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The segmentation of MR images is of great interests in automatic medical diagnosis. However, such images are corrupted by Rician noise and with fuzzy edges. The non-additive and intensity dependant features of Rician noise make image processing very challenging. In this paper, a combination of techniques are carefully selected, tailored, and organized to improve the image quality for automatic segmentation...
We propose a new level set segmentation method with statistical shape prior using a variational approach. The image energy is derived from a robust image gradient feature. This gives the active contour a global representation of the geometric configuration, making it more robust to image noise, weak edges and initial configurations. Statistical shape information is incorporated using nonparametric...
The gradient vector flow (GVF) algorithm has been used extensively as an efficient method for medical image segmentation. This algorithm suffers from poor robustness against noise as well as lack of convergence in small scale details and concavities. As a cure to this problem, in this paper the idea of multi scale is applied to the traditional GVF algorithm for segmentation of brain tumors in MRI...
This paper proposes a novel external force for active contours, called adaptive diffusion flow (ADF). We reconsider the generative mechanism of gradient vector flow (GVF) diffusion process from the perspective of image restoration, and exploit a harmonic hyper surface minimal function to substitute smoothness energy term of GVF for alleviating the possible leakage problem. Meanwhile, a ∞- laplacian...
Mass segmentation plays an important role in many computer-aided diagnosis (CAD) system. It is usually used as the previous step of mass classification. In this paper, we propose one novel scheme for segmentation of breast mass in digitized mammograms, which is based on gradient vector flow (GVF) snake and multi-scale analysis using Gaussian pyramid. In the proposed method, mammogram is decomposed...
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