articleInternational Journal of Biomedical ImagingJan 1, 2017GOLD OA

Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM

KIIT University · Lovely Professional University

PubMed
Indexed incrossrefdoajpubmed

Abstract

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each…

Citation impact

608
total citations
FWCI
22.79
Percentile
100%
References
28
Citations per year

Authors

3

Topics & keywords

Keywords
  • Support vector machine
  • Computer science
  • Artificial intelligence
  • Sørensen–Dice coefficient
  • Pattern recognition (psychology)
  • Segmentation
  • Dice
  • Feature extraction
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