Automatic Regions of Interest Segmentation for Computer Aided Classification of Prostate Trus Images
Transrectal ultrasound (TRUS) plays two central roles in prostate cancer diagnosis, prostate examination and measurement and biopsy guidance, but its sensitivity and specificity need improvement. This paper presents one possible method to improve TRUS detection and biopsy guidance using computer-aided diagnosis techniques for ultrasound images. The method uses automated segmentation of regions of interest followed by a supervised classifier. It was tested on a database of 37 prostate TRUS RF scans (22 with cancer). Average sensitivity was 78%, average specificity was 92% and average accuracy was 90% in discriminating normal from cancerous tissue.
KeywordsUltrasound Segmentation Tissue characterization Computer aided diagnosis (CAD) Prostate cancer Transrectal ultrasound (TRUS)
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