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A Novel FCM Algorithm Incorporating Spatial Information for Color Image Segmentation

Li Ling, Song Yingwei, Yin Zhongnan, Yang Xiuhua
2016 International Journal of Signal Processing, Image Processing and Pattern Recognition  
Fuzzy c-means clustering (FCM) with spatial information (FCM_S) is an effective algorithm for image segmentation.  ...  In this paper, we present a novel fuzzy c-means algorithm named nFCM_S that incorporates spatial information into the membership function and cluster center function for segmentation of color images.  ...  Conclusions In this paper, we have proposed a novel fuzzy c-means algorithm (nFCM_S) incorporating local spatial information and color information for color image segmentation.  ... 
doi:10.14257/ijsip.2016.9.8.28 fatcat:u4l5sjq73jhhpl5lj3q2rxvjmy

Color Image Segmentation Using Fuzzy C-Regression Model

Min Chen, Simone A. Ludwig
2017 Advances in Fuzzy Systems  
Most fuzzy clustering algorithms have originated from fuzzy c-means (FCM) and have been successfully applied in image segmentation.  ...  Therefore, a Fuzzy C-Regression Model (FCRM) using spatial information has been proposed whose prototype is hyperplaned and can be either linear or nonlinear allowing for better cluster partitioning.  ...  A modified fuzzy c-means clustering algorithm for MR brain image segmentation is introduced in [35] . The proposed algorithm extracts a scalar feature value from the neighborhood of each pixel.  ... 
doi:10.1155/2017/4582948 fatcat:ba5oasepg5h4jelydantxowtyy

A Review of Challenges in Clustering Techniques for Image Segmentation

Anju Bala
2020 International Journal for Research in Applied Science and Engineering Technology  
This paper deals with the cluster based image segmentation methods as it gives a new way of mathematical pattern to identify regions in an image.  ...  This study helps researcher to find out the major issues in clustering algorithms for image segmentation.  ...  Fuzzy c-means algorithm may also lead to under or over-segmentation.  ... 
doi:10.22214/ijraset.2020.32534 fatcat:y4bgxswtjfapxccqgh2sxue6vy

A MODIFIED SPATIAL FUZZY C-MEANS CLUSTERING ALGORITHM FOR DETECTING GLAUCOMAIN RETINAL FUNDUS IMAGES

Shoba
2014 Journal of Computer Science  
But it does not fully utilize the spatial information in the image. The Modified Spatial Fuzzy C-Means clustering with spatial rotation has been proposed to detect glaucoma in retinal fundus images.  ...  Fuzzy C Means (FCM) Clustering is used for clustering the data in which the data points are clustered with different membership degree.  ...  A new adaptive method has been proposed for the automatic segmentation of the optic disk in digital color fundus images.  ... 
doi:10.3844/jcssp.2014.1362.1372 fatcat:dmcul6j6ivg4jjgifutwkacrgm

A Cluster Number Adaptive Fuzzy c-means Algorithm for Image Segmentation

Shaoping Xu, Lingyan Hu, Xiaohui Yang, Xiaoping Liu
2013 International Journal of Signal Processing, Image Processing and Pattern Recognition  
It is well known that Fuzzy c-means (FCM) algorithm is one of the most popular methods for image segmentation.  ...  In this paper, we propose a novel cluster number adaptive fuzzy c-means image segmentation algorithm (CNAFCM) for automatically grouping the pixels of an image into different homogeneous regions when the  ...  To solve this problem, Li and Shen [4] proposed an algorithm called automatic modified fuzzy c-means cluster segmentation algorithm (AMFCM) that can automatically determine the optimal cluster number  ... 
doi:10.14257/ijsip.2013.6.5.17 fatcat:s2rxw6u7ibfj3jx35xnlttddey

Segmentation of color lip images by spatial fuzzy clustering

Alan Wee-Chung Liew, Shu Hung Leung, Wing Hong Lau
2003 IEEE transactions on fuzzy systems  
In this paper, we describe the application of a novel spatial fuzzy clustering algorithm to the lip segmentation problem.  ...  Index Terms-Color lip segmentation, local spatial interactions, spatial fuzzy clustering.  ...  A novel spatial fuzzy C-means (FCM) clustering algorithm [10] , [11] is employed. With appropriate pre-and postprocessing, good lip segmentation results can be obtained. II.  ... 
doi:10.1109/tfuzz.2003.814843 fatcat:swm46dypzfggpcv3fl6nuldbuy

Superpixel-based Fast Fuzzy C-Means Clustering for Color Image Segmentation

Tao Lei, Xiaohong Jia, Yanning Zhang, Shigang Liu, Hongying Meng, Asoke K. Nandi
2018 IEEE transactions on fuzzy systems  
A great number of improved fuzzy c-means (FCM) clustering algorithms have been widely used for grayscale and color image segmentation.  ...  In this work, we propose a superpixel-based fast FCM clustering algorithm (SFFCM) that is significantly faster and more robust than stateof-the-art clustering algorithms for color image segmentation.  ...  As adaptive neighborhood information is consistent with real image structuring information, the second group of algorithms obtains a better robustness for noisy images and a better segmentation effect  ... 
doi:10.1109/tfuzz.2018.2889018 fatcat:iehexp6hu5httlm5nv4ocj6vlq

Comparative Study of IAFCM & SSFCM Segmentation Techniques for Analysis of M-FISH Chromosome Images

