieval of bleeding detection and polyp vascularization are given as examples of the methodology considered here and preliminary results indicate we obtain promising retrieval results.
Title: Feature Extraction and Disease Stage Classification for Glioma Histopathology Images
DOI Bookmark: 10.1109/HealthCom.2015.7454574
This paper discusses the performance of feature descriptors for disease stage evaluation of Glioma images. In the field of histopathology, many evaluation methods for tissue images have been reported. However, pathologists have to analyze and evaluate many tissue images manually. In addition, the criteria of evaluation heavily depend on each pathologist’s experience and feelings. From this background, studies on computational pathology using computer vision have been reported. The proposed feature descriptors were, however, applied to specified diseases only, and we do not know whether these descriptors will be effective to other tissues or not. This paper applied the feature descriptors defined by previous studies to the Glioma images and investigated the effectiveness of them by using a statistical method. We also discussed a method to distinguish lowgrade from high-grade Glioma images by using the significant descriptors. After the experiments, more than 98% of Glioma images were classified correctly.
Title: Cell Nuclei Segmentation in Glioma Histopathology Images with Color Decomposition based Active Contours
DOI Bookmark: 10.1109/BIBM.2015.7359944
This work discusses the performance of a color decomposition based active contours for segmenting cell nuclei from glioma histopathology. By combining a nuclear staining information obtained from color decomposition with fast variational active contours we obtain unsupervised segmentation of nuclei in histopathological images. Experimental results show promise when compared with different state of the art techniques.
Title: Vascularization Features for Polyp Localization in Capsule Endoscopy
DOI Bookmark: 10.1109/BIBM.2015.7359946
Polyps in gastrointestinal (GI) tract can be precursors to cancer and detecting them early is important in determining their malignancy. Video capsule endoscopy imaging is a useful technology and provides visualization of GI without discomfort to the patient. We consider polyp localization from capsule endoscopy imagery using vascularization features. By using texture features computed from principle curvatures of the image surface, and multiscale directional vesselness stamping we obtain localization of polyps from a given video frame. We present our preliminary results for malignant and benign polyps from capsule endoscopy images.
Title: Automatic Mucosa Detection in Video Capsule Endoscopy with Adaptive Thresholding
DOI Bookmark: 10.1007/978-81-322-2734-2_10
Video capsule endoscopy (VCE) is a revolutionary imaging technique (CAD) systems to aid the experts in making clinically relevant decisions. In this work, we consider an automatic tissue detection method that uses an adaptive entropy thresholding for better separation of mucosa which lines the colon wall from lumen, which is the hollowed gastrointestinal tract. Comparison with other thresholding methods such as Niblack, Bernsen, Otsu, and Sauvola as well as active contour is undertaken. Experimental results indicate that our method performs better than other methods in terms of segmentation accuracy in various VCE videos.
Title: Adaptive Nonlocal Filtering for Brain MRI Restoration
DOI Bookmark: 10.1007/978-3-319-28658-7_48
Brain magnetic resonance images (MRI) plays a crucial role in neuroscience and medical diagnosis. Denoising brain MRI images is an important pre-processing step required in many of the automatic computed aided-diagnosis systems in neuroscience. Recently, nonlocal means (NLM) and variants of these filters, which are widely used in Gaussian noise removal from digital image processing, have been adapted to handle Rician noise which occur in MRI. One of the crucial ingredient for the successful image filtering with NLM is the patch similarity. In this work we consider the use of fuzzy Gaussian mixture model (FGMM) for determining the patch similarity in NLM instead of the usual Euclidean distance. Experimental results with different noise levels on synthetic and brain MRI images are given to highlight the advantage of the proposed approach. Comparison with other image filtering methods our scheme obtains better results in terms of peak signal to noise ratio and structure preservation.
Title: Mixed Noise Removal Using Hybrid Fourth Order Mean Curvature Motion
DOI Bookmark: 10.1007/978-3-319-28658-7_53
Image restoration is one of the fundamental problems in digital image processing. Although there exists a wide variety of methods for removing additive Gaussian noise, relatively few works tackle the problem of removing mixed noise type from images. In this work we utilize a new hybrid partial differential equation (PDE) model for mixed noise corrupted images. By using a combination mean curvature motion (MMC) and fourth order diffusion (FOD) PDE we study a hybrid method to deal with mixture of Gaussian and impulse noises. The MMC-FOD hybrid model is implemented using an efficient essentially non-dissipative (ENoD) scheme for the MMC first to eliminate the impulse noise with a no dissipation. The FOD component is implemented using explicit finite differences scheme. Experimental results indicate that our scheme obtains optimal denoising with mixed noise scenarios and also outperforms related schemes in terms of signal to noise ratio improvement and structural similarity.
