Raja S. Alomari

Labeling of Lumbar Discs Using Both Pixel- and Object-Level Features With a Two-Level Probabilistic Model

Backbone anatomical structure detection and labeling is a necessary step for various analysis tasks of the vertebral column. Appearance, shape and geometry measurements are necessary for abnormality detection locally at each disc and vertebrae (such as herniation) as well as globally for the whole spine (such as spinal scoliosis). We propose a two-level probabilistic model for the localization of discs from clinical magnetic resonance imaging (MRI) data that captures both pixel- and object-level features. Using a Gibbs distribution, we model appearance and spatial information at the pixel level, and at the object level, we model the spatial distribution of the discs and the relative distances between them. We use generalized expectation-maximization for optimization, which achieves efficient convergence of disc labels. Our two-level model allows the assumption of conditional independence at the pixel-level to enhance efficiency while maintaining robustness. We use a dataset that contains 105 MRI clinical normal and abnormal cases for the lumbar area. We thoroughly test our model and achieve encouraging results on normal and abnormal cases.

Lumbar Disc Localization and Labeling with a Probabilistic Model on Both Pixel and Object Features

Repeatable, quantitative assessment of intervertebral disc pathology requires accurate localization and labeling of the lumbar region discs. To that end, we propose a two-level probabilistic model for such disc localization and labeling. Our model integrates both pixel-level information, such as appearance, and object-level information, such as relative location. Utilizing both levels of information adds robustness to the ambiguous disc intensity signature and high structure variation. Yet, we are able to do efficient (and convergent) localization and labeling with generalized expectation-maximization. We present accurate results on 20 normal cases (96%) and a promising extension to a pathology case.

Automatic Lumbar Vertebra Segmentation from clinical CT for Wedge Compression Fracture Diagnosis

Lumbar vertebral fractures vary greatly in types and causes and usually result from severe trauma or pathological conditions such as osteoporosis. Lumbar wedge compression fractures are amongst the most common ones where the vertebra is severely compressed forming a wedge shape and causing pain and pressure on the nerve roots and the spine. Since vertebral segmentation is the first step in any automated diagnosis task, we present a fully automated method for robustly localizing and segmenting the vertebrae for preparation of vertebral fracture diagnosis. Our segmentation method consists of five main steps towards the CAD(Computer-Aided Diagnosis) system: 1) Localization of the intervertebral discs. 2) Localization of the vertebral skeleton. 3) Segmentation of the individual vertebra. 4) Detection of the vertebrae center line and 5) Detection of the vertebrae major boundary points. Our segmentation results are promising with an average error of 1.5mm (modified Hausdorff distance metric) on 50 clinical CT cases i.e. a total of 250 lumbar vertebrae. We also present promising preliminary results for automatic wedge compression fracture diagnosis on 15 cases, 7 of which have one or more vertebral compression fracture, and obtain an accuracy of 97.33%.

Computer-aided diagnosis of lumbar disc pathology from clinical lower spine MRI

PurposeDetection of abnormal discs from clinical T2-weighted MR Images. This aids the radiologist as well as subsequent CAD methods in focusing only on abnormal discs for further diagnosis. Furthermore, it gives a degree of confidence about the abnormality of the intervertebral discs that helps the radiologist in making his decision.Materials and methodsWe propose a probabilistic classifier for the detection of abnormality of intervertebral discs. We use three features to label abnormal discs that include appearance, location, and context. We model the abnormal disc appearance with a Gaussian model, the location with a 2D Gaussian model, and the context with a Gaussian model for the distance between abnormal discs. We infer on the middle slice of the T2-weighted MRI volume for each case. These MRI scans are specific for the lumbar area. We obtain our gold standard for the ground truth from our collaborating radiologist group by having the clinical diagnosis report for each case.ResultsWe achieve over 91% abnormality detection accuracy in a cross-validation experiment with 80 clinical cases. The experiment runs ten rounds; in each round, we randomly leave 30 cases out for testing and we use the other 50 cases for training.ConclusionWe achieve high accuracy for detection of abnormal discs using our proposed model that incorporates disc appearance, location, and context. We show the extendability of our proposed model to subsequent diagnosis tasks specific to each intervertebral disc abnormality such as desiccation and herniation.