Shruti N. Korane
2019 International Journal for Research in Applied Science and Engineering Technology  
In this paper IAFCM(Improved Adaptive Fuzzy C mean Clustering) & SSFCM(Spatial & Spectral Fuzzy C Mean Clustering ) algorithms are used for segmentation.  ...  Analysis of Chromosomes is an important and difficult task for clinical diagnosis and biological research. Conventional analysis of chromosomes using gray scale images is a complex and tough task.  ...  This paper presents a modified version of improved fuzzy c-means (FCM) algorithm that integrates both spatial information and spectral information into the membership function for clustering of images.  ... 
doi:10.22214/ijraset.2019.6162 fatcat:yzb4xpupkzabhdk4bravgvl2by

A Novel Coarse-to-fine Sea-land Segmentation Technique Based on Superpixel Fuzzy C-Means Clustering and Modified Chan-Vese Model

Eman Elkhateeb, Hassan Soliman, Ahmed Atwan, Mohammed Elmogy, Kyung-Sup Kwak, Nagham Mekky
2021 IEEE Access  
C. FUZZY C-MEANS CLUSTERING FCM is one of the effective fuzzy clustering algorithms [39] , [40] . The image segmentation problem can be considered a clustering problem.  ...  Elkhateeb et al.: Sea-land Segmentation Based on Superpixel Fuzzy C-Means and Modified Chan-Vese Model  ... 
doi:10.1109/access.2021.3065246 fatcat:qbrb2r3c6vf7bpwbeaastyxray

Image segmentation based on adaptive cluster prototype estimation

A.W.-C. Liew, Hong Yan, N.F. Law
2005 IEEE transactions on fuzzy systems  
An image segmentation algorithm based on adaptive fuzzy c-means (FCM) clustering is presented in this paper.  ...  By introducing a novel dissimilarity index in the modified FCM objective function, the new adaptive fuzzy clustering algorithm is capable of utilizing local contextual information to impose local spatial  ...  In comparison, the ASFCM algorithm produced a much better segmentation in Fig. 6 (c) due to adaptation of the prototypes. We also perform segmentation on some color images.  ... 
doi:10.1109/tfuzz.2004.841748 fatcat:b5hrryn545arvl23x7kvv7qxtu

Significantly Fast and Robust Fuzzy C-Means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering

Tao Lei, Xiaohong Jia, Yanning Zhang, Lifeng He, Hongying Meng, Asoke K. Nandi
2018 IEEE transactions on fuzzy systems  
As fuzzy c-means clustering (FCM) algorithm is sensitive to noise, local spatial information is often introduced to an objective function to improve the robustness of the FCM algorithm for image segmentation  ...  Index Terms-Fuzzy c-means clustering (FCM), image segmentation, local spatial information, morphological reconstruction (MR). 1063-6706  ...  However, FCM is able to segment color image with a shorter time, as local spatial information is neglected in FCM.  ... 
doi:10.1109/tfuzz.2018.2796074 fatcat:mfcye333urdxrpkrnam46ucjjm

Robust c-prototypes algorithms for color image segmentation

Dante Mújica-Vargas, Francisco J Gallegos-Funes, Alberto J Rosales-Silva, José de Jesús Rubio
2013 EURASIP Journal on Image and Video Processing  
In this paper, we present a modified clustering algorithm to segment color images.  ...  The algorithm performance is tested on real images with natural artifacts that make the segmentation process difficult.  ...  Acknowledgements The authors thank the Instituto Politécnico Nacional de México (National Polytechnic Institute of Mexico) and CONACYT for their financial support.  ... 
doi:10.1186/1687-5281-2013-63 fatcat:6t4u3y3azjgh5lmezwpickrh4e

Road Sign Segmentation and Recognition under Bad Illumination Condition using Modified Fuzzy C-means Clustering

Zinat Afrose, Md. Al-Amin Bhuiyan
2012 International Journal of Computer Applications  
In this paper, we present a novel approach on road sign segmentation under bad lighting condition employing a modified fuzzy c-means clustering.  ...  The proposed system implements a system that segments the road sign using fuzzy c-means clustering. At first, the image is detected in RGB colour space and then converted into HSV colour model.  ...  and the proposed Modified Fuzzy c-means algorithm.  ... 
doi:10.5120/7788-0888 fatcat:ci2wzlghzrgp7dlfnv6lllogsy

Detection of Exudates in Diabetic Retinopathy Images using Laplacian Kernel Induced Spatial FCM Clustering Algorithm

R. Ravindraiah, P. Rajendra Prasad
2016 Indian Journal of Science and Technology  
This paper presents laplacian kernel and it is induced into the kernel spatial FCM clustering algorithm for the segmentation of retinal fundus images.  ...  In general, FCM and KFCM algorithms very sensitive to noise and other imaging artefacts because it doesn't have spatial information.  ...  Rajput et al. 29 presented k-means clustering algorithm applied on LAB color space image.  ... 
doi:10.17485/ijst/2016/v9i15/88171 fatcat:pl5v4jusgjbhjmhobuwb4xnxpe

Modified Kernel Based Fuzzy Clustering for MR Brain Image Segmentation using Deep Learning

2019 International Journal of Engineering and Advanced Technology  
This article presents an efficient approach for segmenting MR brain images using a modified kernel based fuzzy clustering (MKFC) algorithm.  ...  Hence, it is still a challenging task to segment MR brain images due to possible noise presence, bias field and impact of partial volume.  ...  and modified spatial FCM algorithm [18, 19] .  ... 
doi:10.35940/ijeat.f8790.088619 fatcat:yefadnrdkjc6fodoc2wmhsrcsq
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