Title: Textured Object Video Segmentation using Temporal Coherency
Deformable and articulated objects are hard to segment and track over time. Purely motion based techniques such as optical flow can produce good results in terms of segmenting motion, however they do not incorporate salient object features. In this work, we propose a textured object segmentation method using temporal coherency in videos. We utilize color and texture feature fusion within a fast globally convex active contour method to obtain multi-scale intra frame segmentations. A long distance optical flow based point trajectories is then combined with frame segmentations to obtain label propagation along the temporal direction. Experimental results are given to highlight our method and shows that we obtain dense segmentations even with sparse and noisy initial label set.
Title: Mosaicing with Poisson Blending Regularization for Wide Area Motion Imagery
We study mosaicing for wide area motion imagery (WAMI) data using poisson blending regularization. Exploitation of full motion video and WAMI data is crucial for change detection, target tracking, activity-based recognition and 3D reconstruction. Creating a mosaicing of WAMI data is challenging due to problems such as blurring, ghosting due to parallax and varying image exposures. In this work we review image alignment and panoramic mosaicing techniques for WAMI data and identify corresponding challenges for these existing methods. We describe mosaicing with Poisson blending based regularization which helps avoid blending of colors from different frames. Experimental results show that we obtain seamless mosaics from full motion videos without artifacts associated with existing approaches.
Title: Multiscale Focus Driven Segmentation using Elastic Body Splines
We study a method for extracting blurred or sharp regions of interest (ROI) that could help in initializing an interactive segmentation method using elastic body splines. We use the basic property of sensitivity of derivatives to blur to mask out the ROI and initialize our novel elastic body splines based interactive segmentation technique (EBS). EBS models the elastic deformation of homogeneous isotropic elastic body subjected to external forces. Our initial set of experiments of using a focus driven EBS interactive segmentation method shows promise and has vastly improved the quality of the segmentation over just the Hessian based ROI detection.
Title: Interactive Target Selection in Aerial Imagery using Elastic Body Splines
Video object tracking in aerial imagery is challenging due to complex urban scenes, small support maps for the objects of interest and intensity changes due to shadows. Accuracy of appearance based target trackers depends on efficient and precise segmentation of the object. In this work we study an elastic body spline based interactive target selection method which has proven to be robust even when the input consists of only a few pixels that belong to the object of interest. Elastic body spline belongs (EBS) to family of splines and have been applied for the task of biomedical image registration. It models the elastic deformation of homogeneous isotropic elastic body subjected to external forces. Our proposed method allows the user to select a target by marking only 4-6 pixels and performs an accurate segmentation of the target. The sparse interaction of labeling very few pixels by the user leads to fast target detection and segmentation.
Title: A Study on Feature Extraction and Disease Stage Classification for Glioma Pathology Images
DOI Bookmark: 10.1109/FUZZ-IEEE.2016.7737958
Computer aided diagnosis (CAD) systems are important in obtaining precision medicine and patient driven solutions for various diseases. One of the main brain tumor is the Glioblastoma multiforme (GBM) and histopathological tissue images can provide unique insights into identifying and grading disease stages. In this work, we consider feature extraction and disease stage classification for brain tumor histopathological images using automatic image analysis methods. In particular we utilized automatic nuclei segmentation and labeling for histopathology image data obtained from The Cancer Genome Atlas (TCGA) and check for classification accuracy using support vector machine (SVM), Random Forests (RF). Our results indicate that we obtain classification accuracy 98.9% and 99.6% respectively.
Title: Random Forests for Dura Mater Microvasculature Segmentation Using Epifluorescence Images
DOI Bookmark: 10.1109/EMBC.2016.7591336
Automatic segmentation of microvascular structures is a critical step in quantitatively characterizing vessel remodeling and other physiological changes in the dura mater or other tissues. We developed a supervised random forest (RF) classifier for segmenting thin vessel structures using multiscale features based on Hessian, oriented second derivatives, Laplacian of Gaussian and line features. The latter multiscale line detector feature helps in detecting and connecting faint vessel structures that would otherwise be missed. Experimental results on epifluorescence imagery show that the RF approach produces foreground vessel regions that are almost 20 and 25 percent better than Niblack and Otsu threshold-based segmentations respectively.
