title: A new kernel-based fuzzy level set method for automated segmentation of medical images in the presence of intensity inhomogeneity.
abstract: Researchers recently apply an integrative approach to automate medical image segmentation for benefiting available methods and eliminating their disadvantages. Intensity inhomogeneity is a challenging and open problem in this area, which has received less attention by this approach. It has considerable effects on segmentation accuracy. This paper proposes a new kernel-based fuzzy level set algorithm by an integrative approach to deal with this problem. It can directly evolve from the initial level set obtained by Gaussian Kernel-Based Fuzzy C-Means (GKFCM). The controlling parameters of level set evolution are also estimated from the results of GKFCM. Moreover the proposed algorithm is enhanced with locally regularized evolution based on an image model that describes the composition of real-world images, in which intensity inhomogeneity is assumed as a component of an image. Such improvements make level set manipulation easier and lead to more robust segmentation in intensity inhomogeneity. The proposed algorithm has valuable benefits including automation, invariant of intensity inhomogeneity, and high accuracy. Performance evaluation of the proposed algorithm was carried on medical images from different modalities. The results confirm its effectiveness for medical image segmentation.
authors.affiliation: Department of Computer Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad University, P.O. Box 14515/775, Tehran 1477893855, Iran.
authors.forename: Maryam
authors.identifier: 0000-0002-7095-151X
authors.initials: M
authors.lastname: Rastgarpour
authors.affiliation: Department of Computer Engineering, Faculty of Engineering, Kharazmi University, Tehran 14911-15719, Iran.
authors.forename: Jamshid
authors.initials: J
authors.lastname: Shanbehzadeh
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology; D016000:Cluster Analysis; D003952:Diagnostic Imaging / Q000379:methods*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008490:Medical Informatics; D015233:Models, Statistical; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012984:Software; D014057:Tomography, X-Ray Computed