Clique Identification and Propagation for Multimodal Brain Tumor Image Segmentation

Brain tumors vary considerably in size, morphology, and location across patients, thus pose great challenge in automated brain tumor segmentation methods. Inspired by the concept of clique in graph theory, we present a clique-based method for multimodal brain tumor segmentation that considers a brain tumor image as a graph and automatically segment it into different sub-structures based on the clique homogeneity. Our proposed method has three steps, neighborhood construction, clique identification, and clique propagation. We constructed the neighborhood of each pixel based on its similarities to the surrounding pixels, and then extracted all cliques with a certain size k to evaluate the correlations among different pixels. The connections among all cliques were represented as a transition matrix, and a clique propagation method was developed to group the cliques into different regions. This method is also designed to accommodate multimodal features, as multimodal neuroimaging data is widely used in mapping the tumor-induced changes in the brain. To evaluate this method, we conduct the segmentation experiments on the publicly available Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) dataset. The qualitative and quantitative results demonstrate that our proposed clique-based method achieved better performance compared to the conventional pixel-based methods

Learning Grammars for Architecture-Specific Facade Parsing

International audienceParsing facade images requires optimal handcrafted grammar for a given class of buildings. Such a handcrafted grammar is often designed manually by experts. In this paper, we present a novel framework to learn a compact grammar from a set of ground-truth images. To this end, parse trees of ground-truth annotated images are obtained running existing inference algorithms with a simple, very general grammar. From these parse trees, repeated subtrees are sought and merged together to share derivations and produce a grammar with fewer rules. Furthermore, unsupervised clustering is performed on these rules, so that, rules corresponding to the same complex pattern are grouped together leading to a rich compact grammar. Experimental validation and comparison with the state-of-the-art grammar-based methods on four diff erent datasets show that the learned grammar helps in much faster convergence while producing equal or more accurate parsing results compared to handcrafted grammars as well as grammars learned by other methods. Besides, we release a new dataset of facade images from Paris following the Art-deco style and demonstrate the general applicability and extreme potential of the proposed framework

Graph-based Detection, Segmentation & Characterization of Brain Tumors

International audienceIn this paper we propose a novel approach for detection, segmentation and characterization of brain tumors. Our method exploits prior knowledge in the form of a sparse graph representing the expected spatial positions of tumor classes. Such information is coupled with image based classification techniques along with spatial smoothness constraints towards producing a reliable detection map within a unified graphical model formulation. Towards optimal use of prior knowledge, a two layer interconnected graph is considered with one layer corresponding to the low-grade glioma type (characterization) and the second layer to voxel-based decisions of tumor presence. Efficient linear programming both in terms of performance as well as in terms of computational load is considered to recover the lowest potential of the objective function. The outcome of the method refers to both tumor segmentation as well as their characterization. Promising results on substantial data sets demonstrate the extreme potentials of our method