Toward a clinical lumbar CAD: herniation diagnosis

PurposeA CAD system for lumbar disc degeneration and herniation based on clinical MR images can aid diagnostic decision-making provided the method is robust, efficient, and accurate.Material and methodsA Bayesian-based classifier with a Gibbs distribution was designed and implemented for diagnosing lumbar disc herniation. Each disc is segmented with a gradient vector flow active contour model (GVF-snake) to extract shape features that feed a classifier. The GVF-snake is automatically initialized with an inner boundary of the disc initiated by a point inside the disc. This point is automatically generated by our previous work on lumbar disc labeling. The classifier operates on clinical T2-SPIR weighted sagittal MRI of the lumbar area. The classifier is applied slice-by-slice to tag herniated discs if they are classified as herniated in any of the 2D slices. This technique detects all visible herniated discs regardless of their location (lateral or central). The gold standard for the ground truth was obtained from collaborating radiologists by analyzing the clinical diagnosis report for each case.ResultsAn average 92.5% herniation diagnosis accuracy was observed in a cross-validation experiment with 65 clinical cases. The random leave-out experiment runs ten rounds; in each round, 35 cases were used for testing and the remaining 30 cases were used for training.ConclusionAn automatic robust disk herniation diagnostic method for clinical lumbar MRI was developed and tested. The method is intended for clinical practice to support reliable decision-making.

Computer-aided diagnosis for lumbar mri using heterogeneous classifiers

In this paper we propose a robust and fully automated lumbar herniation diagnosis system based on clinical MRI data which will not only aid a radiologist to make a decision with increased confidence, but will also reduce the time needed to analyze each case. Our method is based on three steps: 1) We automatically label the five lumbar intervertebral discs in a sagittal MRI slice using a probabilistic model and then extract an ROI for each disc using an Active Shape Model. 2) We generate relevant intensity and texture features from each disc ROI. 3) We construct five different classifiers (SVM, PCA+LDA, PCA+Naive Bayes, PCA+QDA, PCA+SVM) and combine them in a majority voting scheme. We perform 5-fold cross-validation experiments and achieve an accuracy of 94.85%, specificity of 95.9% and sensitivity of 92.45% for 35 clinical cases, i.e. a total of 175 lumbar intervertebral discs.

Segmentation of the Liver from Abdominal CT Using Markov Random Field Model and GVF Snakes

Liver segmentation from scans of the abdominal area is an important step in several diagnostic processes. CT scans of the abdominal area contain several organs in close proximity exhibiting similar image characteristics. In this paper, we present preliminary results on an algorithm that uses Markov random fields to obtain an initial contour of the liver. Gradient vector fields (GVF) and active contours are used to refine the initial estimate and segment the liver. Tests are reported on 13 clinical cases using a similarity metric that combines area and space.

Composite features for automatic diagnosis of intervertebral disc herniation from lumbar MRI

Lower back pain is widely prevalent in the world today, and the situation is aggravated due to a shortage of radiologists. Intervertebral disc disorders like desiccation, degeneration and herniation are some of the major causes of lower back pain. In this paper, we propose a robust computer-aided herniation diagnosis system for lumbar MRI by first extracting an approximate Region Of Interest (ROI) for each disc and then using a combination of viable features to produce a highly accurate classifier. We describe the extraction of raw, LBP (Local Binary Patterns), Gabor, GLCM (Gray-Level Co-occurrence Matrix), shape, and intensity features from lumbar SPIR T2-weighted MRI and also present a thorough performance comparison of individual and combined features. We perform 5-fold cross validation experiments on 35 cases and report a very high accuracy of 98.29% using a combination of features. Also, combining the desired features and reducing the dimensionality using LDA, we achieve a high sensitivity (true positive rate) of 98.11%.

Automatic diagnosis of lumbar disc herniation with shape and appearance features from MRI

Intervertebral disc herniation is a major reason for lower back pain (LBP), which is the second most common neurological ailment in the United States. Automation of herniated disc diagnosis reduces the large burden on radiologists who have to diagnose hundreds of cases each day using clinical MRI. We present a method for automatic diagnosis of lumbar disc herniation using appearance and shape features. We jointly use the intensity signal for modeling the appearance of herniated disc and the active shape model for modeling the shape of herniated disc. We utilize a Gibbs distribution for classification of discs using appearance and shape features. We use 33 clinical MRI cases of the lumbar area for training and testing both appearance and shape models. We achieve over 91% accuracy in detection of herniation in a cross-validation experiment with specificity of 91% and sensitivity of 94%.

Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model

Lower Back Pain (LBP) is the second most common neurological ailment in the United States after the headache. It costs over