Ayse Betül Oktay

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We propose a novel fully automatic approach to localize the lumbar intervertebral discs in MR images with PHOG based SVM and a probabilistic graphical model. At the local level, our method assigns a score to each pixel in target image that indicates whether it is a disc center or not. At the global level, we define a chain-like graphical model that(More)
This paper presents a method for localizing and labeling the lumbar vertebrae and intervertebral discs in mid-sagittal MR image slices. The approach is based on a Markov-chain-like graphical model of the ordered discs and vertebrae in the lumbar spine. The graphical model is formulated by combining local image features and semiglobal geometrical(More)
This paper presents a novel level set method with shape priors. The method keeps the level set deformations and the integration of the prior information as separate processes and hence it can be used with any level set formulation without complicating the level set functional. The method does not need any explicit training phase and by the addition of an(More)
Prostate cancer is one of the most frequent cancers among men. Abdominal Ultrasound scans are very practical alternatives to more precise but inconvenient Transrectal Ultra-sound scans for the diagnosis and treatment of prostate cancer. However, detection of the prostate region alone is very difficult for the Abdominal Ultrasound images. This paper presents(More)
This paper presents a novel method for the automated diagnosis of the degenerative intervertebral disc disease in midsagittal MR images. The approach is based on combining distinct disc features under a machine learning framework. The discs in the lumbar MR images are first localized and segmented. Then, intensity, shape, context, and texture features of(More)