Multifeature Prostate Cancer Diagnosis and Gleason Grading of Histological Images

• Published in: IEEE transactions on medical imaging Vol. 26; no. 10; pp. 1366 - 1378
• Main Authors: Tabesh, A., Teverovskiy, M., Ho-Yuen Pang, Kumar, V.P., Verbel, D., Kotsianti, A., Saidi, O.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2007; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aggregates; Algorithms; Artificial Intelligence; Biological tissues; Cancer; Classification; Color; Colorimetry - methods; Computer-aided diagnosis; Diagnosis; Evaluation; Glands; Gleason grading; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image retrieval; Laboratories; Male; Neoplasms; Pattern Recognition, Automated - methods; Prostate; Prostate cancer; Prostatic Neoplasms - classification; Prostatic Neoplasms - pathology; Reproducibility of Results; Sensitivity and Specificity; statistical pattern recognition; Supervised learning; Support vector machines; Texture; Tumors
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2007.898536

We present a study of image features for cancer diagnosis and Gleason grading of the histological images of prostate. In diagnosis, the tissue image is classified into the tumor and nontumor classes. In Gleason grading, which characterizes tumor aggressiveness, the image is classified as containing a low- or high-grade tumor. The image sets used in this paper consisted of 367 and 268 color images for the diagnosis and Gleason grading problems, respectively, and were captured from representative areas of hematoxylin and eosin-stained tissue retrieved from tissue microarray cores or whole sections. The primary contribution of this paper is to aggregate color, texture, and morphometric cues at the global and histological object levels for classification. Features representing different visual cues were combined in a supervised learning framework. We compared the performance of Gaussian, -nearest neighbor, and support vector machine classifiers together with the sequential forward feature selection algorithm. On diagnosis, using a five-fold cross-validation estimate, an accuracy of 96.7% was obtained. On Gleason grading, the achieved accuracy of classification into low- and high-grade classes was 81.0%.