Abstract
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.
Highlights
Image segmentation is considered an indispensable component in image processing, comprehension, and computer vision [1,2,3]
Different from FCM_S1, FCM_S2, and FRFCM algorithm, MRIFCM_GF obtains the best average SA (ASA) on almost all ST images. is is because the introduced morphological reconstruction (MR) is effective in removing any type of noise, and the guided filter whose guidance image is adjusted by the influence factor can improve the segmentation on edges
We first present the IFCM_GF method, in which a novel impact factor ρ is designed to adapt the guidance image. e essence of this adjustment on guidance image is proved to be equal to the change on parameter ε of guided filter
Summary
Image segmentation is considered an indispensable component in image processing, comprehension, and computer vision [1,2,3]. A guided filter-based FCM algorithm with an impact factor named IFCM_GF is presented, in which a novel positive impact factor ρ is defined to adapt the guidance image. The FCM is conducted on the reconstructed image obtained by MR, and the guided filter is implemented on the membership metrics with the guidance of the raw noise image adjusted by the impact factor ρ. The MRIFCM_GR method is further developed by incorporating MR technique into IFCM_GF for better segmentation performance on images with heavy noise and easier selection of proper values of factor ρ.
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