Efficacy of Gabor-Wavelet versus statistical features for brain tumor classification in MRI: A comparative study

Nooshin Nabizadeh, Miroslav Kubat, Nigel John, Clinton B Wright

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Automatic tumor segmentation can only be as successful as the feature extraction techniques it relies on. While many such techniques have been employed, it is still not quite clear which of feature extraction methods should be preferred. To help improve the situation, we present here the results of a study in which we compare the efficiency of using Gaborwavelet features and statistical features, which are two main groups of competent and successful texture-based features in tumor segmentation. To be more specific, we experiment with three different segmentation techniques that employ Support Vector Machines (SVM), K-Nearest Neighbor classifiers (KNN), and the K-Means classifiers. The system that serves as our testbed includes tumor slice detection, feature extraction, feature selection, and finally feature classification and comparison. The method implementation and the results are discussed.

Original languageEnglish (US)
Title of host publicationProceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013
EditorsHamid R. Arabnia, Leonidas Deligiannidis, Joan Lu, Fernando G. Tinetti, Jane You, George Jandieri, Gerald Schaefer, Ashu M. G. Solo, Vladimir Volkov
PublisherCSREA Press
Pages242-247
Number of pages6
ISBN (Electronic)1601322526, 9781601322524
StatePublished - Jan 1 2013
Event2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013, at WORLDCOMP 2013 - Las Vegas, United States
Duration: Jul 22 2013Jul 25 2013

Publication series

NameProceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013
Volume1

Conference

Conference2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013, at WORLDCOMP 2013
CountryUnited States
CityLas Vegas
Period7/22/137/25/13

Keywords

  • Gabor-wavelet
  • MR imaging
  • Statistical feature
  • Tumor segmentation

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Efficacy of Gabor-Wavelet versus statistical features for brain tumor classification in MRI: A comparative study'. Together they form a unique fingerprint.

  • Cite this

    Nabizadeh, N., Kubat, M., John, N., & Wright, C. B. (2013). Efficacy of Gabor-Wavelet versus statistical features for brain tumor classification in MRI: A comparative study. In H. R. Arabnia, L. Deligiannidis, J. Lu, F. G. Tinetti, J. You, G. Jandieri, G. Schaefer, A. M. G. Solo, & V. Volkov (Eds.), Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013 (pp. 242-247). (Proceedings of the 2013 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2013; Vol. 1). CSREA Press.