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MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique.

Medical image processing is the most challenging and emerging field nowadays. Magnetic Resonance Images (MRI) act as the source for the development of classification system. The extraction, identification and segmentation of infected region from Magnetic Resonance (MR) brain image is significant con...

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Published in:Journal of Medical Systems Vol. 42; no. 4; pp. 1 - 2
Main Authors: Kanmani, P., Marikkannu, P.
Format: research tables/charts Journal Article
Published: Springer Nature Apr2018
Online Access:View this record in EBSCOhost
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        01485598
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      jtl: Journal of Medical Systems
      issn: 01485598
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    pubinfo:
      dt: Apr2018
      vid: 42
      iid: 4
      pid: 237
      pub: Springer Nature
      place: New York, New York
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        128680938
        128680938
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        10.1007/s10916-018-0915-8
        128680938
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      tig:
        atl: MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique.
      aug:
        au:
          Kanmani, P.
          Marikkannu, P.
        affil: Sri Ramakrishna Institute of Technology, Coimbatore, India
      sug:
        subj:
          Brain Neoplasms Classification
          Magnetic Resonance Imaging
          Classification
          Data Analysis, Computer Assisted
          Brain Analysis
          Descriptive Statistics
          Validity
          Sensitivity and Specificity
          Data Analysis Software
          Brain Neoplasms Diagnosis
          Reliability
          Algorithms
          Maximum Likelihood
          Human
          Comparative Studies
          Precision
      ab: Medical image processing is the most challenging and emerging field nowadays. Magnetic Resonance Images (MRI) act as the source for the development of classification system. The extraction, identification and segmentation of infected region from Magnetic Resonance (MR) brain image is significant concern but a dreary and time-consuming task performed by radiologists or clinical experts, and the final classification accuracy depends on their experience only. To overcome these limitations, it is necessary to use computer-aided techniques. To improve the efficiency of classification accuracy and reduce the recognition complexity involves in the medical image segmentation process, we have proposed Threshold Based Region Optimization (TBRO) based brain tumor segmentation. The experimental results of proposed technique have been evaluated and validated for classification performance on magnetic resonance brain images, based on accuracy, sensitivity, and specificity. The experimental results achieved 96.57% accuracy, 94.6% specificity, and 97.76% sensitivity, shows the improvement in classifying normal and abnormal tissues among given images. Detection, extraction and classification of tumor from MRI scan images of the brain is done by using MATLAB software.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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