Computational Intelligence for Modelling, Control and Automation, International Conference on (2006)
Nov. 28, 2006 to Dec. 1, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CIMCA.2006.75
Narsis Morteza , iust
Ghorashi Alhosseini Analoui , iust
Prostate cancer is one of the most frequent cancers in men and is a major cause of mortality in developed countries. Detection of prostate carcinoma at an early stage is crucial for successful treatment. The ability to diagnose early prostate cancer has outpaced imaging methods for accurate localization and staging of the disease, and for delivering the most appropriate form of therapy needs include improved methods for identification of the boundary of the prostate and localization of the extent of the disease. The method is based on computer assisted image analysis functions being able to assign each picture element (pixel) one tissue class. The classification of pixels and their local features follows a statistical, supervised learning approach and also a method for the analysis of Transrectal ultrasound images aimed at computer-aided diagnosis of prostate cancer is tested in this paper with classifier Hidden Markov Model.
Narsis Morteza, Ghorashi Alhosseini Analoui, "Computer-Aided Detection of Prostate Cancer", Computational Intelligence for Modelling, Control and Automation, International Conference on, vol. 00, no. , pp. 140, 2006, doi:10.1109/CIMCA.2006.75