Rangaraj M. Rangayyan

The University of Calgary, Calgary, Alberta, Canada

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Publications (301)286.57 Total impact

  • Faraz Oloumi, Rangaraj M Rangayyan, Anna L Ells
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    ABSTRACT: Purpose: To test the hypothesis that the openness of the major temporal arcade (MTA) changes in the presence of plus disease, by quantification via parabolic modeling of the MTA, as well as measurement of an arcade angle for comparative analysis. Such analysis could assist in the detection and treatment of progressive retinopathy of prematurity (ROP). Methods: Digital image processing techniques were applied for the detection and modeling of the MTA via a graphical user interface (GUI) to quantify the openness of the MTA. An arcade angle measure, based on a previously proposed method, was also obtained via the GUI for comparative analysis. The statistical significance of the differences between the plus cases and the no-plus cases for each parameter was analyzed using the p-value. The area (Az) under the receiver operating characteristic curve was used to assess the diagnostic performance of each feature. Results: The openness of the MTA represented by the temporal arcade angle measure and the openness parameter of the parabolic model were used to perform discrimination of plus versus no-plus cases. Using a set of 19 cases with plus disease and 91 cases with no plus disease, Az = 0.70 was obtained using the results of dual-parabolic modeling in screening for plus disease. The arcade angle measure provided comparable results with Az = 0.73. Conclusions: Using our proposed image analysis techniques and software, this study demonstrates, for the first time, that the openness of the MTA decreases in the presence of plus disease.
    Investigative Ophthalmology &amp Visual Science 08/2014; 55(10). DOI:10.1167/iovs.13-13640 · 3.66 Impact Factor
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    ABSTRACT: We present a comprehensive and fully automated system for computer-aided detection and diagnosis of masses in mammograms. Novel methods for detection include: selection of suspicious focal areas based on analysis of the gradient vector field, rejection of oriented components of breast tissue using multidirectional Gabor filtering, and use of differential features for rejection of false positives (FPs) via clustering of the surrounding fibroglandular tissue. The diagnosis step is based on extraction of contour-independent features for characterization of lesions as benign or malignant from automatically detected circular and annular regions. A new unified 3D free-response receiver operating characteristic framework is introduced for global analysis of two binary categorization problems in cascade. In total, 3,080 suspicious focal areas were extracted from a set of 156 full-field digital mammograms, including 26 malignant tumors, 120 benign lesions, and 18 normal mammograms. The proposed system detected and diagnosed malignant tumors with a sensitivity of 0.96, 0.92, and 0.88 at, respectively, 1.83, 0.46, and 0.45 FPs/image, with two stages of stepwise logistic regression for selection of features, a cascade of Fisher linear discriminant analysis and an artificial neural network with radial basis functions, and leave-one-patient-out cross-validation.
  • Faraz Oloumi, Rangaraj M Rangayyan, Anna L Ells
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    ABSTRACT: Diagnosis of plus disease is crucial for timely treatment and management of retinopathy of prematurity. An indicator of the presence of plus disease is an increase in the tortuosity of blood vessels in the retina. In this work, we propose a new angle-variation-based measure for quantification of tortuosity in retinal fundus images of preterm infants. The methods include the use of Gabor filters to detect vessels as well as to obtain their orientation at each pixel. Morphological image processing methods are used to obtain a skeleton image of the vessels for measurement of tortuosity. Out of 11 vessel segments, marked by an expert ophthalmologist as showing high levels of tortuosity due to plus disease, all were correctly identified using the proposed methods.
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    ABSTRACT: The purpose of this article is to propose the use of fractal and texture analysis for computer-aided diagnosis (CAD) of diffuse pulmonary diseases (DPDs) in high-resolution computed tomography (HRCT) images. We propose multiple techniques to extract features from preprocessed regions of interest (ROIs) selected to represent five radiographic patterns useful in the differential diagnosis of DPDs, as well as normal cases. First-order statistics of gray-level distribution, Haralick's and Laws' texture features, statistical information extracted from the ROIs' discrete Fourier transforms, and their fractal dimension values were used as attributes. The features were used as inputs for a k-nearest neighbor classifier (k=5). With a dataset of 3252 ROIs, correct classification rates of up to 82.62% were achieved.
    2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA); 06/2014
  • Faraz Oloumi, Rangaraj M. Rangayyan, Anna L. Ells
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    ABSTRACT: Changes in retinal vessel width can be indicative of the presence of several diseases, such as diabetic retinopathy, retinopathy of prematurity (ROP), and hypertension. Accurate detection and measurement of such changes could help in computer-aided diagnosis. An increase in venular thickness is a sign of plus disease, which warrants treatment of ROP. We present image processing methods for detection, tracking, and measurement of the width of the major temporal arcade (MTA), which is the thickest branch of the venular vessels, in retinal fundus images of preterm infants. The methods include the use of Gabor filters for the detection of the blood vessels, as well as morphological image processing for tracking and measurement of the width of the MTA. The results indicate a statistically significant difference in vessel width of normal cases as compared to cases diagnosed with plus disease (p = 0.015).
    2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA); 06/2014
  • Edited by E. Y. K. Ng, U. Rajendra Acharya, Rangaraj M. Rangayyan, Jasjit S. Suri, 05/2014; CRC Press., ISBN: 978-1466559134
