Automatic spinal canal detection in lumbar MR images in the sagittal view using dynamic programming.

As there is an increasing need for the computer-aided effective management of pathology in lumbar spine, we have developed a computer-aided diagnosis and characterization framework using lumbar spine MRI that provides radiologists a second opinion. In this paper, we propose a left spinal canal boundary extraction method, based on dynamic programming in lumbar spine MRI. Our method fuses the absolute intensity difference of T1-weighted and T2-weighted sagittal images and the inverted gradient of the difference image into a dynamic programming scheme and works in a fully automatic fashion. The boundaries generated by our method are compared against reference boundaries in terms of the Euclidean distance and the Chebyshev distance. The experimental results from 85 clinical data show that our methods find the boundary with a mean Euclidean distance of 3mm, achieving a speedup factor of 167 compared with manual landmark extraction. The proposed method successfully extracts landmarks automatically and fits well with our framework for computer-aided diagnosis in lumbar spine.

Disc herniation diagnosis in MRI using a CAD framework and a two-level classifier.

PURPOSE: Disc herniation in the lumbar spine is a common condition, so an automated method for diagnosis could be helpful in clinical applications. A computer-aided framework for disk herniation diagnosis was developed for use in magnetic resonance imaging (MRI). MATERIALS AND METHOD: A computer-aided diagnosis framework for lumbar spine with a two-level classification scheme for disc herniation diagnosis was developed using heterogeneous classifiers: a perceptron classifier, a least mean square classifier, a support vector machine classifier, and a k-Means classifier. Each classifier makes a diagnosis based on a feature set generated from regions of interest that contain vertebrae, a disc, and the spinal cord. Then, an ensemble classifier makes a final decision using score values of each classifier. We used clinical MR image data from 70 subjects in T1-weighted sagittal view and T2-weighted sagittal view for evaluation of the system. RESULTS: MR images of 70 subjects were processed using the proposed framework resulting in successful detection of disc herniation with 99% accuracy, achieving a speedup factor of 30 in comparison with radiologist's diagnosis. CONCLUSION: The computer-aided framework works well to diagnose herniated discs in MRI scans. We expect the framework can be adapted to effectively diagnose a variety of abnormalities in the lumbar spine.

Automated boundary extraction of the spinal canal in MRI based on dynamic programming.

The spinal cord is the only communication link between the brain and the body. The abnormalities in it can lead to severe pain and sometimes to paralysis. Due to the growing gap between the number of available radiologists and the number of required radiologists, the need for computer-aided diagnosis and characterization is increasing. To ease this gap, we have developed a computer-aided diagnosis and characterization framework in lumbar spine that includes the spinal cord, vertebrae, and intervertebral discs. In this paper, we propose two spinal cord boundary extraction methods that fit into our framework based on dynamic programming in lumbar spine MRI. Our method incorporates the intensity of the image and the gradient of the image into a dynamic programming scheme and works in a fully-automatic fashion. The boundaries generated by our method is compared against reference boundaries in terms of Fréchet distance which is known to be a metric for shape analysis. The experimental results from 65 clinical data show that our method finds the spinal canal boundary correctly achieving a mean Fréchet distance of 13.5 pixels. For almost all data, the extracted boundary falls within the spinal cord. So, it can be used as a landmark when marking background regions and finding regions of interest.

Automatic segmentation of the spinal cord and the dural sac in lumbar MR images using gradient vector flow field.

A Computer-aided diagnosis (CAD) system aims to facilitate characterization and quantification of abnormalities as well as minimize interpretation errors caused by tedious tasks of image screening and radiologic diagnosis. The system usually consists of segmentation, feature extraction and diagnosis, and segmentation significantly affects the diagnostic performance. In this paper, we propose an automatic segmentation method that extracts the spinal cord and the dural sac from T2-weighted sagittal magnetic resonance (MR) images of lumbar spine without the need of any human intervention. Our method utilizes a gradient vector flow (GVF) field to find the candidate blobs and performs a connected component analysis for the final segmentation. MR Images from fifty two subjects were employed for our experiments and the segmentation results were quantitatively compared against reference segmentation by two medical specialists in terms of a mutual overlap metric. The experimental results showed that, on average, our method achieved a similarity index of 0.7 with a standard deviation of 0.0571 that indicated a substantial agreement. We plan to apply this segmentation method to computer-aided diagnosis of many lumbar-related pathologies.