Mr. Yehuda K. Ben Zikri Profile

Yehuda K. Ben Zikri

Doctoral Candidate/Research Assistant at Rochester Institute of Technology

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Publications (6)

Proceedings Article | 13 March 2017 Presentation + Paper

Anatomical based registration of multi-sector x-ray images for panorama reconstruction

Yehuda Kfir Ben-Zikri, Stacy Mendez, Cristian Linte

Proceedings Volume 10137, 101370H (2017) https://doi.org/10.1117/12.2256047

KEYWORDS: Image registration, X-ray imaging, X-rays, Panoramic photography, Bone, Edge detection, Image processing, Feature extraction, Surgery, Digital x-ray imaging, Image segmentation, Image filtering, Data modeling, Transparency

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Accurate measurement of long limb alignment is an essential stage of the pre-operative planning of realignment surgery. This alignment is quantified according to the hip-knee-ankle (HKA) angle of the mechanical axis of the lower extremity and is measured based on a full-length weight-bearing X-ray or standard computed radiography (CR) image of the patient in standing position. Due to the limited field-of-view of the traditionally employed digital X-ray imaging systems, several sector images are required to capture the posture of a standing individual. These sector images need to then be stitched" together to reconstruct the standing posture. To eliminate user-induced variability and time constraints associated with the traditional manual "stitching" protocol, we have created an image processing application to automate the stitching process, when there are no reliable external markers available in the images, by only relying on the most reliable anatomical content of the image. The application starts with a rough segmentation of the tibia and the sector images are then registered by evaluating the DICE coefficient between the edges of these corresponding bones along the medial edge. The identified translations are then used to register the original sector images into the standing panorama image. To test the robustness of our method, we randomly selected 40 datasets from a variant database consisting of nearly 100 patient X-ray images acquired for patient screening as part of a multi-site clinical trial. The resulting horizontal and vertical translation values from the automated registration were compared to the homologous translations recorded during the manual panorama generation conducted by a knowledgeable X-ray imaging technician. The mean and standard deviation of the differences for the horizontal translation parameters was -0:27±1:14 mm and 0:31±1:86 mm for the left and right tibia, respectively. The vertical translation differences for the left and right tibia were 1:05±5:24 mm and 1:32±4:77 mm, respectively. For these differences, the expert radiologist reported no difference in the hip-knee-ankle angular assessment.

Proceedings Article | 13 March 2017 Paper

3D printing for orthopedic applications: from high resolution cone beam CT images to life size physical models

Amiee Jackson, Lawrence Ray, Shusil Dangi, Yehuda Ben-Zikri, Cristian Linte

Proceedings Volume 10138, 101380T (2017) https://doi.org/10.1117/12.2256181

KEYWORDS: 3D modeling, Computed tomography, 3D printing, Process modeling, Printing, Image resolution, Cones, Stereolithography, Data modeling, Imaging informatics, Bone, Fused deposition modeling, Image segmentation

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Proceedings Article | 29 March 2016 Paper

Automated reconstruction of standing posture panoramas from multi-sector long limb x-ray images

Linzey Miller, Caroline Trier, Yehuda Ben-Zikri, Cristian Linte

Proceedings Volume 9788, 97880B (2016) https://doi.org/10.1117/12.2217742

KEYWORDS: Image registration, Panoramic photography, Image segmentation, Image processing, Edge detection, Bone, Digital x-ray imaging, Image filtering, Binary data

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Proceedings Article | 18 March 2016 Paper

A robust automated left ventricle region of interest localization technique using a cardiac cine MRI atlas

Yehuda Kfir Ben-Zikri, Cristian Linte

Proceedings Volume 9786, 97862T (2016) https://doi.org/10.1117/12.2217885

KEYWORDS: Image segmentation, Magnetic resonance imaging, Image registration, Medical imaging, Image analysis, Image processing, Computer aided diagnosis and therapy, Visualization, Image enhancement, Heart, Image quality, Expectation maximization algorithms

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Region of interest detection is a precursor to many medical image processing and analysis applications, including segmentation, registration and other image manipulation techniques. The optimal region of interest is often selected manually, based on empirical knowledge and features of the image dataset. However, if inconsistently identified, the selected region of interest may greatly affect the subsequent image analysis or interpretation steps, in turn leading to incomplete assessment during computer-aided diagnosis or incomplete visualization or identification of the surgical targets, if employed in the context of pre-procedural planning or image-guided interventions. Therefore, the need for robust, accurate and computationally efficient region of interest localization techniques is prevalent in many modern computer-assisted diagnosis and therapy applications. Here we propose a fully automated, robust, a priori learning-based approach that provides reliable estimates of the left and right ventricle features from cine cardiac MR images. The proposed approach leverages the temporal frame-to-frame motion extracted across a range of short axis left ventricle slice images with small training set generated from les than 10% of the population. This approach is based on histogram of oriented gradients features weighted by local intensities to first identify an initial region of interest depicting the left and right ventricles that exhibits the greatest extent of cardiac motion. This region is correlated with the homologous region that belongs to the training dataset that best matches the test image using feature vector correlation techniques. Lastly, the optimal left ventricle region of interest of the test image is identified based on the correlation of known ground truth segmentations associated with the training dataset deemed closest to the test image. The proposed approach was tested on a population of 100 patient datasets and was validated against the ground truth region of interest of the test images manually annotated by experts. This tool successfully identified a mask around the LV and RV and furthermore the minimal region of interest around the LV that fully enclosed the left ventricle from all testing datasets, yielding a 98% overlap with their corresponding ground truth. The achieved mean absolute distance error between the two contours that normalized by the radius of the ground truth is 0.20 ± 0.09.

