Abstract
Fuzzy c-means clustering (FCM) has proved highly successful in the manipulation and analysis of image information, such as image segmentation. However, the effectiveness of FCM-based technique is limited by its poor robustness to noise and edge-preserving during the segmentation process. To tackle these problems, a new objective function of FCM is developed in this work. The main innovation work and results of this paper are outlined as follows. First, a regularization operation performed by total generalized variation (TGV) is used to guarantee noise smoothing and detail preserving. Second, a weight factor incorporated into the spatial information term is designed to form nonuniform membership functions, which can contribute to the assignment of each pixel for the highest membership value. In addition, a regularization parameter is used to balance the respective importance of penalty between whole image and each neighborhood. The main advantage of this technique over conventional FCM-based methods is that it can reconstruct image patterns in heavy noise with only a small loss. We perform experiments on both synthetic and real images. Compared to state-of-the art FCM-based methods, the proposed algorithm exhibits a very good ability to noise and edge-preserving in image segmentation.
Highlights
Image segmentation focuses on the process of partitioning an image into meaningful regions, which has emerged as a fundamental topic for diverse applications in image understanding and computer vision [1]–[3]
To address the challenges in a more precise way, one of our intuitions is that if a model can effectively suppress noise and artifacts and produce visually pleasing edge profiles, we can get more accurate segmentation when treating image. Another object of this paper is to investigate the unimodal property of membership functions and its application to optimal assignment of each pixel. To accomplish these two purposes, this paper proposed a robust fuzzy clustering with total generalized variation (TGV) [28]–[30] and weight factor based on spatial information to achieve a high segmentation precision
In order to set up the general form of comparative experiments, the TGVFCMS as well as six stateof-the-art Fuzzy c-means (FCM)-based methods robust FCM (RFCM) [12], KGFCMS [13], KWFLICM [11], adaptively regularized kernel FCM (ARKFCM) [14], Selective-LSM [15] and fast and robust FCM (FRFCM) [16], are applied to the test data mentioned above
Summary
Image segmentation focuses on the process of partitioning an image into meaningful regions, which has emerged as a fundamental topic for diverse applications in image understanding and computer vision [1]–[3]. During the past several decades, a variety of clustering techniques have been developed for their applications to image segmentation [4]. In these clustering algorithms, Fuzzy c-means (FCM) approach has been attracting significant attentions in this emerging area and has become increasingly important to elucidate image structures because they may identify the belongingness of. Good performances have been achieved in the development of FCM segmentation algorithms, the segmentation for noise images and imaging artifacts remains a largely unsolved problem [4]. Many improved versions have been developed and formalized in many different ways
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