Performance of statistical models of shape and appearance for semiautomatic segmentations of spinal vertebrae T4-L4 on digitized vertebral fracture assessment images.

BACKGROUND CONTEXT: Despite its clinical importance, accurate identification of vertebral fractures is problematic and time-consuming. There is a recognized need to improve the detection of vertebral fractures so that appropriate high-risk patients can be selected to initiate clinically beneficial therapeutic interventions. PURPOSE: To develop and evaluate semiautomatic algorithms for detailed annotation of vertebral bodies from T4 to L4 in digitized lateral spinal dual-energy X-ray absorptiometry (DXA) vertebral fracture assessment (VFA) images. STUDY DESIGN: Using lateral spinal DXA VFA images from subjects imaged at University Hospital fracture liaison service, image algorithms were developed for semiautomatic detailed annotation of vertebral bodies from T4 to L4. PATIENT SAMPLE: Two hundred one women aged 50 years or older with nonvertebral fractures. OUTCOME MEASURES: Algorithm accuracy and precision. METHODS: Statistical models of vertebral shape and appearance from T4 to L4 were constructed using VFA images from 130 subjects. The resulting models form a part of an algorithm for performing semiautomatic detailed annotation of vertebral bodies from T4 to L4. Algorithm accuracy and precision were evaluated on a test-set of 71 independent images. RESULTS: Overall accuracy was 0.72 mm (3.00% of vertebral height) and overall precision was 0.26 mm (1.11%) for point-to-line distance. Accuracy and precision were best on normal vertebrae (0.65 mm [2.67%] and 0.21 mm [0.90%], respectively) and mild fractures (0.78 mm [3.18%] and 0.32 mm [1.39%], respectively), but accuracy and precision errors were higher for moderate (1.07 mm [4.66%] and 0.48 mm [2.15%], respectively) and severe fractures (2.07 mm [9.65%] and 1.10 mm [5.09%], respectively). Accuracy and precision results for the algorithm were comparable with other reported results in the literature. CONCLUSIONS: This semiautomatic image analysis had high overall accuracy and precision on normal vertebrae and mild fractures, but performed less well in moderate and severe fractures. It is, therefore, a useful tool to identify normality of vertebral shape and to identify mild fractures.

Automatic rib segmentation and labeling in computed tomography scans using a general framework for detection, recognition and segmentation of objects in volumetric data.

A system for automatic segmentation and labeling of the complete rib cage in chest CT scans is presented. The method uses a general framework for automatic detection, recognition and segmentation of objects in three-dimensional medical images. The framework consists of five stages: (1) detection of relevant image structures, (2) construction of image primitives, (3) classification of the primitives, (4) grouping and recognition of classified primitives and (5) full segmentation based on the obtained groups. For this application, first 1D ridges are extracted in 3D data. Then, primitives in the form of line elements are constructed from the ridge voxels. Next a classifier is trained to classify the primitives in foreground (ribs) and background. In the grouping stage centerlines are formed from the foreground primitives and rib numbers are assigned to the centerlines. In the final segmentation stage, the centerlines act as initialization for a seeded region growing algorithm. The method is tested on 20 CT-scans. Of the primitives, 97.5% is classified correctly (sensitivity is 96.8%, specificity is 97.8%). After grouping, 98.4% of the ribs are recognized. The final segmentation is qualitatively evaluated and is very accurate for over 80% of all ribs, with slight errors otherwise.

A trained spin-glass model for grouping of image primitives.

A method is presented that uses grouping to improve local classification of image primitives. The grouping process is based upon a spin-glass system, where the image primitives are treated as possessing a spin. The system is subject to an energy functional consisting of a local and a bilocal part, allowing interaction between the image primitives. Instead of defining the state of lowest energy as the grouping result, the mean state of the system is taken. In this way, instabilities caused by multiple minima in the energy are being avoided. The means of the spins are taken as the a posteriori probabilities for the grouping result. In the paper, it is shown how the energy functional can be learned from example data. The energy functional is defined in such a way that, in case of no interactions between the elements, the means of the spins equal the a priori local probabilities. The grouping process enables the fusion of the a priori local and bilocal probabilities into the a posteriori probabilities. The method is illustrated both on grouping of line elements in synthetic images and on vessel detection in retinal fundus images.

Automatic detection of red lesions in digital color fundus photographs.

The robust detection of red lesions in digital color fundus photographs is a critical step in the development of automated screening systems for diabetic retinopathy. In this paper, a novel red lesion detection method is presented based on a hybrid approach, combining prior works by Spencer et al. (1996) and Frame et al. (1998) with two important new contributions. The first contribution is a new red lesion candidate detection system based on pixel classification. Using this technique, vasculature and red lesions are separated from the background of the image. After removal of the connected vasculature the remaining objects are considered possible red lesions. Second, an extensive number of new features are added to those proposed by Spencer-Frame. The detected candidate objects are classified using all features and a k-nearest neighbor classifier. An extensive evaluation was performed on a test set composed of images representative of those normally found in a screening set. When determining whether an image contains red lesions the system achieves a sensitivity of 100% at a specificity of 87%. The method is compared with several different automatic systems and is shown to outperform them all. Performance is close to that of a human expert examining the images for the presence of red lesions.

Ridge-based vessel segmentation in color images of the retina.

A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kappaNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. The results show that our method is significantly better than the two rule-based methods (p < 0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.

Active shape model segmentation with optimal features.

An active shape model segmentation scheme is presented that is steered by optimal local features, contrary to normalized first order derivative profiles, as in the original formulation [Cootes and Taylor, 1995, 1999, and 2001]. A nonlinear kNN-classifier is used, instead of the linear Mahalanobis distance, to find optimal displacements for landmarks. For each of the landmarks that describe the shape, at each resolution level taken into account during the segmentation optimization procedure, a distinct set of optimal features is determined. The selection of features is automatic, using the training images and sequential feature forward and backward selection. The new approach is tested on synthetic data and in four medical segmentation tasks: segmenting the right and left lung fields in a database of 230 chest radiographs, and segmenting the cerebellum and corpus callosum in a database of 90 slices from MRI brain images. In all cases, the new method produces significantly better results in terms of an overlap error measure (p < 0.001 using a paired T-test) than the original active shape model scheme.