Segmentation of brain MRI using active contour model

Rabeh, Amira Ben; Benzarti, Faouzi; Amiri, Hamid

doi:10.1002/ima.22205

Cited by 34 publications

(5 citation statements)

References 21 publications

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“…The contribution is to combine the geometric method with the statistical method of the AC. They find that our method Modify Level set to give a good result [21].…”

Section: Active Contour With Convolutional Neural Networkmentioning

confidence: 92%

Medical Image Segmentation with active contour and optimization Techniques: Survey

Hussein,

Mosa

2022

J. Al-Qadisiyah Comp. Sci. Math.

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The principal aim is to improve medical diagnosis through segmented images. Thus, medical image segmentation become one of the key technologies in computer-aided diagnosis. Active contours, or snakes, have been widely used for image segmentation purposes. However, high noise sensitivity and poor performance over weak edges are the most acute issues that hinder the segmentation accuracy of these curves, particularly in medical images. To overcome these issues, a novel external force that integrates gradient vector flow (GVF) field forces and the traditional snake function is proposed in this research. In addition, a novel technique is applied to limit the boundary of the initial contour by set four initial points around the medical issue and then connecting them by polynomial curves. Moreover, the positive effect of Particle Swarm Optimization (PSO) on calculating the final active contour area and its percentage to the entire image area is proved in this work.

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“…The contribution is to combine the geometric method with the statistical method of the AC. They find that our method Modify Level set to give a good result [21].…”

Section: Active Contour With Convolutional Neural Networkmentioning

confidence: 92%

Medical Image Segmentation with active contour and optimization Techniques: Survey

Hussein,

Mosa

2022

J. Al-Qadisiyah Comp. Sci. Math.

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“…Brain MRI is specifically employed to unfold the sensitive information related to muscles, ligaments, tendons, nerve damage, bleeding, blood clot, etc., with inbuilt power of distinguishing soft tissues [2]. MRI segmentation approaches play a vital role in numerous applications in neurology that involve precise estimation of tumor size, tumor location, tumor volume, lesions, blood cells demarcation, therapy, and surgical planning [1,3].…”

Section: Introductionmentioning

confidence: 99%

Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm

Kapoor¹,

Agarwal²

2021

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Medical image segmentation is usually integrated as a critical step in medical image analysis, often associated with numerous clinical applications. Magnetic Resonance Imaging (MRI) provides detailed visualization of the various anatomical structures decisive for interventions and surgical plans. OBJECTIVES: The objective of this paper is to design and apply an enhanced brain tumor MRI segmentation using Kmean with K-means as machine learning based Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). METHODS: A novel fitness function of Swarm Based PSO works on velocity variation is introduced, which enhances the segmented regions. The traditional k-means algorithm is enhanced by applying PSO to the segmented part. Another extension of Swarm Intelligence named Firefly is applied to compare the results of the PSO based segmentation, and Firefly based segmentation is used. RESULTS: The simulation results are evaluated in terms of precision (98%), recall (0.95), f-measure (0.96), accuracy (97%), and segmentation time (2.63s) to measure the image segmentation the quality of main results obtained. CONCLUSION: Comparative studies have shown that the proposed design using k-means combined with FA exhibited high accuracy and precision in detecting brain tumor RoI.

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“…Furthermore, segmentation occupies a critical function for many applications in analysis, extraction of features, image understanding, and interpretation. It also has extensive medical science uses such as atlas matching, blood-cell delineation, tumor volume estimation, image matching registration, surgical preparation, tissue classification, and tumor localization [1]. Brain MRI segmentation impacts all the analysis results; therefore, this is a necessary exercise in several clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Automatic MRI Brain Tumor Segmentation Techniques: A Survey

Basir

Shantta

2021

IRA-JAS

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Image segmentation plays a crucial role in recognizing image signification for checking and mining medical image records. Brain tumor segmentation is a complicated assignment in medical image analysis. It is challenging to identify precisely and extract that a portion of the image has abnormal tissues for further diagnosis and analysis. The method of segmenting a tumor from a brain MRI image is a highly concentrated medical science community field, as MRI is non-invasive. In this survey, brain MRI images' latest brain tumor segmentation techniques are addressed a thoroughgoing literature review. Besides, surveys the several approved techniques regularly applied for brain tumor MRI segmentation. Also, highlighting variances among them and reviews their abilities, pros, and weaknesses. Various approaches to image segmentation are described and explicated with the modern participation of several investigators.

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Segmentation of brain MRI using active contour model

Cited by 34 publications

References 21 publications

Medical Image Segmentation with active contour and optimization Techniques: Survey

Medical Image Segmentation with active contour and optimization Techniques: Survey

Enhanced Brain Tumour MRI Segmentation using K-means with machine learning based PSO and Firefly Algorithm

Automatic MRI Brain Tumor Segmentation Techniques: A Survey

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