Image classification of microscopic colonic images using textural properties and KSOM

• Main Authors: Gan Lim, Laurence A., Naguib, Raouf N. G., Dadios, Elmer Jose P., Avila, Jose Maria C.
• Format: text
• Published: Animo Repository 2010
• Subjects: Colon (Anatomy)—Cancer—Imaging; Self-organizing maps; Diagnostic imaging; Computer Sciences; Medicine and Health Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2813
• Institution: De La Salle University

Colorectal cancer is considered the third most common neoplasm in the world. Traditionally, pathologists use a microscope to examine histopathological images of biopsy samples taken from patients and make judgments based on their professional expertise. Since this procedure is performed by a human expert, it is therefore subject to inconsistencies due to factors that might affect human performance. To overcome this problem, this paper proposes the use of Kohonen self-organising map and Haralick texture in the analysis of microscopic colonic images. The results presented here are preliminary and show great promise. Copyright © 2010 Inderscience Enterprises Ltd.