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• W2330201765 abstract "In this paper, we propose a method that can be used to automatically segment and delineate the suspected lesion regions of parotid glands for computer-aided diagnosis and treatment planning on clinical applications. Because the suspected lesions are unpredictable, it needs to be found by distinguishing the local features of soft tissues. The proposed method starts from image sub-band decomposition and the gathered coefficients are utilized to derived local feature descriptors. The pixels of soft tissue regions can de segmented based on the corresponding local features and the pathological regions can be localized. In order to improve the accuracy of segmentation, the active contour method is then involved based on the segmentation results as the initial conditions. The active contour method is more sensitive to the gradient variation such that the initial conditions based on prior segmentation can help converge to the weak boundaries between pathological tissues and normal tissues. In this paper, the effective method that can improve medical automation is described, and the results are compared with the contour directly found by clinical experts. As a result, the experiment of H&N CT images with parotid lesions shows that the accuracy can approach over 94%." @default.
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• W2330201765 date "2013-01-01" @default.
• W2330201765 modified "2023-09-23" @default.
• W2330201765 title "A Method for Automatic Extraction of Parotid Lesions in CT Images with Feature-Based Segmentation and Active Contour" @default.
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• W2330201765 doi "https://doi.org/10.12720/ijsps.1.2.225-231" @default.
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