| Summary: | 碩士 === 國立雲林科技大學 === 資訊工程研究所 === 97 === Recently, Transrectal ultrasoundgraphy (TRUS) imaging is widely used to diagnose prostate disease. Before a physician can diagnose prostate lesions, contour of the prostate in TRUS images must be manually outlined. However, manual segmentation is time-consuming and inefficient. Therefore, an automatic segmentation of prostate in TRUS images is necessary. Among the segmentation methods, active contour model (ACM) is a successful contour detection method. But the shortcoming of ACM is that the determination of the initial contour is manual. Thus, in this paper, an automatic neural-network-based prostate segmentation method in TRUS images is proposed, which can omit the complicated step of determine the initial contour. The proposed system consists of the Validation Incremental Neural Network and Radial-Basis Function Neural Networks for prostate segmentation. Experimental results show that the proposed method has higher accuracy than Active Contour Model (ACM).
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