Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algorithms

Yang, Miin-Shen; Hu, Yang; Lin, Karen Chia-Ren; Lin, Charles Chia-Lee

doi:10.1016/s0730-725x(02)00477-0

Cited by 146 publications

(55 citation statements)

References 9 publications

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“…The median filter [10] is generally exploited to lessen image noise. This filter slightly works similar to the mean filter but this replaces only the median value while mean filter substitutes only mean value in the neighborhood of a pixel.…”

Section: A Preprocessing Median Filtermentioning

confidence: 99%

Enhancement of Magnetic Resonance Images Using Soft Computing Based Segmentation

Javed¹,

Wang²,

Alenezi³

et al. 2014

IJMLC

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Abstract-Segmentation is the process of extracting points, lines or regions, which are then used as inputs for complementary tasks such as registration, measurement, movement analysis, visualization , etc in MRI. The noise in MR images degrades the image quality and also affect on the segmentation process which can lead to wrong diagnosis. The main aim of this study is to suggest a system to enhance the quality of the human brain MRI. In the proposed system, median filter is used for image enhancement of brain MRI and fuzzy c-means for segmentation purpose. The proposed method is completely automatic that is there is no user involvement in the proposed system. The system is tested on different kinds of brain MR images and proved robust against noise as well as segments the images fast with improvements.Index Terms-Dunn's index, fuzzy c-means (FCM), image segmentation, median filter.

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Section: A Preprocessing Median Filtermentioning

confidence: 99%

Enhancement of Magnetic Resonance Images Using Soft Computing Based Segmentation

Javed¹,

Wang²,

Alenezi³

et al. 2014

IJMLC

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“…Let us assume P=(p1, p2,.,pN) denotes an image with N pixels to be partitioned into c clusters, where p i represents multispectral data. The algorithm [8]discussed is an iterative escalation that minimizes the cost function defined as follows:…”

Section: Fuzzy C-means Clusteringmentioning

confidence: 99%

Integrating Variational Level Set Method and Fuzzy c-Means to Automatically Segment the MRI Brain Images

Singh¹,

Bhadauria²,

A.S.Bhadauria³

2014

IJCA

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The main aim of this paper is to provide a method that could easily delineate the exact location of tumor region from brain MRI images by integrating the fuzzy c-means and level set method. The proposed method smoothly exploits the spatial function during FCM clustering which in turn proves the automaticity of the method by dividing the original image into clusters and then using one cluster for automatic initialization. Since automating the process reduces the time utilization of processing thereby making the work less tedious, hence if considered it could be competent tool in future. Another problem associated when utilizing manual methods is that it may lead to different results when produced by different medical experts which can be completely erased while using this method. Secondly, the proposed method uses the level set method to find the contour of tumor region in the original image which helps in situations where image changes their topologies by merging or splitting in two. Thus, the proposed method is using the convenient variational level set method in place of traditional level set method thereby eliminating one more drawback of re-initializing the contour several times during image segmentation.

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“…Every particle keeps track of its coordinates in hyperspace, which are linked with the solution (fitness) it has accomplished so far. There are some disadvantages in image segmentation using PSO (Yang et al, 2002). Some pixels used to miss during image segmentation using conventional PSO.…”

Section: Introductionmentioning

confidence: 99%

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

2017

IJAERD

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Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algorithms

Cited by 146 publications

References 9 publications

Enhancement of Magnetic Resonance Images Using Soft Computing Based Segmentation

Enhancement of Magnetic Resonance Images Using Soft Computing Based Segmentation

Integrating Variational Level Set Method and Fuzzy c-Means to Automatically Segment the MRI Brain Images

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

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