Title: Multiquadric Spline-based Interactive Segmentation of Vascular Networks
DOI Bookmark: 10.1109/EMBC.2016.7592074
Commonly used drawing tools for interactive image segmentation and labeling include active contours or boundaries, scribbles, rectangles and other shapes. Thin vessel shapes in images of vascular networks are difficult to segment using automatic or interactive methods. This paper introduces the novel use of a sparse set of user-defined seed points (supervised labels) for precisely, quickly and robustly segmenting complex biomedical images. A multiquadric spline-based binary classifier is proposed as a unique approach for interactive segmentation using as features color values and the location of seed points. Epifluorescence imagery of the dura mater microvasculature are difficult to segment for quantitative applications due to challenging tissue preparation, imaging conditions, and thin, faint structures. Experimental results based on twenty epifluorescence images is used to illustrate the benefits of using a set of seed points to obtain fast and accurate interactive segmentation compared to four interactive and automatic segmentation approaches.
Title: A Study on Nuclei Segmentation, Feature Extraction and Disease Stage Classification for Human Brain Histopathological Images
DOI Bookmark: 10.1016/j.procs.2016.08.164
Computer aided diagnosis (CAD) systems are important in obtaining precision medicine and patient driven solutions for various diseases. One of the main brain tumor is the Glioblastoma multiforme (GBM) and histopathological tissue images can provide unique insights into identifying and grading disease stages. In this study, we consider nuclei segmentation method, feature extraction and disease stage classification for brain tumor histopathological images using automatic image analysis methods. In particular we utilized automatic nuclei segmentation and labeling for histopathology image data obtained from The Cancer Genome Atlas (TCGA) and check for significance of feature discriptors using K-S test and classification accuracy using support vector machine (SVM) and Random Forests (RF). Our results indicate that we obtain classification accuracy 98.6% and 99.8% in the case of Object-Level features and 82.1% and 86.1% in the case of Spatial Arrangement features, respectively.
Title: Confocal Vessel Structure Segmentation with Optimized Feature Bank and Random Forests
DOI Bookmark: 10.1109/AIPR.2016.8010580
In this paper, we consider confocal microscopy based vessel segmentation with optimized features and random forest classification. By utilizing vessel-specific features tuned to capture curvilinear structures such as multiscale Frobenius norm of the Hessian eigenvalues, Laplacian of Gaussians we obtain better segmentation results in challenging imaging conditions. We obtain binary segmentations using random forest classifier trained on physiologists marked ground-truth. Experimental results on mice dura mater confocal microscopy vessel segmentations indicate that we obtain better results compared to global segmentation approaches.
Title: CSANG: Continuous Scale Anisotropic Gaussians for Robust Linear Structure Extraction
DOI Bookmark: 10.1109/AIPR.2016.8010551
Robust estimation of linear structures such as edges and junction features in digital images is an important problem. In this paper, we use an adaptive robust structure tensor (ARST) method where the local adaptation process uses spatially varying adaptive Gaussian kernel that is initialized using the total least-squares structure tensor solution. An iterative scheme is designed with size, orientation, and weights of the Gaussian kernel adaptively changed at each iteration step. Such an adaptation and continuous scale anisotropic Gaussian kernel change for local orientation estimation helps us obtain robust edge and junction features. We consider an efficient graphical processing unit (GPU) implementation which obtained 30x speed improvement over traditional central processing unit (CPU) based implementations. Experimental results on noisy synthetic and natural images indicate that we obtain robust edge detections and further comparison with other edge detectors shows that our approach obtains better localization and accuracy under noisy conditions.
Title: Cloud-based Interactive Image Segmentation Using the FireFly Visualization Tool
DOI Bookmark: 10.1109/AIPR.2016.8010603
User driven interactive image segmentation provides a flexible framework for obtaining supervised object detection in digital images. We have recently proposed a novel user seed points based interactive segmentation approach with spline interpolation. In this work, we combine interactive segmentation method with a web based tool for visualization, segmentation, and tracking tool called Firefly. It is based on a Client-Server model written in Flex and PHP which uses MySQL database for storing the annotations. Firefly provides an automatic analysis of segmentation results by executing MATLAB scripts in the background. We provide user cases in segmenting natural, and biomedical imagery which requires minimal effort and no storage requirement on the user side.
