@article {1492,
	title = {Shape registration by optimally coding shapes.},
	journal = {IEEE Transactions on Information Technology in Biomedicine : A Publication of the IEEE Engineering in Medicine and Biology Society},
	volume = {12},
	year = {2008},
	month = {2008 Sep},
	pages = {627-35},
	abstract = {<p>This paper formulates shape registration as an optimal coding problem. It employs a set of landmarks to establish the correspondence between shapes, and assumes that the best correspondence can be achieved when the polygons formed by the landmarks optimally code all the shape contours, i.e., obtain their minimum description length (MDL). This is different from previous MDL-based shape registration methods, which code the landmark locations. In this paper, each contour is discretized to be a set of points to make the coding feasible, and a number of strategies are adopted to tackle the difficult optimization problem involved. The resulting algorithm, called CAP, is able to yield statistical shape model with better quality in terms of model generalization error, which is demonstrated on both synthetic and biomedical shapes.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Computer-Assisted, Image Enhancement, Image Interpretation, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity},
	issn = {1558-0032},
	doi = {10.1109/TITB.2008.920798},
	author = {Jiang, Yifeng and Xie, Jun and Tsui, Hung-Tat}
}
@article {1490,
	title = {Boundary enhancement and speckle reduction for ultrasound images via salient structure extraction.},
	journal = {IEEE Transactions on Bio-Medical Engineering},
	volume = {53},
	year = {2006},
	month = {2006 Nov},
	pages = {2300-9},
	abstract = {<p>In this paper, we present an approach for medical ultrasound (US) image enhancement. It is based on a novel perceptual saliency measure which favors smooth, long curves with constant curvature. The perceptual salient boundaries of tissues in US images are enhanced by computing the saliency of directional vectors in the image space, via a local searching algorithm. Our measure is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. To restrain speckle noise during the enhancement process, an adaptive speckle suspension term is also combined into the proposed saliency measure. The results obtained on both simulated images and medical US data reveal superior performance of the novel approach over a number of commonly used speckle filters. Applications of US image segmentation show that although the proposed algorithm cannot remove the speckle noise completely and may discard weak anatomical structures in some case, it still provides a considerable gain to US image processing for computer-aided diagnosis.</p>
},
	keywords = {Algorithms, Automated, Computer-Assisted, Humans, Image Enhancement, Image Interpretation, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity, Ultrasonography},
	issn = {0018-9294},
	doi = {10.1109/TBME.2006.878088},
	author = {Xie, Jun and Jiang, Yifeng and Tsui, Hung-Tat and Heng, Pheng-Ann}
}
