Illumination Estimation in Capsule Endoscopy Images

Abstract:

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 these works, we consider automatic contrast enhancement methods for WCE videos.

Example Illumination Correction Results:

The following examples illustrates the proposed SOCTM based contrast tone mapping [1] for Capsule Endoscopy image:

Input

SOCTM [1]

Example AI-based Exposure Correction Results:

The following examples illustrates a generative AI model for Capsule Endoscopy images:

Input

AI-based [2]

References:


[1] V. B. S. Prasath, R. Delhibabu. Automatic contrast enhancement for wireless capsule endoscopy videos with spectral optimal contrast-tone mapping. International Conference on Computational Intelligence in Data Mining (ICCIDM), Burla, India, Dec 20-21, 2014. Proc. Springer Smart Innovation, Systems and Technologies, (eds. L. C. Jain, H. S. Behera, J. K. Mandal, D. P. Mohapatra), pp. 243-250, 2015. doi:10.1007/978-81-322-2205-7_23 


[2] V. B. S. Prasath. Illuminating the Way – Benchmarking AI-based exposure correction in capsule endoscopy videos. In preparation, 2024. Preliminary work presented at 7th Annual IBDHorizons Midwest Symposium, Cincinnati, 2024.


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