Title: Video Haze Removal and Poisson Blending Based Mini-mosaics for Wide Area Motion Imagery
DOI Bookmark: 10.1109/AIPR.2016.8010552
In this work we consider haze removal from videos using the dark channel prior approach with applications wide area motion imagery (WAMI). Most of the haze-free non-sky outdoor images contain at least one color channel with average intensity close to zero; this channel is called dark channel and provides a good estimate of the transmission of haze which is used to remove haze from image. By utilizing neighborhood consistency in temporal direction of videos we apply haze removal algorithm sequentially. Once the haze-free images are produced, we register them in order to generate mini-mosaics and apply poison blending to remove seam effect from the mini-mosaics. Experimental results on synthetic image sequences and WAMI indicate we obtain good haze-free and mosaiced results.
Title: HEp-2 Cell Classification and Segmentation Using Motif Texture Patterns and Spatial Features with Random Forests
DOI Bookmark: 10.1109/ICPR.2016.7899614
Human epithelial (HEp-2) cell specimens is obtained from indirect immunofluorescence (IIF) imaging for diagnosis and management of autoimmune diseases. Analysis of HEp2 cells is important and in this work we consider automatic cell segmentation and classification using spatial and texture pattern features and random forest classifiers. In this paper, we summarize our efforts in classification and segmentation tasks proposed in ICPR 2016 contest. For the cell level staining pattern classification (Task 1), we utilized texture features such as rotational invariant co-occurrence (RIC) versions of the well-known local binary pattern (LBP), median binary pattern (MBP), joint adaptive median binary pattern (JAMBP), and motif cooccurrence matrix (MCM) along with other optimized features. We report the classification results utilizing different classifiers such as the k-nearest neighbors (kNN), support vector machine (SVM), and random forest (RF). We obtained the best mean class accuracy of 94.29% for six cell classes with RIC-LBP combined with a RIC variant of MCM. For specimen level staining pattern classification (Task 2) we utilize a combination RIC-LBP with RF classifier and obtain 80% mean class accuracy (MCA) for seven classes. For cell segmentation (Task 4), we use our optimized multiscale spatial feature bank along with RF classifier for pixelwise labeling to achieve an F-measure of 84.26% for 1008 images.
Title: Victory sign biometric for terrorists identification: Preliminary results
DOI Bookmark: 10.1109/IACS.2017.7921968
Covering the face and all body parts, sometimes the only evidence to identify a person is their hand geometry, and not the whole hand- only two fingers (the index and the middle fingers) while showing the victory sign, as seen in many terrorists videos. This paper investigates for the first time a new way to identify persons, particularly (terrorists) from their victory sign. We have created a new database in this regard using a mobile phone camera, imaging the victory signs of 50 different persons over two sessions. Simple measurements for the fingers, in addition to the Hu Moments for the areas of the fingers were used to extract the geometric features of the shown part of the hand shown after segmentation. The experimental results using the KNN classifier with three different distance metrics were encouraging for most of the recorded persons; with about 40% to 93% total identification accuracy, depending on the features, distance metric and K used.
Title: Disease stage classification for Glioblastoma Multiforme histopathological images using deep convolutional neural network
This paper discusses the classification accuracy for evaluating disease stage of Glioma histopathological images. In the field of histopathology, computer-assisted diagnosis (CAD) systems are important in obtaining patient-specific diagnosis for various diseases and helps define precision medicine. One of the main brain tumors is the Glioblastoma multiforme (GBM), and histopathological tissue images can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of tedious manual evaluation required for efficient evaluation. In some cases, pathologists diagnose these images during surgery which necessitates instant decision making process. Therefore, there is a demand for quick analysis to do that. In addition, the criteria of evaluation heavily depends on the experience of each pathologist. In this study, the authors consider feature extraction and disease stage classification for brain tumor histopathological images using automatic image analysis methods. In particular, we utilized automatic feature extraction and labeling for histopathological image data given by TCGA and checked the classification accuracy using Convolutional Neural Network (CNN). CNN is one of the popular types of Deep Learning techniques that is increasingly applied in various image analysis problems.