Proceedings Article | 18 March 2016 Paper

A comparison study of atlas-based 3D cardiac MRI segmentation: global versus global and local transformations

Aditya Daryanani, Shusil Dangi, Yehuda Kfir Ben-Zikri, Cristian Linte

Proceedings Volume 9786, 978624 (2016) https://doi.org/10.1117/12.2217883

KEYWORDS: Image segmentation, Cardiovascular magnetic resonance imaging, Image registration, Heart, Electronic filtering, Statistical analysis, 3D modeling

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Proceedings Article | 18 March 2015 Paper

Endocardial left ventricle feature tracking and reconstruction from tri-plane trans-esophageal echocardiography data

Shusil Dangi, Yehuda Ben-Zikri, Nathan Cahill, Karl Schwarz, Cristian Linte

Proceedings Volume 9415, 941505 (2015) https://doi.org/10.1117/12.2082541

KEYWORDS: Image segmentation, 3D image processing, 3D acquisition, Image enhancement, Image acquisition, Image analysis, Echocardiography, Real time imaging, Image registration, Image processing

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Two-dimensional (2D) ultrasound (US) has been the clinical standard for over two decades for monitoring and assessing cardiac function and providing support via intra-operative visualization and guidance for minimally invasive cardiac interventions. Developments in three-dimensional (3D) image acquisition and transducer design and technology have revolutionized echocardiography imaging enabling both real-time 3D trans-esophageal and intra-cardiac image acquisition. However, in most cases the clinicians do not access the entire 3D image volume when analyzing the data, rather they focus on several key views that render the cardiac anatomy of interest during the US imaging exam. This approach enables image acquisition at a much higher spatial and temporal resolution. Two such common approaches are the bi-plane and tri-plane data acquisition protocols; as their name states, the former comprises two orthogonal image views, while the latter depicts the cardiac anatomy based on three co-axially intersecting views spaced at 60⁰to one another. Since cardiac anatomy is continuously changing, the intra-operative anatomy depicted using real-time US imaging also needs to be updated by tracking the key features of interest and endocardial left ventricle (LV) boundaries. Therefore, rapid automatic feature tracking in US images is critical for three reasons: 1) to perform cardiac function assessment; 2) to identify location of surgical targets for accurate tool to target navigation and on-target instrument positioning; and 3) to enable pre- to intra-op image registration as a means to fuse pre-op CT or MR images used during planning with intra-operative images for enhanced guidance. In this paper we utilize monogenic filtering, graph-cut based segmentation and robust spline smoothing in a combined work flow to process the acquired tri-plane TEE time series US images and demonstrate robust and accurate tracking of the LV endocardial features. We reconstruct the endocardial LV geometry using the tri-plane contours and spline interpolation, and assess the accuracy of the proposed work flow against gold-standard results from the GE Echopac PC clinical software according to quantitative clinical LV characterization parameters, such as the length, circumference, area and volume. Our proposed combined work flow leads to consistent, rapid and automated identification of the LV endocardium, suitable for intra-operative applications and "on-the-fly" computer-assisted assessment of ejection fraction for cardiac function monitoring.Two-dimensional (2D) ultrasound (US) has been the clinical standard for over two decades for monitoring and assessing cardiac function and providing support via intra-operative visualization and guidance for minimally invasive cardiac interventions. Developments in three-dimensional (3D) image acquisition and transducer design and technology have revolutionized echocardiography imaging enabling both real-time 3D trans-esophageal and intra-cardiac image acquisition. However, in most cases the clinicians do not access the entire 3D image volume when analyzing the data, rather they focus on several key views that render the cardiac anatomy of interest during the US imaging exam. This approach enables image acquisition at a much higher spatial and temporal resolution. Two such common approaches are the bi-plane and tri-plane data acquisition protocols; as their name states, the former comprises two orthogonal image views, while the latter depicts the cardiac anatomy based on three co-axially intersecting views spaced at 600 to one another. Since cardiac anatomy is continuously changing, the intra-operative anatomy depicted using real-time US imaging also needs to be updated by tracking the key features of interest and endocardial left ventricle (LV) boundaries. Therefore, rapid automatic feature tracking in US images is critical for three reasons: 1) to perform cardiac function assessment; 2) to identify location of surgical targets for accurate tool to target navigation and on-target instrument positioning; and 3) to enable pre- to intra-op image registration as a means to fuse pre-op CT or MR images used during planning with intra-operative images for enhanced guidance. In this paper we utilize monogenic filtering, graph-cut based segmentation and robust spline smoothing in a combined work flow to process the acquired tri-plane TEE time series US images and demonstrate robust and accurate tracking of the LV endocardial features. We reconstruct the endocardial LV geometry using the tri-plane contours and spline interpolation, and assess the accuracy of the proposed work flow against gold-standard results from the GE Echopac PC clinical software according to quantitative clinical LV characterization parameters, such as the length, circumference, area and volume. Our proposed combined work flow leads to consistent, rapid and automated identification of the LV endocardium, suitable for intra-operative applications and on-the- y" computer-assisted assessment of ejection fraction for cardiac function monitoring.

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