Useful GLCM textural properties in the classification of colonic mucosa microscopic images

Authors

Laurence A. Gan Lim, De La Salle University, Manila
Raouf N. G. Naguib, De La Salle University, Manila
Elmer P. Dadios, De La Salle University, Manila
Debbie de la Fuente, De La Salle University, Dasmarinas

College

Gokongwei College of Engineering

Department/Unit

Mechanical Engineering

Document Type

Conference Proceeding

Source Title

Pacific Asia Conference on Mechanical Engineering (PACME) 2007

Publication Date

2007

Abstract

This paper reports about extraction and analysis of textural features of colonic mucosa microscopic images. The data presented here is a preliminary result of a much larger study on automatic classification of colonic mucosa microscopic images using textural features and AI structures proposed by Gan Lim et al. (2007). The images used were initially classified by a human expert into three classifications: normal, neoplastic, and malignant. A total of 14 features were considered and analysis of the features showed that the mean, correlation, sum average, and sum variance were more effective in discriminating the images compared to other GLCM-derived properties.

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Recommended Citation

Gan Lim, L. A., Naguib, R. G., Dadios, E. P., & de la Fuente, D. (2007). Useful GLCM textural properties in the classification of colonic mucosa microscopic images. Pacific Asia Conference on Mechanical Engineering (PACME) 2007 Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/5832

Disciplines

Computer Engineering

Keywords

Colon (Anatomy)—Cancer—Imaging; Three-dimensional imaging

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