Ian Ross

The University of Western Ontario, London, Ontario, Canada

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Publications (22)7.93 Total impact

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    Shuo Li, Ian Ross, Richard Rankin
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    ABSTRACT: Example methods and apparatus to process left-ventricle cardiac images are disclosed. A disclosed example method includes identifying a first landmark point in a first cardiac image, identifying a first centroid of a left ventricle depicted in the first cardiac image, and performing a Cartesian-to-polar transformation to form a first rectangular representation of the left ventricle depicted in the first cardiac image based on the first landmark point and the first centroid.
    Ref. No: U.S. Patent 8,229,192, Year: 07/2012
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    ABSTRACT: Image tracking as described herein can include: segmenting a first image into regions; determining an overlap of intensity distributions in the regions of the first image; and segmenting a second image into regions such that an overlap of intensity distributions in the regions of the second image is substantially similar to the overlap of intensity distributions in the regions of the first image. In certain embodiments, images can depict a heart at different points in time and the tracked regions can be the left ventricle cavity and the myocardium. In such embodiments, segmenting the second image can include generating first and second curves that track the endocardium and epicardium boundaries, and the curves can be generated by minimizing functions containing a coefficient based on the determined overlap of intensity distributions in the regions of the first image.
    Ref. No: U.S. Patent 8,144,930, Year: 03/2012
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    ABSTRACT: Obliterating the left atrial appendage from systemic circulation in patients with atrial fibrillation has been proposed to reduce thromboembolic events. The goal of this study was to assess the effectiveness of a circular method of epicardial surgical ligation in obliterating the left atrial appendage and maintaining sustained exclusion. Patients with permanent atrial fibrillation and an indication for elective cardiac surgery were enrolled. All patients underwent preoperative cardiac gated computerized tomography (CT) and transesophageal echocardiography (TEE). During the cardiac procedure circular ligation of the appendage was performed. Twelve patients, mean (SD) age 65 (12) years completed the study. Intraoperative TEE demonstrated all patients (12/12) had complete postligation occlusion of the left atrial appendage. At three-month follow-up, cardiac gated CT demonstrated that 75% (9/12) of the patients had communication of contrast dye from the left atrial appendage to body of left atrium. Left atrial appendage orifice area and volume were reduced from mean (SD) (5.5 cm(2) [1.8] to 0.5 cm(2) [0.4] p = 0.002) and (14.0 cm(3) [8.3] to 2.7 cm(3) [1.3] p = .005) postligation, respectively. No clinically significant thromboembolic events were reported. Epicardial suture ligation of the left atrial appendage resulted in successful intra-operative exclusion on TEE; however, a significant portion of patient's demonstrated communication of contrast on CT. This is suggestive of incomplete long-term exclusion. The clinical significance of reduction in left atrial appendage orifice area and volume with a persistent communication requires further study.
    Journal of Cardiac Surgery 03/2012; 27(2):270-3. · 1.35 Impact Factor
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    ABSTRACT: Certain embodiments of the present technology provide systems, methods and computer instructions for computer aided analysis of images. In certain embodiments, for example, such a method includes: isolating a motion area in an image; segmenting the image; utilizing a support vector machine to identify a region of interest in the image; utilizing a graph-cut algorithm to refine the region of interest; and verifying the region of interest. In certain embodiments, for example, such a method further includes: aligning a set of images and/or outputting a set of aligned images sequentially. In certain embodiments, the systems, methods and computer instructions disclosed herein can be used to aid analysis of cardiac images, for example. In certain embodiments, the systems, methods and computer instructions disclosed herein can be used to aid analysis of four dimensional images, for example.
