High Frequency Assessment from Multiresolution Analysis
ICCS '09 Proceedings of the 9th International Conference on Computational Science: Part I
Blood vessel segmentation using multi-scale quadrature filtering
Pattern Recognition Letters
Localisation and tracking of an airport's approach lighting system
ICCVG'10 Proceedings of the 2010 international conference on Computer vision and graphics: Part I
An improved statistical approach for cerebrovascular tree extraction
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
Generalized rough fuzzy c-means algorithm for brain MR image segmentation
Computer Methods and Programs in Biomedicine
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Image segmentation is a fundamental problem in early computer vision. In segmentation of flat shaded, nontextured objects in real-world images, objects are usually assumed to be piecewise homogeneous. This assumption, however, is not always valid with images such as medical images. As a result, any techniques based on this assumption may produce less-than-satisfactory image segmentation. In this work, we relax the piecewise homogeneous assumption. By assuming that the intensity nonuniformity is smooth in the imaged objects, a novel algorithm that exploits the coherence in the intensity profile to segment objects is proposed. The algorithm uses a novel smoothness prior to improve the quality of image segmentation. The formulation of the prior is based on the coherence of the local structural orientation in the image. The segmentation process is performed in a Bayesian framework. Local structural orientation estimation is obtained with an orientation tensor. Comparisons between the conventional Hessian matrix and the orientation tensor have been conducted. The experimental results on the synthetic images and the real-world images have indicated that our novel segmentation algorithm produces better segmentations than both the global thresholding with the maximum likelihood estimation and the algorithm with the multilevel logistic MRF model.