This article describes the SRI24 atlas, a new standard reference system of normal human brain anatomy, that was created using template-free population registration of high-resolution magnetic resonance images acquired at 3T in a group of 24 normal control subjects. The atlas comprises anatomical channels (T1, T2, and proton density weighted), diffusion-related channels (fractional anisotropy, m...

Label propagation has been shown to be effective in many automatic segmentation applications. However, its reliance on accurate image alignment means that segmentation results can be affected by any registration errors which occur. Patch-based methods relax this dependence by avoiding explicit one-to-one correspondence assumptions between images but are still limited by the search window size. ...

Trace ratio is a natural criterion in discriminant analysis as it directly connects to the Euclidean distances between training data points. This criterion is re-analyzed in this paper and a fast algorithm is developed to find the global optimum for the orthogonal constrained trace ratio problem. Based on this problem, we propose a novel semi-supervised orthogonal discriminant analysis via labe...

The label propagation algorithm (LPA) has been proved to be a fast and effective method for detecting communities in large complex networks. However, its performance is subject to the non-stable and trivial solutions of the problem. In this paper, we propose a modified label propagation algorithm LPAf to efficiently detect community structures in networks. Instead of the majority voting rule of...

Interactive foreground/background segmentation in a static image is a hot topic in image processing. Classical frameworks focus on providing one class label for the user to specify the foreground. This may be not enough in image editing. In this paper, we develop an interactive framework which can allow the user to label multiply foreground objects of interest. Our framework is constructed on b...

Groupwise segmentation that simultaneously segments a set of images and ensures that the segmentations for the same structure of interest from different images are consistent usually can achieve better performance than segmenting each image independently. Our main contribution is that we adopt the groupwise segmentation framework to improve the performance of multi-atlas label fusion. We develo...

In recent years, relevance feedback has been studied extensively as a way to improve performance of content-based image retrieval (CBIR). Since users are usually unwilling to provide much feedback, the insufficiency of training samples limits the success of relevance feedback. In this paper, we propose two strategies to tackle this problem: (i) to make relevance feedback more informative by pre...

Semi supervised learning methods have gained importance in today’s world because of large expenses and time involved in labeling the unlabeled data by human experts. The proposed hybrid approach uses SVM and Label Propagation to label the unlabeled data. In the process, at each step SVM is trained to minimize the error and thus improve the prediction quality. Experiments are conducted by using ...

Learning from complex data is becoming increasingly important, and graph kernels have recently evolved into a rapidly developing branch of learning on structured data. However, previously proposed kernels rely on having discrete node label information. Propagation kernels leverage the power of continuous node label distributions as graph features and hence, enhance traditional graph kernels to ...