@inproceedings{cb3ff472d5d3485db3b26b45907c5a3c,
title = "Curvelet-based classification of prostate cancer histological images of critical Gleason scores",
abstract = "This paper is aimed at the development of an approach of applying the curvelet transform to images of prostatectomy pathological specimens of critical Gleason grades for computer-aided classification. A set of Tissue MicroArray (TMA) images from the Johns Hopkins University have been used as the data base. We utilize a moving window to sample multiple patches of a given image leading to a majority decision by the patches for image class assignment. The curvelet-based feature extraction may capture both textural and, implicitly, structural information in an image patch. A tree-structured classifier consisting of three Gaussian-kernel support vector machines each with an embedded voting mechanism has been successfully trained and tested yielding high accuracy to classify tissue images of four critical Gleason scores (GS) 3+3, 3+4, 4+3 and 4+4. The experimental result has demonstrated an enhanced performance as compared to other reported works.",
keywords = "Curvelets, Gleason grading, Gleason scores, prostate cancer, tissue texture classification",
author = "Lin, {Wen Chyi} and Li, {Ching Chung} and Christudass, {Christhunesa S.} and Epstein, {Jonathan I.} and Veltri, {Robert W.}",
year = "2015",
month = jul,
day = "21",
doi = "10.1109/ISBI.2015.7164044",
language = "English (US)",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1020--1023",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
note = "12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",
}