Detection of Brain Tumor by Particle Swarm Optimization using Image Segmentation

Mahalakshmi, S.; Velmurugan, T.

doi:10.17485/ijst/2015/v8i22/79092

Cited by 37 publications

(13 citation statements)

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“…Where as in PSNR, which defines the quality of the image, is more in Adaptive K-means as compared to K-means clustering algorithm that shows in the Figure6 graph [10,11]. Also in case of time period Adaptive K-means takes very less time as compared to K-means that will define from the figure 7.…”

Section: Graphical Representation Of Parametersmentioning

confidence: 94%

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Sahu¹,

Parvathi²,

Krishna³

2017

IJECE

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<p>Image segmentation takes a major role to analyzing the area of interest in image processing. Many researchers have used different types of techniques to analyzing the image. One of the widely used techniques is K-means clustering. In this paper we use two algorithms K-means and the advance of K-means is called as adaptive K-means clustering. Both the algorithms are using in different types of image and got a successful result. By comparing the Time period, PSNR and RMSE value from the result of both algorithms we prove that the Adaptive K-means clustering algorithm gives a best result as compard to K-means clustering in image segmentation. </p>

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Section: Graphical Representation Of Parametersmentioning

confidence: 94%

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Sahu¹,

Parvathi²,

Krishna³

2017

IJECE

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“…It achieved 95% matching rate. Mahalakshmi and Velmurugan [14] segmented mammogram image using particle swarm optimization technique with multilevel threshold. The output segmented at level three achieved better results and execution time is estimated.…”

Section: Mp Sukassini Tvelmuruganmentioning

confidence: 99%

Ascertaining Abnormal Regions in Mammogram Images using Gravitational Search Local Map View Technique

Sukassini¹,

Velmurugan²

2019

IJITEE

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Segmentation of mammogram images has gained importance in many medical treatments and diagnostic processes. Mammogram image segmentation aims at correctly separating different tissues, organs, or pathologies in volumetric image data. Most of the existing algorithms for image segmentation have a "scattered" cluster problem (disconnected clusters) happened in many clustering techniques (agglomerative, k-means, Dbscan) . Above algorithms not taken into account of both quality value and connectivity of points and region varying shapes. Two methods are proposed in this paper. The first technique is LMV (Local Map View) and the second technique is GSLMV (Gravitational Search Local Map View). LMV concentrates on determination of local quality for each point in all instances of the region in the comparative similarity view by applying the initial cluster technique. This view allows the user to choose instances for detailed analysis and filter the outlier instances from the input, next specific feature selection process identifies regions with systematic characteristics across the images. In this research work, pixels in groups with high intensity are assumed to be abnormal regions in cancer and non-cancer images. Fuzzy clustering is used to cluster the pixels. The optimal threshold from GS are initialized as cluster centres. This increases the speed of GSLMV algorithm. Performances of GSA, LMV and GSLMV methods are measured using False Rejection Rate, pixel count, Peak signal to noise ratio and runtime metrics. GSLMV showed better results based on pixel count, PSNR and runtime.

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“…The authors found that separate algorithms were suitable for differentiated regions of the tumor and no particular algorithm was found best for all the subregions. Mahalakshmi & Velmurugan [11] developed an algorithm to detect brain tumor using particle swarm optimization. The algorithm comprises of four stages the conversion, implementation, selection, and extraction.…”

Section: Related Workmentioning

confidence: 99%

Efficient MRI Segmentation and Detection of Brain Tumor using Convolutional Neural Network

Jijja¹,

Dinesh²

2019

IJACSA

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Brain tumor is one of the most life-threatening diseases at its advance stages. Hence, detection at early stages is very crucial in treatment for improvement of the life expectancy of the patients. magnetic resonance imaging (MRI) is being used extensively nowadays for detection of brain tumors that requires segmenting huge volumes of 3D MRI images which is very challenging if done manually. Thus, automatic segmentation of the images will significantly lessen the burden and also improve the process of diagnosing the tumors. This paper presents an efficient method based on convolutional neural networks (CNN) for the automatic segmentation and detection of a brain tumor using MRI images. Water cycle algorithm is applied to CNN to obtain an optimal solution. The developed technique has an accuracy of 98.5%.

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Detection of Brain Tumor by Particle Swarm Optimization using Image Segmentation

Cited by 37 publications

References 0 publications

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Parametric Comparison of K-means and Adaptive K-means Clustering Performance on Different Images

Ascertaining Abnormal Regions in Mammogram Images using Gravitational Search Local Map View Technique

Efficient MRI Segmentation and Detection of Brain Tumor using Convolutional Neural Network

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