Computer-aided prostate cancer detection using texture features and clinical features in ultrasound image.
In this paper, we propose a new prostate detection method using multiresolution autocorrelation texture features and clinical features such as location and shape of tumor. With the proposed method, we can detect cancerous tissues efficiently with high specificity (about 90-95%)and high sensitivity (about 92-96%) by the measurement of the number of correctly classified pixels. Multiresolution autocorrelation can detect cancerous tissues efficiently, and clinical knowledge helps to discriminate the cancer region by location and shape of the region and increases specificity. The support vector machine is used to classify tissues based on those features. The proposed method will be helpful in formulating a more reliable diagnosis, increasing diagnosis efficiency.
Han
Seok Min
SM
Department of Electrical and Computer Engineering, Seoul National University, Seoul, Republic of Korea.
Lee
Hak Jong
HJ
Choi
Jin Young
JY
D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation / Q000379:methods; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011471:Prostatic Neoplasms / Q000175:diagnosis / Q000000981:diagnostic imaging*; D012680:Sensitivity and Specificity; D018608:Ultrasonography, Doppler / Q000379:methods