Probabilistic atlases provide pivotal information for medical image segmentation and registration. Typically an atlas has been built on a common target image space which other training images are mapped onto. This introduces bias towards to the chosen target. Here we present a method to choose a target image which has the least bias considering all training images. Our method chooses a target i...

Current approaches to image classification require training images prepared by hand. In this paper, we describe experiments on image classification using images gathered from the Web automatically as training images. To gather images from the Web, we use the probabilistic method we proposed before. In the method, we build a generative model which is based on the Gaussian mixture model (GMM) fro...

The explosive availability of remote sensing images has challenged supervised classification algorithms such as Support Vector Machines (SVM), as training samples tend to be highly limited due to the expensive and laborious task of ground truthing. The temporal correlation and spectral similarity between multitemporal images have opened up an opportunity to alleviate this problem. In this study...

This paper presents a new, efficient and accurate technique for the semantic segmentation of medical images. The paper builds upon the successful random decision forests model and improves on it by modifying the way in which randomness is injected into the tree training process. The contribution of this paper is two-fold. First, we replace the conventional bagging procedure (the uniform samplin...

Using acomplex training image, such as an analog, for the SNESIM algorithm results in poor simulation since the training image contains trends and may not meet training image requirements. By pooling all the training image patterns in a single search tree but not recording the patterns’ relative locations, some critical features of these complex training images are lost. The search tree partiti...

The focus of image classification through supervised distance metric learning is to find an appropriate measure of similarity between images. Although this approach is effective in the presence of large amounts of training data, classification accuracy will deteriorate when the number of training samples is small, which, unfortunately, is often the situation in several medical applications. We ...

Background: Brain tissue segmentation for delineation of 3D anatomical structures from magnetic resonance (MR) images can be used for neuro-degenerative disorders, characterizing morphological differences between subjects based on volumetric analysis of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF), but only if the obtained segmentation results are correct. Due to image arti...

conclusions concerns about body image, an avoidant attachment style, and cognitive strategies to regulate negative emotions were the strongest predictors for eating disorder symptoms. based on current research findings, an avoidance attachment style, concerns about body image, and negative emotion regulation cognitive strategies increase eating disorder symptoms in students. because attachment ...