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Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and...
Fractal theory is widely used in every field of biomedicine. Digital segmentation recognition and fractal analysis of brain medical images in 30 infants (16 boys, 14 girls, age 27-36ms, average age 33.87 ± 1.12ms ) shows that infants' brains have been highly differentiated. Further clinical randomized controlled study, which was used to test the validity and accuracy of the mathematical morphological...
We present a method for real-time simultaneous object recognition and segmentation based on cascaded discriminative Markov random fields. A Markov random field models coupling between the labels of adjacent image regions. The MRF affinities are learned as linear functions of image features in a structured max-margin framework that admits a solution via convex optimization. In contrast to other known...
Accurately extracting the airway tree is of utmost importance to correctly analyze CT images of the lungs. A survey of published methods reveals the existence of a trade-off between sensitivity -number of airway branches found- and accuracy -how much parenchymal leakage occurs-. In this paper, we present an algorithm for robust airway segmentation that attains both high sensitivity and accuracy. This...
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