Ayse Betül Oktay

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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)
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 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)
Extracting endocardium and epicardium from echocardiographic images is a challenging task because of large amounts of noise, signal drop-out, unrelated structures, and unseen wall parts. This paper introduces a new technique that automatically extracts cardiac borders by incorporating local and global priors through boosting and level set methods. The(More)
Aim of this study is to localize intervertebral discs from MR images. The study implements a system that uses HOG-SVM based method which was used in previous studies. In addition to this, the study uses projection method to obtain better results. Intensity values of MR images can vary from sample to sample. HOG features provide better results by using(More)
This paper presents a novel technique for the extraction of the left ventricle borders from echocardiograms with prior information. Although the literature includes many successful prior based methods, priors that include both image and non-image related features are rare for the contour extraction. We classify these features as local and global priors(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 ultrasound 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 uses a(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)