Automatic diagnosis for prostate cancer using run-length matrix method

Xiaoyan Sun; Shao-Hui Chuang; Jiang Li; Frederic McKenzie

doi:10.1117/12.811414

27 February 2009 Automatic diagnosis for prostate cancer using run-length matrix method

Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, Frederic McKenzie

Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72603H (2009) https://doi.org/10.1117/12.811414
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Prostate cancer is the most common type of cancer and the second leading cause of cancer death among men in US1. Quantitative assessment of prostate histology provides potential automatic classification of prostate lesions and prediction of response to therapy. Traditionally, prostate cancer diagnosis is made by the analysis of prostate-specific antigen (PSA) levels and histopathological images of biopsy samples under microscopes. In this application, we utilize a texture analysis method based on the run-length matrix for identifying tissue abnormalities in prostate histology. A tissue sample was collected from a radical prostatectomy, H&E fixed, and assessed by a pathologist as normal tissue or prostatic carcinoma (PCa). The sample was then subsequently digitized at 50X magnification. We divided the digitized image into sub-regions of 20 X 20 pixels and classified each sub-region as normal or PCa by a texture analysis method. In the texture analysis, we computed texture features for each of the sub-regions based on the Gray-level Run-length Matrix(GL-RLM). Those features include LGRE, HGRE and RPC from the run-length matrix, mean and standard deviation of the pixel intensity. We utilized a feature selection algorithm to select a set of effective features and used a multi-layer perceptron (MLP) classifier to distinguish normal from PCa. In total, the whole histological image was divided into 42 PCa and 6280 normal regions. Three-fold cross validation results show that the proposed method achieves an average classification accuracy of 89.5% with a sensitivity and specificity of 90.48% and 89.49%, respectively.

Citation Download Citation

Xiaoyan Sun, Shao-Hui Chuang, Jiang Li, and Frederic McKenzie "Automatic diagnosis for prostate cancer using run-length matrix method", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72603H (27 February 2009); https://doi.org/10.1117/12.811414

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