Morphological Reconstruction-Based Image-Guided Fuzzy Clustering with a Novel Impact Factor

• Published in: Journal of healthcare engineering Vol. 2021; pp. 6747371 - 13
• Main Authors: Qin, Qingxue, Xu, Guangmei, Zhou, Jin, Wang, Rongrong, Jiang, Hui, Wang, Lin, Han, Shiyuan, Du, Tao, Ji, Ke, Zhao, Ya-ou, Zhang, Kun
• Format: Journal Article
• Language: English
• Published: England Hindawi 2021
• Subjects: Algorithms; Cluster Analysis; Fuzzy Logic; Humans; Journal Impact Factor; Magnetic Resonance Imaging - methods
• ISSN: 2040-2295; 2040-2309
• DOI: 10.1155/2021/6747371

The guided filter is a novel explicit image filtering method, which implements a smoothing filter on “flat patch” regions and ensures edge preserving on “high variance” regions. Recently, the guided filter has been successfully incorporated into the process of fuzzy c-means (FCM) to boost the clustering results of noisy images. However, the adaptability of the existing guided filter-based FCM methods to different images is deteriorated, as the factor ε of the guided filter is fixed to a scalar. To solve this issue, this paper proposes a new guided filter-based FCM method (IFCM_GF), in which the guidance image of the guided filter is adjusted by a newly defined influence factor ρ. By dynamically changing the impact factor ρ, the IFCM_GF acquires excellent segmentation results on various noisy images. Furthermore, to promote the segmentation accuracy of images with heavy noise and simplify the selection of the influence factor ρ, we further propose a morphological reconstruction-based improved FCM clustering algorithm with guided filter (MRIFCM_GF). In this approach, the original noisy image is reconstructed by the morphological reconstruction (MR) before clustering, and the IFCM_GF is performed on the reconstructed image by utilizing the adjusted guidance image. Due to the efficiency of the MR to remove noise, the MRIFCM_GF achieves better segmentation results than the IFCM_GF on images with heavy noise and the selection of the influence factor for the MRIFCM_GF is simple. Experiments demonstrate the effectiveness of the presented methods.