Lumbar vertebral fracture seriously endangers the health of people, which has a higher mortality. Due to the tiny difference among various fracture features in CT images, multiple vertebral fractures classification has a great challenge for computer-aided diagnosis system. To solve this problem, this paper proposes a multiclass PSVM ensemble method with multi-feature selection to recognize lumbar vertebral fractures from spine CT images. In the proposed method, firstly, the active contour model is utilized to segment lumbar vertebral bodies. It is helpful for the subsequent feature extraction. Secondly, different image features are extracted, including 3 geometric shape features, 3 texture features, and 5 height ratios. The importance of these features is analyzed and ranked by using infinite feature selection method, thus selecting different feature subsets. Finally, three multiclass probability SVMs with binary tree structure are trained on three datasets. The weighted voting strategy is used for the final decision fusion. To validate the effectiveness of the proposed method, probability SVM, K-nearest neighbor, and decision tree as base classifiers are compared with or without feature selection. Experimental results on 25 spine CT volumes demonstrate that the advantage of the proposed method compared to other classifiers, both in terms of the classification accuracy and Cohen’s kappa coefficient.
Spine curvature disorders have been found relevant as the nervous system diseases and may produce serious disturbances of the whole body. The ability to automatically segment and locate the spinal vertebrae is, therefore, an important task for modern studies of the spinal curvature disorders detection. In this work, we devise a modern, simple and automated human spinal vertebrae segmentation and localization method using transfer learning, that works on CT and MRI acquisitions. We exploit pre-trained models to spinal vertebrae segmentation and localization problem. We first explore and evaluate different medical imaging architectures and choose the deep dilated convolutions as the initialization for our spinal vertebrae segmentation and localization task. Then we conduct the pre-trained model from spinal cord gray matter dataset to our spinal vertebrae segmentation task with supervised fine-tuning. The vertebral centroid coordinate can be computed from the segmented result, and the centroid localization error is used as the feedback for fine-tuning. We evaluate our method against traditional method on medical image segmentation and localization task and report the comparison of evaluation metrics. We show the qualitative and quantitative evaluation on spine CT images which are from spine CT volumes on the publicity platform SpineWeb. The evaluation results show that our approach was able to capture many properties of the spinal vertebrae, and provided good segmentation and localization performance. From our research we show that the deep dilated convolutions pre-trained on MRI spinal cord gray matter images can be transfer to process CT spinal vertebrae images.