Major Research Areas/Interests/Projects - Bioinformatics
I. Biomedical Signal/Image/Video Processing and Analysis
Application of signal processing, image processing, computer vision and machine learning techniques to biomedical data - Magnetic Resonance (MR), Computed Tomography (CT), Positron emission tomography (PET), Single-Photon Emission Computed Tomography (SPECT), Optical Coherence Tomography (OCT), X-ray, Dual energy X-ray absorptiometry (DEXA), Ultrasound, Wireless Capsule Endoscopy, Colonoscopy, Histopathology, Confocal, Fluorescence, Magnetic Resonance Angiography (MRA), Fluorescein Angiogram, Color Fundus, Two-photon Microscopy, Mass Spectrometry Imaging (MSI), Matrix-assisted Laser Desorption/Ionization (MALDI), Mammography, Cryo-EM, cDNA Microarray images, Laryngeal High-Speed Videos, Electroencephalography (EEG), Electrocardiography (ECG), MR Elastography, Calcium Imaging. Image reconstruction, compressed sensing, enhancement, noise removal, compression of endoscopic videos, image segmentation based on active contours, shape based (Shape from Shading, Structure from Motion) approaches for endoscopic images. Machine learning, and deep learning for biomedical image analysis - Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Generative Adversarial Network (GAN), Recurrent Neural Network (RNN), Transformers,.... Bioinformatics, visualization and interpretation of biomedical imaging data. Segmentation and quantitative image analysis for magnetic resonance, histopathological, fluorescence microscopy images. E-health, telemedicine, m-health related data processing. Computational neuroscience, transcranial direct current stimulation (tDCS), fMRI analysis. Clinical implementation of machine learning, deep learning models for imaging and nonimaging based data, clinical informatics.
Endoscopy : MucosaSeg, 3D-SfS, Stamping, Illumination, Polyps, Bleeding, Distortion, Stereo, Registration, Compression, Summary, Polypseg, Quality, Celiac, Tumor, Ulcer, ArcEndos, Colonopolyps (coming soon)
Mammography : Segmentation, Enhancement, Registration (coming soon)
Video Analysis : CP/Gait Tracking, 3D Cell Tracking (coming soon)
II. Machine Learning for Bioinformatics/Computational Biology - To Be Added
Application of machine/deep learning to single cell RNA sequence (scRNAseq), Assay for Transposase-Accessible Chromatin using sequence (ATAC-seq), etc. Analysis of high throughput sequencing genomics data (ChIP-Seq, DNase-Seq, and/or ATAC-Seq). Deep learning, explainable AI for computational biology problems - quality control (QC), clustering, denoising, imputation, trajectory inference, immunogenicity prediction, RNA binding protein prediction, transcription factor binding prediction. Analysis of multiplexed imaging, mass spectrometry imaging (MSI), CODEX imaging, spatial transcriptomics in tandem with single-cell genomics data and in general multi-omics analysis. Exploration of latent structures in single cell data, finding common embeddings/integration for mutimodal biological data.
1. Clustering, DeepImmuno, scTriangulate, MaxATAC, DL4Bio (coming soon)
Other Projects - Applied Mathematics - Imaging/Data Science
Note that these contain prior major research projects in the areas of applied mathematics, image processing, computer vision 2004 to 2018 i.e. before the start of Prasath Lab @CCHMC in March 2018.
I. Analysis & PDEs with Image Processing Applications
Nonlinear, anisotropic diffusion PDEs, Weak/viscosity/dissipative/Young measure solutions, Perona-Malik type diffusion PDEs, variable exponent PDEs, p-Laplacian, p(t,x)-Laplacian, complex diffusion, higher order PDEs, adaptive PDEs and computational methods (Finite Differences, Finite Elements) for solving them are major themes. Linear, nonlinear scale space theory and applications - smoothing, denoising, segmentation, decomposition.
Mono-channel (Gray-scale) : CoupledPDEs, AFBD, ABO4, Fractional, Infinity, Rinse, Ahana (coming soon)
Multi-channel (Color, Multispectral, Hyperspectral) : MultiAD, VTV-denoise, CMAC, VarEx, MMIS, CEDzoo, Hyper (coming soon)
Regularization: MTTV, MAC, CMAC, PIDTGV, SIMREN, Gradfit, M2AC, Modseg, CBseg, Fusion, OmniReg, TVzoo, L0z, Cartoon, Featurefit, REPAIR, Super, OCT (coming soon)
Total variation: VTV-denoise, Decomposition, AdaptiveTV/wTV (coming soon)
III. Remote Sensing, Biometrics, Other Image Processing/Computer Vision Problems and Non-Imaging Domains
Image speckle denoising, segmentation for SAR, PolSAR images. Road network extraction from aerial imagery. Wavelets, Shearlets for image processing. Regression analysis, Robust M-estimators, Discontinuity adaptive smoothing schemes and Kernel smoothing. Image and data fusion, multi-focus fusion, multi-sensor fusion, sensor networks. Biometrics - ocular, periocular, fingerprint, iris, retina, face, palm print. Multi-view geometry, shape from X, segmentation, optical flow, mosaicing, blending, registration, point cloud processing, large scale 3D reconstruction for full motion video (FMV), wide area motion imagery (WAMI), video surveillance, summarization, event detection. DTM/DEM, edge detection, super-resolution, deblocking, decompression, saliency detection, watermarking, steganography, Kinect depth data processing, local binary patterns, registration, video data analysis. Feature analysis, deep learning for image processing and computer vision problems. Sensor networks with emphasize on visual sensors, internet of things (IoT), natural language processing (text mining), affective computing (sentiment analysis from text, social media data, emotion recognition from image data).
Remote Sensing: Shadows, STLLT, PolSARSeg, Clouds, Roads, WAMI (coming soon)
Biometrics : Periocular, V-sign, Veil, Fingerprint, Iris (coming soon)
Image quality : MSID, BriCho (coming soon)
tDCS : Brief history, Plasticity, Adverse, Electrodes, Safety (coming soon)
Misc : FashionableGAN, Splineseg, CSANG, LOHI, SSTEdges, STEAD, RC-BA, GeLaDA, Weld, LSS3D, P3D, Entrans, Fish, Traffic signs (coming soon)
To be updated soon with more projects! meantime you can take a look at the publications page.