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2007
IEEE

Automated Grading of Prostate Cancer Using Architectural and Textural Image Features

9 years 3 months ago
Automated Grading of Prostate Cancer Using Architectural and Textural Image Features
The current method of grading prostate cancer on histology uses the Gleason system, which describes five increasingly malignant stages of cancer according to qualitative analysis of tissue architecture. The Gleason grading system has been shown to suffer from inter- and intra-observer variability. In this paper we present a new method for automated and quantitative grading of prostate biopsy specimens. A total of 102 graph-based, morphological, and textural features are extracted from each tissue patch in order to quantify the arrangement of nuclei and glandular structures within digitized images of histological prostate tissue specimens. A support vector machine (SVM) is used to classify the digitized histology slides into one of four different tissue classes: benign epithelium, benign stroma, Gleason grade 3 adenocarcinoma, and Gleason grade 4 adenocarcinoma. The SVM classifier was able to distinguish between all four types of tissue patterns, achieving an accuracy of 92.8% when d...
Scott Doyle, Mark Hwang, Kinsuk Shah, Anant Madabh
Added 03 Jun 2010
Updated 03 Jun 2010
Type Conference
Year 2007
Where ISBI
Authors Scott Doyle, Mark Hwang, Kinsuk Shah, Anant Madabhushi, Michael D. Feldman, John E. Tomaszeweski
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