Adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities

IF 3.2 3区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING BMC Medical Imaging Pub Date : 1998-03-12 DOI:10.1117/12.310864
D. Pham, Jerry L Prince
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引用次数: 19

Abstract

We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy c-means algorithm to include a multiplier field, which allows the centroids for each class to vary across the image. First and second order regularization terms ensure that the multiplier field is both slowly varying and smooth. An iterative algorithm that minimizes the objective function is described, and its efficacy is demonstrated on several test images.
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灰度不均匀情况下图像分割的自适应模糊c均值算法
我们提出了一种新的算法,用于获得图像的模糊分割,受到乘法强度不均匀性,如磁共振图像。该算法是通过修改模糊c均值算法中的目标函数来包含一个乘法器域,该乘法器域允许每个类的质心在图像中变化。一阶和二阶正则化项保证了乘法器域的缓慢变化和平滑。描述了一种最小化目标函数的迭代算法,并在多幅测试图像上验证了其有效性。
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来源期刊
BMC Medical Imaging
BMC Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.60
自引率
3.70%
发文量
198
审稿时长
27 weeks
期刊介绍: BMC Medical Imaging is an open access journal publishing original peer-reviewed research articles in the development, evaluation, and use of imaging techniques and image processing tools to diagnose and manage disease.
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