Active Contour Method with Locally Computed Signed Pressure Force Function: An Application to Brain MR Image Segmentation

Farhan Akram, Jeong-Heon Kim, K. Choi
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引用次数: 8

Abstract

This paper presents a region-based active contour method that embeds both region and gradient information. In the proposed algorithm area term practices a new region-based signed pressure force (SPF) function which utilizes the image local information obtained using the local binary fitted (LBF) energy model. By introducing the SPF function based on local fitted image (LFI), the proposed model is able to segment images with intensity in homogeneities. A Gaussian kernel is used to regularize the level set function which not only regularizes it but also removes the need of computationally expensive re-initialization. The proposed segmentation algorithm is applied to synthetic and real images in order to demonstrate the accuracy, effectiveness, and robustness of the algorithm.
局部计算带符号压力函数的主动轮廓法在脑磁共振图像分割中的应用
提出了一种同时嵌入区域和梯度信息的基于区域的活动轮廓线方法。在该算法中,区域术语采用了一种新的基于区域的签名压力(SPF)函数,该函数利用局部二值拟合(LBF)能量模型获得的图像局部信息。通过引入基于局部拟合图像(local fitting image, LFI)的SPF函数,该模型能够对具有均匀性的图像进行强度分割。使用高斯核对水平集函数进行正则化,不仅使其正则化,而且消除了计算开销较大的重新初始化的需要。将所提出的分割算法应用于合成图像和真实图像,以验证该算法的准确性、有效性和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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