Ahmad Shalbaf

Iran University of Science and Technology, Tehrān, Ostan-e Tehran, Iran

Are you Ahmad Shalbaf?

Claim your profile

Publications (8)7.1 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: Purpose: Identification and assessment of left ventricular (LV) global and regional wall motion (RWM) abnormalities are essential for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is performed visually which is highly dependent on the training and experience of echocardiographers and thus is prone to considerable interobserver and intraobserver variability. This paper presents a new automatic method, based on nonlinear dimensionality reduction (NLDR) for global wall motion evaluation and also detection and classification of RWM abnormalities of LV wall in a three-point scale as follows: (1) normokinesia, (2) hypokinesia, and (3) akinesia.Methods: Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this paper, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on two-dimensional (2D) echocardiography images of one cycle of heart. By this approach, nonlinear information in these images is embedded in a 2D manifold and each image is characterized by a point on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes. Then, a new global and regional quantitative index from the resultant manifold is proposed for global wall motion estimation and also classification of RWM of LV wall in a three-point scale. Obtained results by our method are quantitatively evaluated to those obtained visually by two experienced echocardiographers as the reference (gold standard) on 10 healthy volunteers and 14 patients.Results: Linear regression analysis between the proposed global quantitative index and the global wall motion score index and also with LV ejection fraction obtained by reference experienced echocardiographers resulted in the correlation coefficients of 0.85 and 0.90, respectively. Comparison between the proposed automatic RWM scoring and the reference visual scoring resulted in an absolute agreement of 82% and a relative agreement of 97%.Conclusions: The proposed diagnostic method can be used as a useful tool as well as a reference visual assessment by experienced echocardiographers for global wall motion estimation and also classification of RWM abnormalities of LV wall in a three-point scale in clinical evaluations.
    Medical Physics 05/2013; 40(5):052904. · 2.91 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Identification and classification of left ventricular (LV) regional wall motion (RWM) abnormalities on echocardiograms has fundamental clinical importance for various cardiovascular disease assessments especially in ischemia. In clinical practice, this evaluation is still performed visually which is highly dependent on training and experience of the echocardiographers and therefore suffers from significant interobserver and intraobserver variability. This paper presents a new automatic technique, based on nonrigid image registration for classifying the RWM of LV in a three-point scale. In this algorithm, we register all images of one cycle of heart to a reference image (end-diastolic image) using a hierarchical parametric model. This model is based on an affine transformation for modeling the global LV motion and a B-spline free-form deformation transformation for modeling the local LV deformation. We consider image registration as a multiresolution optimization problem. Finally, a new regional quantitative index based on resultant parameters of the hierarchical transformation model is proposed for classifying RWM in a three-point scale. The results obtained by our method are quantitatively evaluated to those obtained by two experienced echocardiographers visually as gold standard on ten healthy volunteers and 14 patients (two apical views) and resulted in an absolute agreement of 83 % and a relative agreement of 99 %. Therefore, this diagnostic system can be used as a useful tool as well as reference visual assessment to classify RWM abnormalities in clinical evaluation.
    Journal of Digital Imaging 01/2013; · 1.10 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this paper, an automatic method for segmentation of the left ventricle in two-dimensional (2D) echocardiography images of one cardiac cycle is proposed. In the first step of this method, using a mean image of a sequence of echocardiography images and its statistical properties the approximate region of left ventricle (LV) is extracted. Then the coordinate of extracted rectangular (ROI) is applied on all frames of sequences automatically. The mean image extracted ROI is used for defining the initial contour by scanning from the center point in polar coordinate. In the next step, all the extracted ROIs from the frames are mapped in a 2D space using the nonlinear dimension reduction manifold learning method. Using the properties of the manifold map end diastole (ED) and end systole (ES) frames are determined. Segmentation of the frames begins from ES frame, utilizing the dynamic directional vector field convolution (DDVFC) level set method with the initial contour as mentioned above. Final contour of each segmented frame is used as the initial contour of the next frame. Maximum range of the active contour motion is limited by a percent of the Euclidean distance between the point corresponds the current frame and the previous one in the resultant manifold. The results obtained from our method are quantitatively evaluated to those obtained by the gold contours drawn by a cardiologist on 489 echocardiographic images of seven volunteers using four distance measures: Hausdorff distance, average distance, area difference and area coverage error. We have also compared our results with the results of applying only DDVFC method. Comparing the implementation of only the DDVFC method, the results show final contours by proposed method are more close to contours drawn by a cardiologist.
