Mr. Yinxiao Liu Profile

Yinxiao Liu

Graduate Research Assistant at Univ of Iowa

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Proceedings Article | 17 February 2012 Paper

Extended contrast detection on fluoroscopy and angiography for image-guided trans-catheter aortic valve implantations (TAVI)

Yinxiao Liu, Rui Liao, Xudong Lv

Proceedings Volume 8316, 831618 (2012) https://doi.org/10.1117/12.911741

KEYWORDS: 3D modeling, Angiography, Detection and tracking algorithms, Heart, Fluoroscopy, Ions, X-rays, X-ray imaging, Image registration, Neodymium

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Navigation and deployment of the prosthetic valve during trans-catheter aortic valve implantation (TAVI) can be greatly facilitated with 3-D models showing detailed anatomical structures. Fast and robust automatic contrast detection at the aortic root on X-ray images is indispensable for automatically triggering a 2-D/3-D registration to align the 3-D model. Previously, we have proposed an automatic method for contrast detection at the aortic root on fluoroscopic and angiographic sequences [4]. In this paper, we extend that algorithm in several ways, making it more robust to handle more general and difficult cases. Specifically, the histogram likelihood ratio test is multiplied with the histogram portion computation to handle faint contrast cases. Histogram mapping corrects sudden changes in the global brightness, thus avoiding potential false positives. Respiration and heart beating check further reduces the false positive rate. In addition, a probe mask is introduced to enhance the contrast feature curve when the dark ultrasound probe partially occludes the aortic root. Lastly, a semi-global registration method for aligning the aorta shape model is implemented to improve the robustness of the algorithm with respect to the selection of region of interest (ROI) containing the aorta. The extended algorithm was evaluated on 100 sequences, and improved the detection accuracy from 94% to 100%, compared to the original method. Also, the robustness of the extended algorithm was tested with 20 different shifts of the ROI, and the error rate was as low as 0.2%, in comparison to 6.6% for the original method.

Proceedings Article | 30 October 2009 Paper

A new image thresholding and gradient optimization algorithm using object class uncertainty theory

Yinxiao Liu, Punam Saha

Proceedings Volume 7497, 749702 (2009) https://doi.org/10.1117/12.851184

KEYWORDS: Tissues, Image segmentation, Interfaces, Computed tomography, Medical imaging, Image processing, Optimization (mathematics), Algorithms, Abdomen, Visualization

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The knowledge of thresholding and gradients at different object interfaces is of paramount interest for image segmentation and other imaging applications. Most thresholding and gradient optimization methods primarily focus on image histograms and therefore, fail to harness the information embedded in image intensity patterns. Here, we investigate the role of a recently conceived object class uncertainty theory in image thresholding and gradient optimization. The notion of object class uncertainty, a histogram-based feature, is formulated and a computational solution is presented. An energy function is designed that captures spatio-temporal correlation between class uncertainty and image gradient which forms objects and shapes. Optimum thresholds and gradients for different object interfaces are determined from the shape of this energy function. The underlying theory behind the method is that objects manifest themselves with fuzzy boundaries in an acquired image and, in a probabilistic sense, intensities with high class uncertainty are associated with high image gradients generally appearing at object interfaces. The method has been applied on several medical as well as natural images and both thresholds and gradients have successfully been determined for different object interfaces even when some of the thresholds are almost impossible to locate in respective histograms.

Proceedings Article | 27 March 2009 Paper

A new method for thresholding and gradient optimization at different tissue interfaces using class uncertainty

Yinxiao Liu, Punam Saha

Proceedings Volume 7259, 72590H (2009) https://doi.org/10.1117/12.811355

KEYWORDS: Tissues, Computed tomography, Image segmentation, 3D image processing, Magnetic resonance imaging, Interfaces, Error analysis, Medical imaging, Bone, Brain

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The knowledge of thresholding and gradient at different tissue interfaces is of paramount interest in image segmentation and other imaging methods and applications. Most thresholding and gradient selection methods primarily focus on image histograms and therefore, fail to harness the information generated by intensity patterns in an image. We present a new thresholding and gradient optimization method which accounts for spatial arrangement of intensities forming different objects in an image. Specifically, we recognize object class uncertainty, a histogram-based feature, and formulate an energy function based on its correlation with image gradients that characterizes the objects and shapes in a given image. Finally, this energy function is used to determine optimum thresholds and gradients for various tissue interfaces. The underlying theory behind the method is that objects manifest themselves with fuzzy boundaries in an acquired image and that, in a probabilistic sense; intensities with high class uncertainty are associated with high image gradients generally indicating object/tissue interfaces. The new method simultaneously determines optimum values for both thresholds and gradient parameters at different object/tissue interfaces. The method has been applied on several 2D and 3D medical image data sets and it has successfully determined both thresholds and gradients for different tissue interfaces even when some of the thresholds are almost impossible to locate in their histograms. The accuracy and reproducibility of the method has been examined using 3D multi-row detector computed tomography images of two cadaveric ankles each scanned thrice with repositioning the specimen between two scans.

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