A comparison of feature extraction techniques for diagnosis of lumbar intervertebral degenerative disc disease

The reduction of fluid that acts as shock absorber placed in lumbar intervertebral discs causes pains and this case is named as degenerative disc disease. Magentic Resonance Imaging is generally used for diagnosis of this disease by radiologists or doctors. However, due to personal errors such as fatigue, inexperience, oversight, wrong diagnosis is possible. In order to prevent these, computer-aided diagnostic (CAD) methods are mostly preferred. In this work, the performance of two different feature extraction methods is compared. The saggital MR images taken from 9 patients were feature extracted by using grey level co-occurrence matrix (GLCM) and average absolute deviation (AAD) methods. The obtained feature vectors were classified by using multi-layered perceptron (MLP) artificial neural networks.