Texture guided active appearance model propagation for prostate segmentation
MICCAI'10 Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention
Feature analysis and classification of lymph nodes
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
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Prostate hyperplasia is usually found affecting male adults in developed countries. Transrectal ultrasoundgraphy (TRUS) imaging is widely used to diagnose prostate disease. Ultrasonic images are often argued with their primitive echo perturbations and speckle noise, which may confuse the physicians in inspection. Therefore, in this paper, we propose an automatic prostate segmentation system in TRUS images. The automatic segmentation system utilizes a prostate classifier which consists of 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).