DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Science, Engineering and Technology(IJASEAT)-IJASEAT |
 Journal Home Volume Issue |
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Issue |
Volume-7, Issue-1, Spl. Iss-1 ( Feb, 2019 ) | |||||||||
Paper Title |
The Detection of Abnormalities in Lumber Spine MRI Images with Computer Aided Diagnostic Systems | |||||||||
Author Name |
Mamona Mumtaz, Munir Ahmad | |||||||||
Affilition |
Lecturer, Department of Physics, University of Lahore Principal Scientist, Institute of Nuclear Medicine and Oncology (INMOL), Lahore, Pakistan | |||||||||
Pages |
31-34 | |||||||||
Abstract |
In today’s world the lower back pain the most significant problem and amongst the most of the causes of lower back pain, disc herniation is the major one. In this study, we proposed a robust CAD system for the detection of the abnormalities in the lumbar spine MRI images. One of the major advantages of this study is that it does not require specific segmentation of the MRI images and gives accurate results. By using KNN and SVM classifiers we found that SVM classifiers the best classifiers for the detection of the lumbar herniated disc. Furthermore, we also test the impact of bulging disc on the accuracy by treating bulging disc as normal and herniated. We found that bulging disc is an initial stage of herniation and thus considering the bulging disc as herniated, we achieve 95% accuracy. Keywords- computer-aided diagnostics, lumbar herniated disc, bulging disc, intensity features, texture features, classification, KNN, SVM | |||||||||
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