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- W2897022333 abstract "The contour extraction of prostate in magnetic resonance imaging (MRI) plays a significant role in clinical diagnosis and related medical research. We propose a level set model, which incorporates shape priors and the gradient information of the image into C-V model which was submitted by Chan T F and Vese L for prostate MRI segmentation, and can segment the prostate contour in MRI with high precision. In our model, prior shape information is used as the external force, and the regional information as well as gradient information is used as internal force to drive curve evolution. Then the evolution curve is modified with the information of the image itself under the constraint of prior shape. Due to the addition of local gradient information, this method can better deal with the situation when there only exists small gray-level difference between the background and target area of medical image. Meanwhile, the accuracy and robustness of segmentation are improved. Finally, the experimental results and quantitative analysis show that the level set algorithm with priori shape information can adapt to different forms of the prostate better, and the addition of gradient information avoids the situation that traditional CV model unable to process the images with intensity inhomogeneity, which demonstrate the potential of the approach proposed in the prostate MRI segmentation." @default.
- W2897022333 created "2018-10-26" @default.
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- W2897022333 date "2018-08-01" @default.
- W2897022333 modified "2023-10-18" @default.
- W2897022333 title "For Prostate MRI Segmentation: A Prior-shape-based Level Set Model Combined with Gradient and Regional Information" @default.
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- W2897022333 doi "https://doi.org/10.1109/icma.2018.8484342" @default.
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