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A reliable method to evaluate and follow the course of arthritis is given by examination of the carpal bones within the wrist joint. Humans typically have eight such small angular bones arranged in two rows. The small size as well as the number make manual segmentation for an analysis of the disease progression a tedious process. Further, fully automatic approaches are still not very reliable. To...
This paper presents a feature-selection-based data fusion method to follow up the evolution of brain tumors under therapeutic treatments with multi-spectral MRI data sequences. The fusion of MRI data is proposed to use a feature selection method to choose the most important features to classify tumor tissues and non-tumor tissues. Our system consists of three steps for each MRI examination (one examination...
In this paper, we propose a new segmentation algorithm that combines a graph-based shape model with image cues based on boosted features. The landmark-based shape model encodes prior constraints through the normalized Euclidean distances between pairs of control points, alleviating the need of a large database for the training. Moreover, the graph topology is deduced from the dataset using manifold...
High-throughput microscopy allows fast imaging of large tissue samples, producing an unprecedented amount of sub-cellular information. The size and complexity of these data sets often out-scale current reconstruction algorithms. Overcoming this computational bottleneck requires extensive parallel processing and scalable algorithms. As high-throughput imaging techniques move into main stream research,...
Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. Mammography has been one of the most reliable methods for early detection of breast carcinomas. However, it is difficult for radiologists to provide both accurate and uniform evaluation for the enormous mammograms generated in widespread screening. The main...
Designing an effective classifier has been a challenging task in the previous methods proposed in the literature. In this paper, we apply a combination of feature selection algorithm and neural network classifier in order to recognize five types of white blood cells in the peripheral blood. For this purpose, first nucleus and cytoplasm are segmented using Gram-Schmidt method and snake algorithm, respectively;...
Accurate segmentation of tissue microarrays is a challenging topic because of some of the similarities exhibited by normal tissue and tumor regions. Processing speed is another consideration when dealing with imaged tissue microarrays as each microscopic slide may contain hundreds of digitized tissue discs. In this paper, a fast and accurate image segmentation algorithm is presented. Both a whole...
We propose a novel representation of shape variation using diffusion wavelets, and a search paradigm based on local features. The representation can reflect arbitrary and continuous interdependencies in the training data. In contrast to state-of-the-art methods our approach during the learning stage optimizes the coefficients as well as the number and the position of landmarks using geometric constraints...
A method is proposed for the representation of localised features using disjoint sub-images taken from several datasets of retinal images for use within an incremental learning system. A tile-based localised adaptive threshold selection method was taken for vessel segmentation based on separate colour components. Arteriole-venous differentiation was done using the composite of these components and...
Automatically extracting vertebra regions from a spinal magnetic resonance image is normally required as the first step to an intelligent spinal MR image diagnosis system. In this work, we develop a fully automatic vertebra detection and segmentation method. Our system consists of three stages; namely, AdaBoost-based vertebra detection, detection refinement via robust curve fitting, and vertebra segmentation...
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