In image based computer aided diagnosis

higher accuracy can be achieved if the objects’ information of shape and texture can be used simultaneously. In spine MRI

intervertebral disc degeneration can be characterized by shape and texture variances. Therefore we propose a framework of computer aided diagnosis on disc degeneration as follows. First

we describe the disc degeneration by modeling non-rigid shape deformation and texture variance respectively. The deformation between the two shapes is measured by the geodesic length in the shape space. Similarly

the texture difference is measured by the Bhattacharyya distance between intensity distributions of two disc regions. Secondly

these two kinds of distances are added by weights as inputs of an unsupervised clustering machine. The clustering result is aimed at assisting doctors in distinguishing normal and degenerated intervertebral discs effectively. In experiments

108 lumbar discs from 18 examinees are selected as experimental data. The highest accuracy of our proposed method is 92.1%

higher than the accuracy when shape or texture information is being used separately.