Classification of brain MR images using corpus callosum shape measurements

Gaurav Vivek Bhalerao, Niranjana Sampathila

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

The corpus callosum is the largest white matter structure in the brain, which connects the two cerebral hemispheres and facilitates the inter-hemispheric communication. Abnormal anatomy of corpus callosum has been revealed for various brain related diseases. Being an important biomarker, Magnetic Resonance Imaging of the brain followed by corpus callosum segmentation and feature extraction has found to be important for the diagnosis of many neurological diseases. This paper focuses on classification of T1-weighted mid-sagittal MR images of brain for dementia patients. The corpus callosum is segmented using K-means clustering algorithm and corresponding shape based measurements are used as features. Based on these shape based measurements, a back-propagation neural network is trained separately for male and female dataset. The input data consists of 54 female and 31 male patients. This paper reports classification accuracy up to 92% for female patients and 94% for male patients using neural network classifier.

Original languageEnglish
Title of host publicationMedical Imaging
Subtitle of host publicationConcepts, Methodologies, Tools, and Applications
PublisherIGI Global Publishing
Pages1427-1436
Number of pages10
ISBN (Electronic)9781522505723
ISBN (Print)1522505717, 9781522505716
DOIs
Publication statusPublished - 18-07-2016

All Science Journal Classification (ASJC) codes

  • Medicine(all)
  • Health Professions(all)

Fingerprint Dive into the research topics of 'Classification of brain MR images using corpus callosum shape measurements'. Together they form a unique fingerprint.

  • Cite this

    Bhalerao, G. V., & Sampathila, N. (2016). Classification of brain MR images using corpus callosum shape measurements. In Medical Imaging: Concepts, Methodologies, Tools, and Applications (pp. 1427-1436). IGI Global Publishing. https://doi.org/10.4018/978-1-5225-0571-6.ch060