State-of-the-art fuzzy active contour models for image segmentation
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METHODOLOGIES AND APPLICATION
State-of-the-art fuzzy active contour models for image segmentation Ajoy Mondal1 · Kuntal Ghosh2
© Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Image segmentation is the initial step for every image analysis task. A large variety of segmentation algorithm has been proposed in the literature during several decades with some mixed success. Among them, the fuzzy energy-based active contour models get attention to the researchers during last decade which results in development of various methods. A good segmentation algorithm should perform well in a large number of images containing noise, blur, low contrast, region inhomogeneity, etc. However, the performances of the most of the existing fuzzy energy-based active contour models have been evaluated typically on the limited number of images. In this article, our aim is to review the existing fuzzy active contour models from the theoretical point of view and also evaluate them experimentally on a large set of images under the various conditions. The analysis under a large variety of images provides objective insight into the strengths and weaknesses of various fuzzy active contour models. Finally, we discuss several issues and future research direction on this particular topic. Keywords Segmentation · Active contour · Fuzzy energy · Blur · Intensity in-homogeneity · Noise and low contrast
1 Introduction Image segmentation is a fundamental task in image analysis, computer vision, medical image processing, etc (Gonzalez and Woods 2008; Garcia-Lamont et al. 2018). Segmentation is a process of partitioning an image into various regions which are homogeneous with respect to their features (e.g., intensity, color, texture, etc) (Gonzalez and Woods 2008; Zaitoun and Aqel 2015). Various image segmentation algorithms have been developed during several decades (Fu and Mui 1981; Sahoo et al. 1988; Pal and Pal 1993; Khan 2014; Zaitoun and Aqel 2015). Among them, clustering and active contour models (acms) are most commonly used for image segmentation. Fuzzy logic has been used to solve various problems: decision making (Amin et al. 2019; Fahmi et al. 2019), pattern recognition (Melin 2018; Mitchell 2005), image segmentation (Naz et al. 2010; Zhang et al. 2017), Communicated by V. Loia.
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Ajoy Mondal [email protected] Kuntal Ghosh [email protected]
1
CVIT, International Institute of Information Technology, Hyderabad, India
2
MIU, Indian Statistical Institute, Kolkata, India
etc. Fuzzy clustering has been successfully considered from the early stage of the image segmentation task up to now (Dunn 1973; Bezdek 1981). It can retain more information from the original image than crisp clustering by introducing the degree of belongingness of each image pixel to the clusters (Bezdek 1981). Fuzzy clustering using global image information is not robust for images which are corrupted by various types of noise (Dunn 1973; Bezdek 1981). A large number of modified fuzzy clustering techniques have been proposed by incorporatin
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