This paper explores the feasibility of using multi-frame analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Barett's Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers considerable advantages compared to current methods.

Abstract—Capsule endoscopy, which uses a wireless camera to take images of the digestive track, is emerging as an alternative to traditional wired colonoscopy. A single examination produces a sequence of approximately 50,000 frames. These sequences are manually reviewed, which is time consuming and typically takes about 45–90 minutes and requires the undivided concentration of the reviewer. In this paper, we propose a novel capsule video summarization framework using sparse coding and dictionary learning in feature space.

The segmentation of retinal layers is vital for tracking progress of medication and diagnosis of various eye diseases. To date many methods for the analysis exists, however the speckle noise and shadows of retinal blood vessel remains a challenge, with negative influence on the performance of segmentation algorithms. Previous attempts have been focused on image pre-processing or developing sophisticated models for segmentation to address this problem, but it still remains an area of active research.