# Multivariate Gaussian Bayes classifier with limited data for segmentation of clean and contaminated regions in the small bowel capsule endoscopy images

## TL;DR

This paper introduces a low-cost statistical model to automatically segment clean and contaminated regions in small bowel capsule endoscopy images, aiming to reduce review time for gastroenterologists.

## Contribution

The novel contribution is a multivariate Gaussian Bayes classifier using limited data for automatic segmentation in capsule endoscopy.

## Key findings

- The model achieved high accuracy (0.89 ± 0.07) and AUROC (0.92 ± 0.06) in segmenting clean and contaminated regions.
- The proposed scheme demonstrated good adaptability using datasets from the SEE-AI project and CECleanliness database.

## Abstract

A considerable amount of undesirable factors in the wireless capsule endoscopy (WCE) procedure hinder the proper visualization of the small bowel and take gastroenterologists more time to review. Objective quantitative assessment of different bowel preparation paradigms and saving the physician reviewing time motivated us to present an automatic low-cost statistical model for automatically segmenting of clean and contaminated regions in the WCE images. In the model construction phase, only 20 manually pixel-labeled images have been used from the normal and reduced mucosal view classes of the Kvasir capsule endoscopy dataset. In addition to calculating prior probability, two different probabilistic tri-variate Gaussian distribution models (GDMs) with unique mean vectors and covariance matrices have been fitted to the concatenated RGB color pixel intensity values of clean and contaminated regions separately. Applying the Bayes rule, the membership probability of every pixel of the input test image to each of the two classes is evaluated. The robustness has been evaluated using 5 trials; in each round, from the total number of 2000 randomly selected images, 20 and 1980 images have been used for model construction and evaluation modes, respectively. Our experimental results indicate that accuracy, precision, specificity, sensitivity, area under the receiver operating characteristic curve (AUROC), dice similarity coefficient (DSC), and intersection over union (IOU) are 0.89 ±  0.07, 0.91 ±  0.07, 0.73 ±  0.20, 0.90 ±  0.12, 0.92 ± 0.06, 0.92 ±  0.05 and 0.86 ±  0.09, respectively. The presented scheme is easy to deploy for objectively assessing small bowel cleansing score, comparing different bowel preparation paradigms, and decreasing the inspection time. The results from the SEE-AI project dataset and CECleanliness database proved that the proposed scheme has good adaptability.