Automated Brain Tumor Segmentation and Detection in MRI Using Enhanced Darwinian Particle Swarm Optimization(EDPSO)

Vijay, Vasupradha; Kavitha, A.; Rebecca, S. Roselene

doi:10.1016/j.procs.2016.07.370

Cited by 57 publications

(19 citation statements)

References 3 publications

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“…Proposed EDPSO and PSO algorithm were comparedregarding accuracy and execution time. The results indicatedthat the EDPSO algorithm has better-quality rate for all the input images [7].…”

Section: G V Vijay a R Kavith And Sr Rebecca (2016)mentioning

confidence: 96%

Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

Kaur¹

2017

IJRASET

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A group of defective cells that grow inside or around the brain referred as Barin Tumor. The number of Brain Tumor cases around the World is increasing day by day. So, it is significant to detect it at anearly stage. Segmentation of brain images holds thesignificant part for detection of Tumor brain. Manual segmentation of brain Tumor tissues cannot be compared with existing high-speed computing machines. Therefore, theemphasisis given on computer aided detection of brain Tumor. This paper provides an overview of various techniques that have been implemented till now to detect the brain tumor. Along with this, these techniques are compared for accuracy, sensitivity, and specificity to detect the Brain tumor. The research findings will help to explore the research in the same field.

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“…Proposed EDPSO and PSO algorithm were comparedregarding accuracy and execution time. The results indicatedthat the EDPSO algorithm has better-quality rate for all the input images [7].…”

Section: G V Vijay a R Kavith And Sr Rebecca (2016)mentioning

confidence: 96%

Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

Kaur¹

2017

IJRASET

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“…Vijay et al 11 developed enhanced Darwinian particle optimization for scheduled tumor division that overcomes the existing particle swarm optimization (PSO) vulnerability. The system has four stages.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain MR image tumor detection and classification using neuro fuzzy with binary cuckoo search technique

Arumugam

Paulraj

Selvaraj³

2021

Int J Imaging Syst Tech

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Brain tumor and stroke are two important causes of death in and around the world. Tumor classification and retrieval system plays a vital role in medical field. Tumor detection, segmentation and MR imaging seizures are a major concern, although it can be a daunting and tedious task for clinical specialists, the accuracy of which depends solely on their experience. In this article, the neuro fuzzy with binary cuckoo search optimization method is proposed for detecting tumors on MR images. The method has four stages. In the first step, raw MR images are pre-processed by the anisotropic filter, and in the second phase, the removal of the skull is classified by type. The third phase involves the functioning of singular value decomposition and principle component analysis. Finally, the NFBCS method is used to detect and classify tumors and the BCS algorithm optimizes the study model for better classification accuracy.

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“…Image segmentation is one of the most challenging tasks in medical imaging and more metaheuristics-based methods have been proposed in recent years: active contour combined to multipopulation CSA in Ilunga-Mbuyamba et al 4 ; a hybrid PSO based on a learning strategy in which the moving strategy is completed by new jumping and random cross-operators 5 ; a dynamic PSO combined with k-means clustering with better results than other clusteringbased segmentation procedures 6 ; partitioned and cooperative quantum-behaved PSO algorithm in Li et al 7 ; and CSA and egg lying radius CSA are used in Pare et al 8 for color images segmentation. More specifically, in Vijay et al, 9 the enhanced Darwinian PSO is applied with good results for automated tumor segmentation, and in Anter and Hassenian, 10 the abdominal computed tomography (CT) liver tumor segmentation based on neutrosophic sets uses PSO and fast fuzzy C-mean algorithm is proposed. Another medical imaging task is image enhancement which was addressed in Daniel and Anitha 11 -an optimum waveletbased masking using CSA is proposed for the contrast improvement of medical images.…”

Section: A Comparison Of Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

Bejinariu

Costin

2018

Methods Inf Med

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Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases Background In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process. Objectives A set of three nature-inspired algorithms, including Cuckoo Search algorithm (CSA), Particle Swarm Optimization (PSO), and Multi-Swarm Optimization (MSO), are compared in terms of strategies used in the evolutionary process and also of the results obtained in case of particular optimization problems. Methods The three algorithms were applied for biomedical image registration (IR) and compared in terms of performances. The expected geometric transform has seven parameters and is composed of rotation against a point in the image, scaling on both axis with different factors, and translation. Results The evaluation consisted of 25 runs of each IR procedure and revealed that (1) PSO offers the most precise solutions; (2) CSA and MSO are more stable in the sense that their solutions are less scattered; and (3) MSO and PSO have a higher convergence speed. Conclusions The evaluation of PSO, MSO, and CSA was made for multimodal IR problems. It is possible that for other optimization problems and also for other settings of the optimization algorithms, the results can be different. Therefore, the nature-inspired algorithms demonstrated their efficacy for this class of optimization problems.

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Automated Brain Tumor Segmentation and Detection in MRI Using Enhanced Darwinian Particle Swarm Optimization(EDPSO)

Cited by 57 publications

References 3 publications

Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

Comparative Review of Image Denoising and Segmentation Approaches For Detection of Tumor in Brain Images

Brain MR image tumor detection and classification using neuro fuzzy with binary cuckoo search technique

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

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