@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}
}
@article {1489,
	title = {Image diffusion using saliency bilateral filter.},
	journal = {Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
	volume = {9},
	year = {2006},
	month = {2006},
	pages = {67-75},
	abstract = {<p>Image diffusion can smooth away noise and small-scale structures while retaining important features, thereby enhancing the performances of many image processing algorithms such as image compression, segmentation and recognition. In this paper, we present a novel diffusion algorithm for which the filtering kernels vary according to the perceptual saliency of boundaries in the input images. The boundary saliency is estimated through a saliency measure which is generally determined by curvature changes, intensity gradient and the interaction of neighboring vectors. The connection between filtering kernels and perceptual saliency makes it possible to remove small-scale structures and preserves significant boundaries adaptively. The effectiveness of the proposed approach is validated by experiments on various medical images including the color Chinese Visible Human data set and gray MRI brain images.</p>
},
	keywords = {Algorithms, Artificial Intelligence, Automated, Brain, Cluster Analysis, Computer-Assisted, Diffusion Magnetic Resonance Imaging, Humans, Image Enhancement, Image Interpretation, Magnetic Resonance Imaging, Pattern Recognition, Reproducibility of Results, Sensitivity and Specificity},
	author = {Xie, Jun and Heng, Pheng-Ann and Ho, Simon S M and Shah, Mubarak}
}
