Philippine Science Letters
vol. 3 | no. 1 | 2010
published online June 17, 2010


ARTICLE


Analysis of colonic histopathological images using pixel intensities and Hough Transform


by Laurence A. Gan Lim1,2,*, Raouf N.G. Naguib1, Elmer P. Dadios3, Jose Maria C. Avila4

1 Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group, Coventry University, Coventry, U.K.
2 Mechanical Engineering Department, De La Salle University, Manila, Philippines
3 Manufacturing Engineering and Management Department, De La Salle University, Manila, Philippines
4 Department of Pathology, University of the Philippines, Manila, Philippines



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Cancer of the colon is among the leading types of cancer. Worldwide, colorectal cancer is considered the third most common neoplasm. Similar to other types of cancers, early detection of colon cancer is the key to a successful treatment. Traditionally, pathologists use microscope to examine histopathological images of biopsy samples taken from patients and make judgments based on their professional expertise. Typically, a pathologist would make observations on some key features in the image and subsequently be able to classify whether or not the tissue under examination contains abnormality. 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, it has been proposed to use computers to aid in the analysis of medical images, such as histopathological images of colonic tissues. One of the strongest signs of abnormal cellular growth is abundance of DNA material in the nuclei, a condition known as hyperchromasia. Another sign is a ‘chaotic’ appearance of tissue usually observed during histological viewing. Cancer is known to be characterized by abnormal and excessive proliferation of cells and larger-than-usual nuclei. These effects are visually manifested by a darkening of the regions with excess DNA due to reaction to staining and a loss of structural order in the tissue. In this paper, pixel intensity and the presence of circular formations are examined separately as discriminating image features to distinguish between normal and abnormal samples. The images used in this study were derived from slides and cases randomly chosen from surgical pathology files of a hospital, previously diagnosed by pathologists as colonic adenocarcinoma, adenomatous polyps from the colon, as well as resection planes of the colonic resections without tumor to serve as controls. All samples were stained with hematoxylin and eosin and were taken at 400x magnification. The results showed very promising results.


*Corresponding author
Email Address: ganliml@uni.coventry.ac.uk
Submitted: February 25, 2010
Revised: May 11, 2010
Accepted May 11, 2010
Published: June 17, 2010
Editor-in-charge: Eduardo A. Padlan