Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and Active Contour Method.
The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain...
| Published in: | Journal of Digital Imaging Vol. 34; no. 5; pp. 1209 - 1225 |
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| Main Authors: | , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
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Springer Nature
Oct2021
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153241283&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153241283 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Oct2021 vid: 34 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153241283 152600877 153241283 153241283 10.1007/s10278-021-00514-6 153241283 ppf: 1209 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Performance Improvement in Brain Tumor Detection in MRI Images Using a Combination of Evolutionary Algorithms and Active Contour Method. aug: au: Saeidifar, Mahtab Yazdi, Mehran Zolghadrasli, Alireza affil: School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran sug: subj: Brain Neoplasms Diagnosis Magnetic Resonance Imaging Algorithms Utilization Early Detection of Cancer Standards Image Processing, Computer Assisted Standards Quality Improvement Automation Human Image Enhancement Brain Anatomy and Histology Skull Brain Radiography ab: The process of treating brain cancer depends on the experience and knowledge of the physician, which may be associated with eye errors or may vary from person to person. For this reason, it is important to utilize an automatic tumor detection algorithm to assist radiologists and physicians for brain tumor diagnosis. The aim of the present study is to automatically detect the location of the tumor in a brain MRI image with high accuracy. For this end, in the proposed algorithm, first, the skull is separated from the brain using morphological operators. The image is then segmented by six evolutionary algorithms, i.e., Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Genetic Algorithm (GA), Differential Evolution (DE), Harmony Search (HS), and Gray Wolf Optimization (GWO), as well as two other frequently-used techniques in the literature, i.e., K-means and Otsu thresholding algorithms. Afterwards, the tumor area is isolated from the brain using the four features extracted from the main tumor. Evaluation of the segmented area revealed that the PSO has the best performance compared with the other approaches. The segmented results of the PSO are then used as the initial curve for the Active contour to precisely specify the tumor boundaries. The proposed algorithm is applied on fifty images with two different types of tumors. Experimental results on T1-weighted brain MRI images show a better performance of the proposed algorithm compared to other evolutionary algorithms, K-means, and Otsu thresholding methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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