@inproceedings{31510acc6efb43c1b985bb0f90824856,
title = "Gleason grade-based automatic classification of prostate cancer pathological images",
abstract = "In this Paper, we introduce a new method for automatic recognition and classification of prostate cancer biopsy images based on Gleason grading system. The introduced algorithm combines features from wavelet transform and fractal analysis domains. Biopsy images are pre-processed prior to features extraction using effective image processing algorithms to analyze textural complexity in terms of RGB color channels, edge and segmentation information. Experimental results achieved an average classification accuracy of 95 % in a set of 45 images with diversities in resolution, magnification levels, and stain colors.",
keywords = "Gleason grading, Prostate cancer, fractal dimension, statistical classification, wavelet features",
author = "Ali Almuntashri and Sos Agaian and Ian Thompson and Danny Rabah and {Zin Al-Abdin}, Osman and Marlo Nicolas",
year = "2011",
doi = "10.1109/ICSMC.2011.6084080",
language = "English (US)",
isbn = "9781457706523",
series = "Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics",
pages = "2696--2701",
booktitle = "2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest",
note = "2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 ; Conference date: 09-10-2011 Through 12-10-2011",
}