Immune-Based Feature Selection in Rigid Medical Image Registration Using Supervised Neural Network

  • Joydev Hazra
  • Aditi Roy Chowdhury
  • Paramartha Dutta
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 611)

Abstract

Different radiological images like computed tomography (CT) and magnetic resonance (MR) are increasingly being used in medical science research for diagnosis and treatment. This article presents an automatic image registration technique to register MR–MR images using gray-level co-occurrence matrix (GLCM) and neural networks. This technique identifies different features of a brain image and its transformational counterpart. GLCM-based image feature extraction is a co-occurrence-based method by which different feature parameters are obtained. These parameters are calculated from the co-occurrence matrix along four directions, namely 0°, 45°, 90°, and 135°. Six features are selected from a set of features using an artificial immune system-based optimized feature selection technique and these six parameters are fed into the proposed neural network. Based on the principle of backpropagation algorithm, transformation parameters between the referenced and the sensed images are estimated. To demonstrate the effectiveness of the proposed method, experiment is carried out on MR T1, T2 datasets, and the results are compared with two other existing medical image registration techniques. The proposed method shows convincing results compared to others with respect to the estimation of underlying transformation parameters.

Keywords

Image registration GLCM Artificial immune system Feature extraction Backpropagation 

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Copyright information

© Springer India 2016

Authors and Affiliations

  • Joydev Hazra
    • 1
  • Aditi Roy Chowdhury
    • 2
  • Paramartha Dutta
    • 3
  1. 1.Department of Information TechnologyHeritage Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and TechnologyBipradas Pal Chowdhury Institute of TechnologyKrishnagarIndia
  3. 3.Department of Computer and System SciencesVisva-Bharati UniversitySantiniketanIndia

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