Classification of Brain MR Images Using Corpus Callosum Shape Measurements

Bhalerao, Gaurav Vivek and Niranjana, S (2017) Classification of Brain MR Images Using Corpus Callosum Shape Measurements. In: Medical Imaging: Concepts, Methodologies, Tools, and Applications. IGI Global, pp. 1427-1429.

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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.

Item Type: Book Section
Uncontrolled Keywords: corpous callosaum, BPNN, MRI
Subjects: Engineering > MIT Manipal > Biomedical
Depositing User: MIT Library
Date Deposited: 30 Dec 2017 09:12
Last Modified: 30 Dec 2017 09:12

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