cellular neural network based medical image segmentation using artificial bee colony algorithm

Duraisamy, Manimegalai; Jane, F. Mary Magdalene

doi:10.1109/icgccee.2014.6922413

Cited by 13 publications

(4 citation statements)

References 27 publications

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“…|∇ | ℎ , 0 ( , ) is initial contour. And for segmentation and ending the process and obtain the optimal solution, Z should be regularized by applying equation (9) and if it's value around zero or means it's on the boundary you follow to apply the equation (10).…”

Section: Figure 2 Level Set Functionmentioning

confidence: 99%

“…In medical image processing, image segmentation is a way to determine the region of tumors and sometimes tends to compute the volume of tumors [8,9]. According to the National Cancer Institute Statistics (NCIS) The National Brain Tumor Foundation (NBTF) for research in United States estimates that brain cancer percentage is increasing every year in U.S and UK and reported that it is the cause of increasing the percentage of death and also in India a huge number of people are affected by different types of tumors [10].…”

Section: Introductionmentioning

confidence: 99%

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K-means algorithm with level set for brain tumor segmentation

Abdelaziz

2019

IJEECS

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<p>Brain is a complicated structure consisting of millions of millions cells so that, it’s difficult to identify any diseases without using any computerized technology. Magnetic resonance imaging (mri) is one of the main assessments of brain tumors. One of the most important steps on medical image processing is segmentation. Segmenting brain mri images, which provide accurate information for the diagnosis and therapy decisions of brain tumors. We proposed to segment brain tumor mri images into three parts (wm (white matter), gm (gray matter), and background). The first algorithm is for applying median filtering on brain mri image for removing the noise from the image for achieving accurate results. The second algorithm is for applying k-means algorithm for accuracy in time consuming and for clustering into regions and the third algorithm indicate the detecting the boundary of the image with the use of level set. By comparison, our proposed method, its efficiency to segment perfectly more than other previous used algorithms especially on time consuming.</p>

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Section: Figure 2 Level Set Functionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

K-means algorithm with level set for brain tumor segmentation

Abdelaziz

2019

IJEECS

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“…(ii) The second order of dependence occurs when a pixels has the same difference ratio as their vicinity Frank and Shouxian Cheng applied the automatic seed selection method, they selected seed which can represents needed segmentation region based on certain similarity criteria and proposed a strategy to solve the above two pixels dependence [1]. The watershed algorithm is more representative in the application of mathematical morphology theory for image segmentation.…”

Section: Seed Region Growing Algorithm and Watershed Algorithmmentioning

confidence: 99%

Medical Image Segmentation using Penalized FCM and Pollination based Optimization Approach

Kaur¹,

Bhatia²

2015

IJCA

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Image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super pixels). The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. In this paper we are going to discuss various approaches in image segmentation and various segmentation algorithms in image processing and analysis.

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“…In image processing, segmentation is clustering the pixels of the objects on the image. In medical image processing, segmentation acts as the key process in the determination of the location and size of the tumours on the image [2]. For segmentation, region based, cluster-based and histogrambased segmentation algorithms are mainly used.…”

Section: Introductionmentioning

confidence: 99%

Brain MRI Segmentation based on Different Clustering Algorithms

Kucukkulahli¹,

Erdoğmuş²,

Polat³

2016

IJCA

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In this study, MR Image segmentation has been realized with some clustering algorithms. In the study, the performances kmeans, lloyds, llyds-kmeans, pso clustering, ga clustering and jaya optimisation algorithms on some MR images from BRATS 2012 dataset have been compared. For the comparison, the manual segmentation results of MR images from BRATS 2012 dataset have been referenced and results have been compared with these referances. In the comparison RI (Rand Index), VOI (Variation of Information) and GCE (Global Consistency Error) have been used and results have been submitted. The results showed that the PSO algorithm yielded better results and has a better processing time than the other algorithms.

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cellular neural network based medical image segmentation using artificial bee colony algorithm

Cited by 13 publications

References 27 publications

K-means algorithm with level set for brain tumor segmentation

K-means algorithm with level set for brain tumor segmentation

Medical Image Segmentation using Penalized FCM and Pollination based Optimization Approach

Brain MRI Segmentation based on Different Clustering Algorithms

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