TY - GEN
T1 - Prostate cancer detection in dynamic MRIs
AU - Chang, Chuan Yu
AU - Hu, Hui Ya
AU - Tsai, Yuh Shyan
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/9
Y1 - 2015/9/9
N2 - In Taiwan, occurrence rate of prostate cancer has been going up over the past few decades. In order to help urologists to detect prostate cancer, a prostate cancer detection system in dynamic MRIs is proposed in this paper. Dynamic MRIs are commonly used for auxiliary tool in clinical study and helpful for diagnosing prostate cancer. Firstly, an ACM (Active Contour Model) is trained and used to segment the prostate. Secondly, 136 features are extracted from the dynamic MRIs after injection at different time (0, 20, 60 and 100 second respectively) and transformed them into RIC curves. Thirdly, 10 discriminative features are selected by FDR (Fisher's Discrimination Ration) and SFFS (Sequential Forward Floating Selection). Finally, the SVM classifier is adopted to classify the segmented prostate into two categories: tumor and normal. Experimental results showed that the accuracy of the proposed method is up to 94.7493%.
AB - In Taiwan, occurrence rate of prostate cancer has been going up over the past few decades. In order to help urologists to detect prostate cancer, a prostate cancer detection system in dynamic MRIs is proposed in this paper. Dynamic MRIs are commonly used for auxiliary tool in clinical study and helpful for diagnosing prostate cancer. Firstly, an ACM (Active Contour Model) is trained and used to segment the prostate. Secondly, 136 features are extracted from the dynamic MRIs after injection at different time (0, 20, 60 and 100 second respectively) and transformed them into RIC curves. Thirdly, 10 discriminative features are selected by FDR (Fisher's Discrimination Ration) and SFFS (Sequential Forward Floating Selection). Finally, the SVM classifier is adopted to classify the segmented prostate into two categories: tumor and normal. Experimental results showed that the accuracy of the proposed method is up to 94.7493%.
UR - http://www.scopus.com/inward/record.url?scp=84961384816&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961384816&partnerID=8YFLogxK
U2 - 10.1109/ICDSP.2015.7252087
DO - 10.1109/ICDSP.2015.7252087
M3 - Conference contribution
AN - SCOPUS:84961384816
T3 - International Conference on Digital Signal Processing, DSP
SP - 1279
EP - 1282
BT - 2015 IEEE International Conference on Digital Signal Processing, DSP 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE International Conference on Digital Signal Processing, DSP 2015
Y2 - 21 July 2015 through 24 July 2015
ER -