Title: Glioblastoma Multiforme tissue histopathology images based disease stage classification with deep CNN
Recently, many feature extraction methods for histopathology images have been reported for automatic quantitative analysis. One of the severe brain tumor is the Glioblastoma multiforme (GBM) and histopathological tissue images can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of tedious manual evaluation traditionally required for efficient evaluation. In this study, we consider feature extraction and disease stage classification for brain tumor histopathology images using automatic image analysis methods. In particular, we utilized an automatic feature extraction and labeling for histopathology imagery data given by The Cancer Genome Atlas (TCGA) and checked the classification accuracy of disease stages in GBM tissue images using deep Convolutional Neural Network (CNN). Experimental results indicate promise in automatic disease stage classification, and high level of accuracy were obtained for tested image data.
Title: Microvasculature Segmentation of Arterioles Using Deep CNN
Segmenting microvascular structures is an important requirement in understanding angioadaptation by which vascular networks remodel their morphological structures. Accurate segmentation for separating microvasculature structures is important in quantifying remodeling process. In this work, we utilize a deep convolutional neural network (CNN) framework for obtaining robust segmentations of microvasculature from epifluorescence microscopy imagery of mice dura mater. Due to the inhomogeneous staining of the microvasculature, different binding properties of vessels under fluorescence dye, uneven contrast and low texture content, traditional vessel segmentation approaches obtain sub-optimal accuracy. We consider a deep CNN for the purpose keeping small vessel segments and handle the challenges posed by epifluorescence microscopy imaging modality. Experimental results on ovariectomized - ovary removed (OVX) - mice dura mater epifluorescence microscopy images show that the proposed modified CNN framework obtains an highest accuracy of 99% and better than other vessel segmentation methods.
Title: Near-Light Perspective Shape from Shading for 3D Visualizations in Endoscopy Systems
A near-light perspective shape from shading (SfS) technique applied to endoscopy for 3D visualizations of the gastrointestinal tract regions is presented. By utilizing an extensible reflectance model, we study a robust Huber regularization function based variational SfS model. A balancing parameter is used for weighting the irradiance ad smoothness/regularization terms. Experimental results on different endoscopy systems show that we obtain 3D visualizations without shrinkage and dilation artifacts on mucosa tissues associated with other SfS models from the past.
Title: Improving the Generalization of Disease Stage Classification with Deep CNN for Glioma Histopathological Images
In the field of histopathology, computer-assisted diagnosis systems are important in obtaining patient-specific diagnosis for various diseases and help define precision medicine. Therefore, many studies on automatic analysis methods for digital pathology images have been reported. One of the severe brain tumors is the Glioma can provide unique insights into identifying and grading disease stages. However, the number of tissue samples to be examined is enormous, and is a burden to pathologists because of the tedious manual evaluation required for efficient decision-making and diagnosis. Therefore, there is a strong demand for quick and automatic analysis to do that. In this study, we consider feature extraction and disease stage classification for Glioma images using automatic image analysis methods with deep learning techniques. By devising a custom made deep convolutional neural network (CNN) for disease stage classification we apply it on image data available on the cancer genome atlas for brain glioma in histopathology.
Title: 3D Workflow for Segmentation and Interactive Visualization in Brain MR Images using Multiphase Active Contours
In this paper, we are proposing a 3D segmentation and interactive visualization workflow. The segmentation implementation uses a globally convex multiphase active contours without edges. This algorithm has been proven to be initialization independent due to their globally convex formulation and better than other approaches due to robustness to image variations and adaptive energy functionals. The workflow includes a flexible 3D visualization application that can handle very large volumes using multi-resolution hierarchical data formats following the segmentation. We also designed a custom fragment shader that is capable of meaningfully fusing the data from three different volumes: a segmented label volume, a mean value per voxel volume and a skull striped volume for effective visualization without modifying the segmented results. Giving researchers the access to a whole end to end pipeline, from 3D segmentation to custom real time interactive 3D visualization is, in our opinion, a powerful tool focused on an analyst/expert centric workflow.
Title: Image Restoration with Total Variation and Iterative Regularization Parameter Estimation
Regularization techniques are widely used for solving ill-posed image processing problems and in particular for image noise removal. Total variation (TV) regularization is one of the foremost edge preserving methods for noise removal from images that can overcome the over-smoothing effects of the classical Tikhonov regularization. One of the important aspects in this approach is the involvement of the regularization parameter that needs to be set appropriately to obtain optimal restoration results. In this work, we utilize a fast split Bregman based implementation of the TV regularization for denoising along with an iterative parameter estimation from local image information. Experimental results on a variety noisy images indicate the promise of our TV regularization with iterative parameter estimation with local variance method, and comparison with related schemes show better edge preservation and robust noise removal.