@inproceedings{1db6d432205b4c3aa9e3b1cd8b379322,
title = "Prostate tumor identification in ultrasound images",
abstract = "There are various medical imaging instruments used for diagnosing prostatic diseases. Ultrasound imaging is the most widely used tool in clinical diagnosis. Urologist outlines the prostate and diagnoses lesions based on his/her experiences. This diagnostic process is subjective and heuristic. Active contour model (ACM) has been successfully applied to outline the prostate contour. However, application of ACM in outlining the contour needs to give the initial contour points manually. In this paper, an automatic prostate tumor identification system is proposed. The sequential floating forward selection (SFFS) is applied to select significant features. A support vector machine (SVM) with radial basis kernel function is used for prostate tumor identification. Experimental results showed that the proposed method achieved higher accuracy than those of other methods.",
author = "Chang, {Chuan Yu} and Tu, {Meng Yu} and Tsai, {Yuh Shyan}",
note = "Publisher Copyright: {\textcopyright} 2014 Springer International Publishing Switzerland.; 1st Euro-China Conference on Intelligent Data Analysis and Applications, ECC 2014 ; Conference date: 13-06-2014 Through 15-06-2014",
year = "2014",
doi = "10.1007/978-3-319-07773-4_2",
language = "English",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "15--24",
editor = "Shyue-Liang Wang and Jeng-Shyang Pan and Vaclav Snasel and Corchado, {Emilio S.} and Ajith Abraham",
booktitle = "Intelligent Data analysis and Its Applications, Volume II - 1st Euro-China Conference on Intelligent Data Analysis and Applications, Proceeding",
address = "Germany",
}