Mr. Ke Li Profile

Ke Li

at Case Western Reserve Univ

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Proceedings Article | 26 March 2008 Paper

Gaussian weighted projection for visualization of cardiac calcification

Xiang Chen, Ke Li, Robert Gilkeson, Baowei Fei

Proceedings Volume 6918, 691831 (2008) https://doi.org/10.1117/12.772597

KEYWORDS: Tissues, Visualization, Heart, Image segmentation, Image visualization, Arteries, Calcium, Radiography, Computed tomography, Bone

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At our institution, we are using dual-energy digital radiography (DEDR) as a cost-effective screening tool for the detection of cardiac calcification. We are evaluating DEDR using CT as the gold standard. We are developing image projection methods for the generation of digitally reconstructed radiography (DRR) from CT image volumes. Traditional visualization methods include maximum intensity projection (MIP) and average-based projection (AVG) that have difficulty to show cardiac calcification. Furthermore, MIP can over estimate the calcified lesion as it displays the maximum intensity along the projection rays regardless of tissue types. For AVG projection, the calcified tissue is usually overlapped with bone, lung and mediastinum. In order to improve the visualization of calcification on DRR images, we developed a Gaussian-weighted projection method for this particular application. We assume that the CT intensity values of calcified tissues have a Gaussian distribution. We then use multiple Gaussian functions to fit the intensity histogram. Based on the mean and standard deviation parameters, we incorporate a Gaussian weighted function into the perspective projection and display the calcification exclusively. Our digital and physical phantom studies show that the new projection method can display tissues selectively. In addition, clinical images show that the Gaussian-weighted projection method better visualizes cardiac calcification than either the AVG or MIP method and can be used to evaluate DEDR as a screening tool for the detection of coronary artery diseases.

Proceedings Article | 13 March 2008 Paper

A deformable model-based minimal path segmentation method for kidney MR images

Ke Li, Baowei Fei

Proceedings Volume 6914, 69144F (2008) https://doi.org/10.1117/12.772347

KEYWORDS: Image segmentation, Kidney, Magnetic resonance imaging, Computer programming, Principal component analysis, Algorithm development, Data modeling, 3D image processing, Edge detection, Control systems

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We developed a new minimal path segmentation method for mouse kidney MR images. We used dynamic programming and a minimal path segmentation approach to detect the optimal path within a weighted graph between two end points. The energy function combines distance and gradient information to guide the marching curve and thus to evaluate the best path and to span a broken edge. An algorithm was developed to automatically place initial end points. Dynamic programming was used to automatically optimize and update end points during the searching procedure. Principle component analysis (PCA) was used to generate a deformable model, which serves as the prior knowledge for the selection of initial end points and for the evaluation of the best path. The method has been tested for kidney MR images acquired from 44 mice. To quantitatively assess the automatic segmentation method, we compared the results with manual segmentation. The mean and standard deviation of the overlap ratios are 95.19%±0.03%. The distance error between the automatic and manual segmentation is 0.82±0.41 pixel. The automatic minimal path segmentation method is fast, accurate, and robust and it can be applied not only for kidney images but also for other organs.

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