Classification of Benign and Malignant bone lesions on CT ImagesUsing Support Vector Machine: A Comparison of Kernel Functions

Kumar, Rishav and Suhas, M V (2016) Classification of Benign and Malignant bone lesions on CT ImagesUsing Support Vector Machine: A Comparison of Kernel Functions. In: IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology, 20/05/2016, Sri Venkatewara College of Engineering.

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Official URL: http://ieeexplore.ieee.org/document/7807941/

Abstract

Skeletal metastasis has tendency to develop from any kind of primary tumor. In the spine, the vertebral body is the most common site of metastasis which then extends to pedicle. About 2/3rd of the malignant tumor cases are found to develop metastasis. This work presents a Computer Aided Diagnosis (CAD) system that helps radiologists in differentiating malignant and benign bone lesions in the spine on Computed Tomography (CT) images usingSupport Vector Machines(SVM).The CT images are segmented using Snakes or Active Contour Model to retrieve the Region of Interest(ROI). From the segmented images, Haralick features are calculated. These features are then passed to the SVM classifier. With the help of SVM model generated, the data are classified into benign and malignant nodules. The performances of different kernel functions are compared.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Bone metastases, Haralick Features, Snakes, Support Vector Machines, SVM Kernels.
Subjects: Engineering > MIT Manipal > Electronics and Communication
Depositing User: MIT Library
Date Deposited: 19 Jan 2017 12:56
Last Modified: 19 Jan 2017 12:56
URI: http://eprints.manipal.edu/id/eprint/148156

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