![]() ![]() 2)VWMS and endocardial border motion are closely related with accurate automated border detection, automated WMA classification should be feasible. Conclusions: 1)ASCs show promising accuracy for automated WMA classification. Volumetric parameters correlated poorly to regional VWMS. ![]() Discriminant analysis showed good prediction from low #ASCs of both segmental (85% correctness) and global WMA (90% correctness). Infarct severity measures correlated poorly to both ASCs and VWMS. ![]() ![]() Linear regression showed clear correlations between ASCs and VWMS. AAMMs were generated from the training set and for all sequences ASCs were extracted and statistically related to regional/global Visual Wall Motion Scoring (VWMS) and clinical infarct severity and volumetric parameters. A set of stress echocardiograms (single-beat 4-chamber and 2-chamber sequences with expert-verified endocardial contours) of 129 infarct patients was split randomly into training (n=65) and testing (n=64) sets. A previously developed Active Appearance Model for time sequences (AAMM) was employed to derive AAMM shape coefficients (ASCs) and we hypothesized these would allow classification of wall motion abnormalities (WMA). Principal Component Analysis of sets of temporal shape sequences renders eigenvariations of shape/motion, including typical normal and pathological endocardial contraction patterns. The average accuracy based on sensitivity and specificity was 0.87 for thirty four marked regions. To validate the segmented position and boundary one grader was asked to digitally outline the drusen boundary. The percentages within the acceptable error between the three graders and the computer are as follows: Grader-A: Area: 84% Size: 81% Grader-B: Area: 63% Size: 76% Grader-C: Area: 81% Size: 88%. Using the Wisconsin Age-Related Maculopathy Grading System, three graders classified the retinal images according to drusen size and area of involvement. For training and validation, the University of Wisconsin provided longitudinal images of 22 subjects from their 10 year Beaver Dam Study. Lesion size and area distribution statistics are then calculated. A binary image is found by applying Otsu's method to the reconstructed image. An algorithm is presented that first classifies the image to optimize the variables of a mathematical morphology algorithm. Variations in the subject's retinal pigmentation, size and profusion of the lesions, and differences in image illumination and quality present significant challenges to most segmentation algorithms. A computer-based system has been developed to provide the ability to track the position and margin of the ARMD associated lesion drusen. Overall, a 5-fold cross-validation accuracy rate of 80% was achieved in the automatic classification of MS lesion voxels using the proposed SVM-RBF classifier.Īge-Related Macular Degeneration (ARMD) is the leading cause of irreversible visual loss among the elderly in the US and Europe. The kernel parameter (γ) and the penalty value for the errors were determined by using a very loose stopping criterion for the SVM decomposition. The SVM kernel used in this study was the radial basis function (RBF). A preprocessing stage including anisotropic diffusion filtering, non-uniformity intensity correction, and intensity tissue normalization was applied to the images. These results were used later in the training and testing stages of the SVM classifier. A neuroradiologist used a computer-assisted technique to identify all MS lesions in each study. A total of eighteen studies (each composed of T1-, T2-weighted and FLAIR images) acquired from a 3T GE Signa scanner was analyzed. In this paper we present preliminary results to automatically segment multiple sclerosis (MS) lesions in multispectral magnetic resonance datasets using support vector machines (SVM). ![]()
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