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 . 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.
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.
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.