Image Processing Strategies for Automatic Detection of Common Gastroenterological Diseases

The analysis of Confocal Laser Endomicroscopy (CLE) is one of the techniques used for diagnosing gastroenterological diseases. However, the manual analysis of such images requires training and experience and will often lead to wrong diagnostics. This work explores the use of attributes taken from classic texture description techniques, gray level co-occurrence matrices (GLCM) and local binary patterns (LBP), as inputs for classifiers to separate images from 3 common gastroenterological diseases, with 262 images. A baseline classifier was trained for the 10 smaller groups and two others were trained using GLCM and LBP attributes. Overall, the benefits of using texture analysis techniques and attributes can be observed as an increase in accuracy and consistency of the results.