Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm.
The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended o...
| Publicado en: | Journal of Digital Imaging Vol. 31; no. 4; pp. 477 - 490 |
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| Autores principales: | , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=131471430&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 131471430 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2018 vid: 31 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 131471430 131471430 131471430 10.1007/s10278-018-0050-6 131471430 ppf: 477 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Comparative Approach of MRI-Based Brain Tumor Segmentation and Classification Using Genetic Algorithm. aug: au: Bahadure, Nilesh Bhaskarrao Ray, Arun Kumar Thethi, Har Pal affil: School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT) University, Bhubaneswar, Odissa, India sug: subj: Magnetic Resonance Imaging Brain Neoplasms Diagnosis Genetic Algorithms Signal Processing, Computer Assisted Brain Neoplasms Classification Descriptive Statistics Comparative Studies Validity Sensitivity and Specificity Automation ab: The detection of a brain tumor and its classification from modern imaging modalities is a primary concern, but a time-consuming and tedious work was performed by radiologists or clinical supervisors. The accuracy of detection and classification of tumor stages performed by radiologists is depended on their experience only, so the computer-aided technology is very important to aid with the diagnosis accuracy. In this study, to improve the performance of tumor detection, we investigated comparative approach of different segmentation techniques and selected the best one by comparing their segmentation score. Further, to improve the classification accuracy, the genetic algorithm is employed for the automatic classification of tumor stage. The decision of classification stage is supported by extracting relevant features and area calculation. The experimental results of proposed technique are evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on segmentation score, accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 92.03% accuracy, 91.42% specificity, 92.36% sensitivity, and an average segmentation score between 0.82 and 0.93 demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 93.79% dice similarity index coefficient, which indicates better overlap between the automated extracted tumor regions with manually extracted tumor region by radiologists. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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