    Ref. No: U.S. Patent 8,121,364, Year: 02/2012
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    ABSTRACT: The Cardiac Ejection Fraction (EF) is an essential criterion in cardiovascular disease prognosis. In clinical routine, EF is often computed from manually or automatically segmenting the Left Ventricle (LV) in End-dyastole and Endsystole frames, which is prohibitively time consuming and needs user interactions. In this paper, we propose a method to minimize user effort and estimate the EF directly from image statistics via machine-learning techniques, without the need for comprehensive segmentations of all the MRI images in a subject dataset. From a user-provided segmentation of a single image, we build a statistic based on the Bhattacharyya coefficient of similarity between image distributions for each of the images in a subject dataset (200 images). We demonstrate that these statistical features are non-linearly related to the LV cavity areas and therefore can be used to estimate the EF. We used Principal Component Analysis (PCA) to reduce the dimensionality of the features and areas. Then, an Artificial Neural Network (ANN) was used to predict the LV cavity areas from the dimension-reduced features. The EF is finally estimated from the obtained areas.
    Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging 01/2012;
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    ABSTRACT: This study investigates fast detection of the left ventricle (LV) endo- and epicardium boundaries in a cardiac magnetic resonance (MR) sequence following the optimization of two original discrete cost functions, each containing global intensity and geometry constraints based on the Bhattacharyya similarity. The cost functions and the corresponding max-flow optimization built upon an original bound of the Bhattacharyya measure yield competitive results in nearly real-time. Within each frame, the algorithm seeks the LV cavity and myocardium regions consistent with subject-specific model distributions learned from the first frame in the sequence. Based on global rather than pixel-wise information, the proposed formulation relaxes the need of a large training set and optimization with respect to geometric transformations. Different from related active contour methods, it does not require a large number of iterative updates of the segmentation and the corresponding computationally onerous kernel density estimates (KDEs). The algorithm requires very few iterations and KDEs to converge. Furthermore, the proposed bound can be used for several other applications and, therefore, can lead to segmentation algorithms which share the flexibility of active contours and computational advantages of max-flow optimization. Quantitative evaluations over 2280 images acquired from 20 subjects demonstrated that the results correlate well with independent manual segmentations by an expert. Moreover, comparisons with a related recent active contour method showed that the proposed framework brings significant improvements in regard to accuracy and computational efficiency.
    Medical image analysis 05/2011; 16(1):87-100. · 3.09 Impact Factor
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    ABSTRACT: Example methods, apparatus and articles of manufacture to process cardiac images to detect heart motion abnormalities are disclosed. A disclosed example method includes adapting a state of a state-space model based on a plurality of cardiac images to characterize motion of a heart, computing an information-theoretic metric from the state of the state-space model, and comparing the information-theoretic metric to a threshold to determine whether the motion of the heart is abnormal.
    Ref. No: U.S. Patent 20110064284, Year: 03/2011
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    ABSTRACT: Example methods, apparatus and articles of manufacture to track endocardial motion are disclosed. A disclosed example method includes segmenting a plurality of cardiac images of a left ventricle to form respective ones of a plurality of segmented images, updating a plurality of models based on the plurality of segmented images to form respective ones of a plurality of motion estimates for the left ventricle, computing a plurality of probabilities for respective ones of the plurality of models, and computing a weighted sum of the plurality of motion estimates based on the plurality of probabilities, the weighted sum representing a predicted motion of the left ventricle.
    Ref. No: U.S. Patent 20110064290, Year: 03/2011
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    ABSTRACT: This study investigates a novel method of tracking Left Ventricle (LV) curve in Magnetic Resonance (MR) sequences. The method focuses on energy minimization by level-set curve boundary evolution. The level-set framework allows introducing knowledge of the field prior on the solution. The segmentation in each particular time relies not only on the current image but also the segmented image from previous phase. Field prior is defined based on the experimental fact that the mean logarithm of intensity inside endo and epi-cardium is approximately constant during a cardiac cycle. The solution is obtained by evolving two curves following the Euler-Lagrange minimization of a functional containing a field constraint. The functional measures the consistency of the field prior over a cardiac sequence. Our preliminary results show that the obtained segmentations are very well correlated with those manually obtained by experts. Furthermore, we observed that the proposed field prior speeds up curve evolution significantly and reduces the computation load.
    Proc SPIE 03/2011;
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    ABSTRACT: Factor VII has been utilized to treat post-operative bleeding after cardiac surgery refractory to other intervention. We report the case of a patient who developed intractable bleeding after a severe protamine reaction following emergency repair of type A aortic dissection and was successfully treated with factor VII.