    Biomedical Engineering Applications Basis and Communications 01/2013; 25(02). · 0.23 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The aim of this study is to evaluate the efficiency of applying a new non-rigid image registration method on two-dimensional echocardiographic images for computing the left ventricle (LV) myocardial motion field over a cardiac cycle. The key feature of our method is to register all images in the sequence to a reference image (end-diastole image) using a hierarchical transformation model, which is a combination of an affine transformation for modeling the global LV motion and a free-form deformation (FFD) transformation based on B-splines for modeling the local LV deformation. Registration is done by minimizing a cost function associated with the image similarity based on a global pixel-based matching and the smoothness of transformation. The algorithm uses a fast and robust optimization strategy using a multiresolution approach for the estimation of parameters of the deformation model. The proposed algorithm is evaluated for calculating the displacement curves of two expert-identified anatomical landmarks in apical views of the LV for 10 healthy volunteers and 14 subjects with pathology. The proposed algorithm is also evaluated for classifying the regional LV wall motion abnormality using the calculation of the strain value at the end of systole in 288 segments as scored by two consensual experienced echocardiographers in a three-point scale: 1: normokinesia, 2: hypokinesia, and 3: akinesia. Moreover, we compared the results of the proposed registration algorithm to those previously obtained using the other image registration methods. Regarding to the reference two experienced echocardiographers, the results demonstrate the proposed algorithm more accurately estimates the displacement curve of the two anatomical landmarks in apical views than the other registration methods in all data set. Moreover, the p values of the t test for the strain value of each segment at the end of systole measured by the proposed algorithm show higher differences than the other registration method. These differences are between each pair of scores in all segments and in three segments of septum independently. The clinical results show that the proposed algorithm can improve both the calculation of the displacement curve of every point of LV during a cardiac cycle and the classification of regional LV wall motion abnormality. Therefore, this diagnostic system can be used as a useful tool for clinical evaluation of the regional LV function.
    International Journal of Computer Assisted Radiology and Surgery 07/2012; 7(5):769-83. · 1.36 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Echocardiographic images have considerable noises (Especially speckle noise) because of their inherent nature and do not have desirable quality which makes difficult to analyze them. Therefore, it is essential to run pre-processing to reduce noises before their interpretation and analysis. In this paper, we have used Contourlet method to reduce the noise of echocardiographic images. In order to evaluate and compare the proposed method with some common de noising methods, three different criteria (mean square error (MSE), peak signal- to-noise ratio and signal to mean square error) are used. The results showed that the proposed method is much better than the other methods. Moreover, according to expert echo cardiologist opinion, we have achieved maximum resolution other common de noising methods. Keywords-echocardiography images; Contourlet; Speckle noise
    01/2011;
  • [Show abstract] [Hide abstract]
    ABSTRACT: Medical applications of ultrasound imaging have expanded enormously over the last two decades. De-noising is challenging issues for better medical interpretation and diagnosis on high volume of data sets in echocardiography. In this paper, manifold learning algorithm is applied on 2-D echocardiography images to discover the relationship between the frames of consecutive cycles of the heart motion. By this approach, each image is depicted by a point on reconstructed two-dimensional manifold by Isomap algorithm and similar points related to similar images according to the property of periodic heartbeat cycle stand together. Noise reduction is achieved by averaging similar images on reconstructed manifold. By comparing the proposed method with some common methods and according to qualitative expert's opinions, the proposed method has maximum noise reduction, minimum blurring and better contrast among the similar methods.
    01/2011;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The first step for automatic calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities in echocardiographic images is automatic detection of the end-systole and end-diastole frames. In this paper, modified Isomap algorithm is applied on two dimensional (2-D) echocardiographic images to reveal the relationship between the frames of one cycle of heart motion. By this approach, the image sequences are represented in a 2-D manifold and each image is characterized by a point on reconstructed manifold. By considering the fact that end-diastolic and the end-systolic frames have the highest volume difference and consequently highest image difference comparing to the other two frames, the maximum distance between the two points in manifold is used to find these frames. The results obtained with our method were validated to those obtained with the reference experienced echo-cardiologist on six healthy volunteers and depicted the usefulness of presented method.
    01/2011;
  • [Show abstract] [Hide abstract]
    ABSTRACT: The automatic detection of end-diastole and end-systole frames of echocardiography images is the first step for calculation of the ejection fraction, stroke volume and some other features related to heart motion abnormalities. In this paper, the manifold learning algorithm is applied on 2D echocardiography images to find out the relationship between the frames of one cycle of heart motion. By this approach the nonlinear embedded information in sequential images is represented in a two-dimensional manifold by the LLE algorithm and each image is depicted by a point on reconstructed manifold. There are three dense regions on the manifold which correspond to the three phases of cardiac cycle ('isovolumetric contraction', 'isovolumetric relaxation', 'reduced filling'), wherein there is no prominent change in ventricular volume. By the fact that the end-systolic and end-diastolic frames are in isovolumic phases of the cardiac cycle, the dense regions can be used to find these frames. By calculating the distance between consecutive points in the manifold, the isovolumic frames are mapped on the three minimums of the distance diagrams which were used to select the corresponding images. The minimum correlation between these images leads to detection of end-systole and end-diastole frames. The results on six healthy volunteers have been validated by an experienced echo cardiologist and depict the usefulness of the presented method.
    Physiological Measurement 09/2010; 31(9):1091-103. · 1.50 Impact Factor