ICIAR 2017: Image Analysis and Recognition pp 446-454 | Cite as

Curvelet-Based Classification of Brain MRI Images

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10317)

Abstract

Classification of brain MRI images is crucial in medical diagnosis. Automatic classification of these images helps in developing effective non-invasive procedures. In this paper, based on curvelet transform, a novel classification scheme of brain MRI images is proposed and a technique for extracting and selecting curvelet features is provided. To study the effectiveness of their use, the proposed features are employed into three different prediction algorithms, namely, K-nearest neighbours, support vector machine and decision tree. The method of K-fold stratified cross validation is used to assess the efficacy of the proposed classification solutions and the results are compared with those of various state-of-the-art classification schemes available in the literature. The experimental results demonstrate the superiority of the proposed decision tree classification scheme in terms of accuracy, generalization capability, and real-time reliability.

Keywords

MRI imaging Curvelet transform Feature extraction and classification 
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Notes

Acknowledgments

The authors would like to thank Yudong Zhang for providing a portion of the MRI dataset.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institut de génie biomédicalÉcole Polytechnique de MontréalMontrealCanada
  2. 2.Department of Electrical and Computer EngineeringConcordia UniversityMontrealCanada

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