    Innovations Technology and Techniques in Cardiothoracic and Vascular Surgery 01/2011; 6(1):48-50.
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    ABSTRACT: Early and accurate detection of Left Ventricle (LV) regional wall motion abnormalities significantly helps in the diagnosis and followup of cardiovascular diseases. We present a regional myocardial abnormality detection framework based on image statistics. The proposed framework requires a minimal user interaction, only to specify initial delineation and anatomical landmarks on the first frame. Then, approximations of regional myocardial segments in subsequent frames were systematically obtained by superimposing the initial delineation on the rest of the frames. The proposed method exploits the Bhattacharyya coefficient to measure the similarity between the image distribution within each segment approximation and the distribution of the corresponding user-provided segment. Linear Discriminate Analysis (LDA) is applied to find the optimal direction along which the projected features are the most descriptive. Then a Linear Support Vector Machine (SVM) classifier is employed for each of the regional myocardial segments to automatically detect abnormally contracting regions of the myocardium. Based on a clinical dataset of 30 subjects, the evaluation demonstrates that the proposed method can be used as a promising diagnostic support tool to assist clinicians.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2011; 14(Pt 3):107-14.
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    ABSTRACT: Robotics-assisted endoscopic atraumatic coronary artery bypass has been shown to be effective in reducing surgical morbidity and length of hospital stay. Unfortunately, the criteria for selecting eligible patients for this procedure are still primitive. This has motivated the use of preoperative computed tomography scans to establish patient eligibility. The objective of this study is to establish which image measurements can be correlated to procedure success. A retrospective study was performed in 144 patients who underwent robotics-assisted coronary bypass surgery. After an initial set of 55 patients, preoperative computed tomography scans of the other patients were used to obtain patient specific measurements: the lateral distance between the midline of the sternum to the left anterior descending coronary artery and its depth from the skin surface, anteroposterior diameter of the thoracic cavity, and the transverse diameter of the thoracic cavity. The procedures were rated as successful if completed in a minimally invasive manner. Different combinations of the variables were evaluated and correlated with success. A strong correlation was found between success rate and the ratio of the lateral distance to the transverse diameter in the female patients only (0.532, P = 0.006). A ratio of less than 0.20 significantly increased the occurrence of conversion during this procedure in female cases. The lateral distance of the left anterior descending coronary artery from the midline divided by the transverse thoracic width of a female patient shows a significant correlation with procedure success. No significant correlations were found for male patients.
    Innovations Technology and Techniques in Cardiothoracic and Vascular Surgery 09/2010; 5(5):335-40.
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    ABSTRACT: This study investigates active curve image segmentation with a statistical overlap constraint, which biases the overlap between the nonparametric (kernel-based) distributions of image data within the segmentation regions a foreground and a background-to a statistical description learned a priori. We model the overlap, measured via the Bhattacharyya coefficient, with a Gaussian prior whose parameters are estimated from a set of relevant training images. This can be viewed as a generalization of current intensity-driven constraints for difficult situations where a significant overlap exists between the distributions of the segmentation regions. We propose to minimize a functional containing the overlap constraint and classic regularization terms, compute the corresponding Euler-Lagrange curve evolution equation, and give a simple interpretation of how the statistical overlap constraint influences such evolution. A representative number of statistical, quantitative, and comparative experiments with Magnetic Resonance (MR) cardiac images and Computed Tomography (CT) liver images demonstrate the desirable properties of the statistical overlap constraint. First, it outperforms significantly the likelihood prior commonly used in level set segmentation. Second, it is easy-to-learn; we demonstrate experimentally that the Gaussian assumption is sufficient for cardiac images. Third, it can relax the need of both complex geometric training and accurate learning of the background distribution, thereby allowing more flexibility in clinical use.
    Information processing in medical imaging: proceedings of the ... conference 02/2009; 21:589-601.
