A Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

Shamsi, Hossein; Seyedarabi, Hadi

doi:10.7763/ijcte.2012.v4.573

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…Cluster center and objective function values remain changing after every iteration. The traditional FCM is sensitive to noise and Shamsi et al [26] proposed a modified FCM that incorporates spatial information along with traditional FCM and modifies membership weighting of each cluster, every point of data set has a weight in relation to every cluster.This weight permits to have a better classification in case of noise.Ji. Z et al [27] proposed another modified possiblistic C means of images that have been corrupted by intensity inhomogeneity and noise.…”

Section: Fuzzy Logic Based Methodsmentioning

confidence: 99%

Energy based Methods for Medical Image Segmentation

Juneja¹,

Kashyap²

2016

IJCA

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Health care applications have become boon for the healthcare industry. It needs correct segmentation connected with medical images regarding correct diagnosis. An efficient method assures good quality segmentation of medical images. Segmentation methods are classified as edge based, region based, clustering based, Level set methods (LSM) and Energy based methods. In this paper, a survey on all the effective methods those are capable for accurate segmentation is given, however quick process employing correct segments is still difficult. Some existing methods do correction and some badly pertain to deep irregularity in images. The wide range of the problems of computer vision may make good use of image segmentation. This paper studies and evaluate the different methods for segmentation techniques. This study is useful for determining the appropriate use of the image segmentation methods and for improving their accuracy and performance and also works on the main objective, which is designing new algorithms. The main goal is to make the image more simple and meaningful. After a brief description of each method an experimental comparison of some empirical (goodness and discrepancy) methods commonly used is then given to provide a rank of their evaluation abilities. This study is helpful for an appropriate use of existing segmentation methods and for improving their performance as well as for systematically designing new segmentation methods.

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Section: Fuzzy Logic Based Methodsmentioning

confidence: 99%

Energy based Methods for Medical Image Segmentation

Juneja¹,

Kashyap²

2016

IJCA

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“…This set partitioning strategy provides a mechanism to avoid the incidence of dead centers when guessing the initial cluster centroids. The dead center syndrome is a common problem often associated with clustering methods [30,[43][44][45]. In codicil, the set partitioning strategy provides an easy generalization of our algorithm to the case of number of clusters with lower computational costs.…”

Section: Methodsmentioning

confidence: 99%

“…The use of this approach obviously implies that the time complexity of PICA is proportionate to the size of the input image. PICA does not utilize spatial information as widely suggested [30], yet it achieves remarkable performance in segmentation result and computational time. Moreover, PICA satisfies survivability and recoverability principles because it can work for any number of clusters from 2 up to the maximum number of pixel intensities in the input image without breaking down.…”

Section: Introductionmentioning

confidence: 99%

Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

Olugbara

Adetiba

Oyewole

2015

Mathematical Problems in Engineering

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Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

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“…The problem of classification of an image to different homogeneous areas is considered as the function of the clustering of pixels in density space. Shamsi et al [12] used genetic fuzzy clustering with real coded variable series length and automatic evolution of clustering in his study. Xie and Beni index is codded in the chromosomes in cluster centers and used as a measure to represent the validity of the corresponding part.…”

Section: Literature Reviewmentioning

confidence: 99%

Vehicle Detection Using Fuzzy C-Means Clustering Algorithm

Saraçoğlu

Nemati²

2020

International Journal of Applied Mathematics Electronics and Computers

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Vehicle detection and identification are very important functions in the field of traffic control and management. Generally, a study should be conducted on big data sets and area characteristics to get closer to this function. The aim is to find the most appropriate model for these data. Also, the model that is prepared for the data aims to recognize the factors on the image. In other words, it aims to assign factors to the right classes and differentiate them. A classification of the image is made in that way. In this study, a vehicle identification system, in which Fuzzy C-Means Algorithm is used for image segmentation and the Support Vector Machine is used for image classification, is presented. The currentness of these methods is their most important property. The obtained results show that the selected methods are applied successfully and effectively.

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A Modified Fuzzy C-Means Clustering with Spatial Information for Image Segmentation

Cited by 17 publications

References 12 publications

Energy based Methods for Medical Image Segmentation

Energy based Methods for Medical Image Segmentation

Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

Vehicle Detection Using Fuzzy C-Means Clustering Algorithm

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