Texture Feature Extraction Using MGRLBP Method for Medical Image Classification

  • Suganya Ramamoorthy
  • R. Kirubakaran
  • Rajaram Siva Subramanian
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 324)


Texture is an important significant property of medical images based on which images can be characterized and classified in a content-based image retrieval and classification system. This paper examines the feature extraction methods to ameliorate texture recognition accuracy by extracting the rotation-invariant texture feature from liver images by the individual Gabor filter method and by multi-scale Gabor rotation-invariant LBP (MGRLBP) method. The features extracted from both the approaches are tested on a set of 60 liver images of four different classes. The classification algorithms such as support vector machine (SVM) and k-nearest neighbor (KNN) were used to evaluate the extracted features from both methods, showing advancing improvements with the MGRLBP method over the individual method in the classification task.


Texture Feature extraction Texture analysis Rotation invariant 



The authors convey their heartfelt thanks to Dr. R. Sambath, Radiologist, and Dr. P.S. Rajan, MS of GEM Hospital, Coimbatore, for providing the medical image dataset used in this paper, and also Dr. Kasthurimohan, MD of Malar Hospital, Dindigul, and Dr. Mahalakshmi, DGO of Meenakshi Mission Hospital, Madurai, for their motivation and support for conducting this work and valuable suggestions at different stages of the work.


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Copyright information

© Springer India 2015

Authors and Affiliations

  • Suganya Ramamoorthy
    • 1
  • R. Kirubakaran
    • 1
  • Rajaram Siva Subramanian
    • 2
  1. 1.Department of CSEThiagarajar College of EngineeringMaduraiIndia
  2. 2.Department of ECEThiagarajar College of EngineeringMaduraiIndia

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