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    ABSTRACT: The goal of this study is to investigate automatic myocardium tracking in cardiac Magnetic Resonance (MR) sequences using global distribution matching via level-set curve evolution. Rather than relying on the pixelwise information as in existing approaches, distribution matching compares intensity distributions, and consequently, is well-suited to the myocardium tracking problem. Starting from a manual segmentation of the first frame, two curves are evolved in order to recover the endocardium (inner myocardium boundary) and the epicardium (outer myocardium boundary) in all the frames. For each curve, the evolution equation is sought following the maximization of a functional containing two terms: (1) a distribution matching term measuring the similarity between the non-parametric intensity distributions sampled from inside and outside the curve to the model distributions of the corresponding regions estimated from the previous frame; (2) a gradient term for smoothing the curve and biasing it toward high gradient of intensity. The Bhattacharyya coefficient is used as a similarity measure between distributions. The functional maximization is obtained by the Euler-Lagrange ascent equation of curve evolution, and efficiently implemented via level-set. The performance of the proposed distribution matching was quantitatively evaluated by comparisons with independent manual segmentations approved by an experienced cardiologist. The method was applied to ten 2D mid-cavity MR sequences corresponding to ten different subjects. Although neither shape prior knowledge nor curve coupling were used, quantitative evaluation demonstrated that the results were consistent with manual segmentations. The proposed method compares well with existing methods. The algorithm also yields a satisfying reproducibility. Distribution matching leads to a myocardium tracking which is more flexible and applicable than existing methods because the algorithm uses only the current data, i.e., does not require a training, and consequently, the solution is not bounded to some shape/intensity prior information learned from of a finite training set.
    International Journal of Computer Assisted Radiology and Surgery 01/2009; 4(1):37-44. · 1.36 Impact Factor
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    ABSTRACT: This study investigates heart wall motion abnormality detection with an information theoretic measure of heart motion based on the Shannon's differential entropy (SDE) and recursive Bayesian filtering. Heart wall motion is generally analyzed using functional images which are subject to noise and segmentation inaccuracies, and incorporation of prior knowledge is crucial in improving the accuracy. The Kalman filter, a well known recursive Bayesian filter, is used in this study to estimate the left ventricular (LV) cavity points given incomplete and noisy data, and given a dynamic model. However, due to similarities between the statistical information of normal and abnormal heart motions, detecting and classifying abnormality is a challenging problem which we proposed to investigate with a global measure based on the SDE. We further derive two other possible information theoretic abnormality detection criteria, one is based on Rényi entropy and the other on Fisher information. The proposed method analyzes wall motion quantitatively by constructing distributions of the normalized radial distance estimates of the LV cavity. Using 269 x 20 segmented LV cavities of short-axis magnetic resonance images obtained from 30 subjects, the experimental analysis demonstrates that the proposed SDE criterion can lead to significant improvement over other features that are prevalent in the literature related to the LV cavity, namely, mean radial displacement and mean radial velocity.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 01/2009; 12(Pt 2):373-80.
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    I. Ben Ayed, Shuo Li, I. Ross
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    ABSTRACT: Recent studies have shown that embedding similarity/dissimilarity measures between distributions in the variational level set framework can lead to effective object segmentation/tracking algorithms. In this connection, existing methods assume implicitly that the overlap between the distributions of image data within the object and its background has to be minimal. Unfortunately, such assumption may not be valid in many important applications. This study investigates an overlap prior, which embeds knowledge about the overlap between the distributions of the object and the background in level set tracking. It consists of evolving a curve to delineate the target object in the current frame. The level set curve evolution equation is sought following the maximization of a functional containing three terms: (1) an original overlap prior which measures the conformity of overlap between the nonparametric (kernel-based) distributions within the object and the background to a learned description, (2) a term which measures the similarity between a model distribution of the object and the sample distribution inside the curve, and (3) a regularization term for smooth segmentation boundaries. The Bhattacharyya coefficient is used as an overlap measure. Apart from leading to a method which is more versatile than current ones, the overlap prior speeds up significantly the curve evolution. Comparisons and results demonstrate the advantages of the proposed prior over related methods, and its usefulness in important applications such as the left ventricle tracking in magnetic resonance (MR) images.
    Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on; 07/2008
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    ABSTRACT: This study investigates overlap priors for tracking the Left Ventricle (LV) endo- and epicardium boundaries in cardiac Magnetic Resonance (MR) sequences. It consists of evolving two curves following the Euler-Lagrange minimization of two functionals each containing an original overlap prior constraint. The latter measures the conformity of the overlap between the nonparametric (kernel-based) intensity distributions within the three target regions-LV cavity, myocardium and background-to a prior learned from a given segmentation of the first frame. The Bhattacharyya coefficient is used as an overlap measure. Different from existing intensity-driven constraints, the overlap priors do not assume implicitly that the overlap between the distributions within different regions has to be minimal. Although neither shape priors nor curve coupling were used, quantitative evaluation showed that the results correlate well with independent manual segmentations and the method compares favorably with other recent methods. The overlap priors lead to a LV tracking which is more versatile than existing methods because the solution is not bounded to the shape/intensity characteristics of a training set. We also demonstrate experimentally that the used overlap measures are approximately constant over a cardiac sequence.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2008; 11(Pt 1):1025-33.
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    ABSTRACT: This study investigates overlap priors for tracking the Left Ventricle (LV) endo- and epicardium boundaries in cardiac Magnetic Resonance (MR) sequences. It consists of evolving two curves following the Euler-Lagrange minimization of two functionals each containing an original overlap prior constraint. The latter measures the conformity of the overlap between the nonparametric (kernel-based) intensity distributions within the three target regions-LV cavity, myocardium and background-to a prior learned from a given segmentation of the first frame. The Bhattacharyya coefficient is used as an overlap measure. Different from existing intensity-driven constraints, the overlap priors do not assume implicitly that the overlap between the distributions within different regions has to be minimal. Although neither shape priors nor curve coupling were used, quantitative evaluation showed that the results correlate well with independent manual segmentations and the method compares favorably with other recent methods. The overlap priors lead to a LV tracking which is more versatile than existing methods because the solution is not bounded to the shape/intensity characteristics of a training set. We also demonstrate experimentally that the used overlap measures are approximately constant over a cardiac sequence.
    Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. 02/2008; 11(Pt 1):1025-33.
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    ABSTRACT: Proper placement of ports during robot-assisted endoscopic surgery is critical to the success of the procedure. In current practice, port placement methods do not consider the ability of the robot to manoeuvre the tools. This paper proposes to choose the best port location such that the performance of the robot is maximized. The Global Conditioning Index (GCI) is used to optimize port placement when using the da Vinci((R)) surgical system during cardiac surgery. The results show that, due to a singularity at the remote centre of motion, higher performance is obtained the further away the port is from the workspace. When compared to the ports selected by an expert surgeon, our results show that it is possible to increase robot performance by at least 29% for the left arm of the robot. Selecting an adequate port location can improve robot performance and ensure that the instruments reach the surgical site.
    International Journal of Medical Robotics and Computer Assisted Surgery 01/2008; 3(4):355-64. · 1.49 Impact Factor
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    ABSTRACT: To facilitate the clinical evaluation on cardiac functions on dynamic images, a fast and robust full-automated clinical computer aided analysis and navigation system is proposed. With recent proposed graph cut method, we are able to achieve an automatic segmentation of ventricles. And with the segmented ventricles, we are able to align and navigated the series of dynamic cardiac images. The system is tested with different series of dynamic MR images. Promising results are demonstrated and analyzed.
    Proceedings of the 21the International Congress and Exhibition, Berlin, Germany; 06/2007

Publication Stats

41 Citations
7.93 Total Impact Points

Institutions

  • 2008–2012
    • The University of Western Ontario
      • Division of Cardiac Surgery
      London, Ontario, Canada
    • GE Healthcare
      Little Chalfont, England, United Kingdom
  • 2011
    • GE India Industrial Pvt. Ltd.
      New Dilli, NCT, India
  • 2008–2011
    • London Health Sciences Centre
      London, Ontario, Canada
  • 2010
    • Lawson Health Research Institute
      London, Ontario, Canada