Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods

Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods Publisher

Salehi E¹ ; Yousefi H¹ ; Rashidi H² ; Ghanaatti H³

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1. Dept. Feculty of Computer and Information Technology Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2. Dept. Mathematics and Computer Science, Allameh Tabataba'i University, Tehran, Iran
3. Dept. Medical Imaging Center, Imam Khomeini University Hospital, Tehran, Iran

Source: 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019 Published:2019

Abstract

Low Back Pain (LBP) is one of the most common reason why people see a spine specialist. This back pain is often caused by lumbar disc herniation. Computer Aided Diagnosis (CAD) systems help reducing medical errors and improving health care quality, like speed and accuracy in diagnosis by radiologists. In this study, we used 50 clinical Magnetic Resonance Images (MRI) cases including 250 lumbar area discs. A diagnosis system have been developed to locate, label and segment discs by processing T1-weighted and T2-weighted sagittal view of MR images in order to diagnose herniation. Then, we extracted some distinct features such as shape, intensity, and geometric features of the discs. In the next step, we used k-Means clustering and active contour model (snake) on the region of interest (ROI) for segmenting discs and extracting features. We find the combination of effective features. The result demonstrated an average of 97.91% and 97.08% accuracy with K-fold cross validation method using k-Nearest Neighbor (KNN) and linear Support Vector Machine (SVM) classifiers respectively, to show a robust method. © 2019 IEEE.

Style	Citing Format
MLA	Salehi E, et al.. "Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods." 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019, vol. , no. , 2019, pp. -.
APA	Salehi E, Yousefi H, Rashidi H, Ghanaatti H (2019). Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods. 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019, (), -.
Chicago	Salehi E, Yousefi H, Rashidi H, Ghanaatti H. "Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods." 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019 , no. (2019): -.
Harvard	Salehi E et al. (2019) 'Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods', 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019, (), pp. -.
Vancouver	Salehi E, Yousefi H, Rashidi H, Ghanaatti H. Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods. 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019. 2019;():-.
BibTex	@article{ author = {Salehi E and Yousefi H and Rashidi H and Ghanaatti H}, title = {Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods}, journal = {2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019}, volume = {}, number = {}, pages = {-}, year = {2019} }
RIS	TY - JOUR AU - Salehi E AU - Yousefi H AU - Rashidi H AU - Ghanaatti H TI - Automatic Diagnosis of Disc Herniation in Two-Dimensional Mr Images With Combination of Distinct Features Using Machine Learning Methods JO - 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science# EBBT 2019 VL - IS - SP - EP - PY - 2019 ER -

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