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    ABSTRACT: This article describes the development of a quantitative method for computer-aided diagnosis (CAD) of intervertebral disc degeneration according to Pfirrmann's scale, a semiquantitative scale with five degrees of degeneration, in T2-weighted magnetic resonance images of the lumbar spine. The dataset consists of images of 210 discs obtained from 42 healthy individuals. The intervertebral discs were assigned Pfirrmann's grades based on independent and blind classification. Binary masks of manually segmented discs were used to compute the centroids of the regions, estimate the curvature of the spine by polynomial fitting, normalize intensities, and extract regions of interest. Texture analysis was performed using Haralick's features and moments were computed for each disc. Classification was performed using an artificial neural network using the full vectors of attributes as well as a reduced set obtained using gradient ascent search. An average true-positive rate of 75.2% and an average area under the receiver operating characteristic curve of 0.78 indicate potential application of this technique for CAD of spinal pathology.
    5th IEEE Biosignals and Biorobotics conference (BRC 2014); 05/2014
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    ABSTRACT: Segmentation of the breast region is a fundamental step in any system for computerized analysis of mammograms. In this work, we propose a novel procedure for the estimation of the breast skin-line based upon multidirectional Gabor filtering. The method includes an adaptive values-of-interest (VOI) transformation, extraction of the skin-air ribbon by Otsu's thresholding method and the Euclidean distance transform, Gabor filtering with 18 real kernels, and a step for suppression of false edge points using the magnitude and phase responses of the filters. On a test set of 361 images from different acquisition modalities (screen-film and full-field digital mammograms), the average Hausdorff and polyline distances obtained were 2.85mm and 0.84mm, respectively, with reference to the ground-truth boundaries provided by an expert radiologist. When compared with the results obtained by other state-of-the-art methods on the same set of images and with respect to the same ground-truth boundaries, our method mostly outperformed the other approaches. The results demonstrate the effectiveness and robustness of the proposed algorithm.
    Computers in biology and medicine 11/2013; 43(11):1870-81. DOI:10.1016/j.compbiomed.2013.09.001 · 1.48 Impact Factor
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    ABSTRACT: In this paper, a novel approach for classification of breast masses is presented that quantifies the texture of masses without relying on accurate extraction of their contours. Two novel feature descriptors based on 2D extensions of the reverse arrangement (RA) and Mantel's tests were designed for this purpose. Measures of radial correlation and radial trend were extracted from the original gray-scale values as well as from the Gabor magnitude response of 146 regions of interest, including 120 benign masses and 26 malignant tumors. Four classifiers, Fisher-linear discriminant analysis, Bayesian, support vector machine, and an artificial neural network based on radial basis functions (ANN-RBF), were employed to predict the diagnosis, using stepwise logistic regression for feature selection and the leave-one-patient-out method for cross-validation. The ANN-RBF resulted in an area under the receiver operating characteristic curve of 0.93. The experimental results show the effectiveness of the proposed approach.
    Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 07/2013; 2013:6490-6493. DOI:10.1109/EMBC.2013.6611041
  • K.Y. Liu, M. R. Smith, E. C. Fear, R. M. Rangayyan
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    ABSTRACT: Concerns about the specificity and reliability of artificial neural networks (ANNs) impede further application of ANNs in medicine. This is particularly true when developing computer-aided diagnosis (CAD) tools using ANNs for orphan diseases and emerging research areas where only a small-sized sample set is available. It is unreasonable to claim one ANN's performance as better than another simply on the basis of a single output without considering possible output variability due to factors including data noise and ANN training protocols. In this paper, a bootstrap resampling method is proposed to quantitatively analyze ANN output reliability and changing performance as the sample data and training protocols are varied. The method is tested in the area of feature classification for analysis of masses detected on mammograms. Our experiments show that ANNs performance, measured in terms of the area under the receiver operating characteristic (ROC) curve, is not a fixed value, but follows a distribution function sensitive to many factors. We demonstrate that our approach to determining the bootstrap estimates of confidence intervals (CIs) and prediction intervals (PIs) can be used to assure optimal performance in terms of ANN model configuration. We also show that the unintentional inclusion of data noise, which biases ANN results in small task-specific databases, can be accurately detected via the bootstrap estimates.
    Biomedical Signal Processing and Control 05/2013; 8(3):255–262. DOI:10.1016/j.bspc.2012.11.001 · 1.53 Impact Factor
  • Faraz Oloumi, Rangaraj M Rangayyan, Anna L Ells
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    ABSTRACT: Monitoring the openness of the major temporal arcade (MTA) and how it changes over time could facilitate diagnosis and treatment of proliferative diabetic retinopathy (PDR). We present methods for user-guided semiautomated modeling and measurement of the openness of the MTA based on Gabor filters for the detection of retinal vessels, morphological image processing, and a form of the generalized Hough transform for the detection of parabolas. The methods, implemented via a graphical user interface, were tested with retinal fundus images of 11 normal individuals and 11 patients with PDR in the present pilot study on potential clinical application. A method of arcade angle measurement was used for comparative analysis. The results using the openness parameters of single- and dual-parabolic models as well as the arcade angle measurements indicate areas under the receiver operating characteristics of A z = 0.87, 0.82, and 0.80, respectively. The proposed methods are expected to facilitate quantitative analysis of the architecture of the MTA, as well as assist in detection and diagnosis of PDR.
    Journal of Digital Imaging 04/2013; 26(6). DOI:10.1007/s10278-013-9592-9 · 1.20 Impact Factor
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    ABSTRACT: Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs). The method includes detection of a plausible nipple/retroareolar area in a mammogram using geometrical constraints, analysis of the gradient vector field by mean and Gaussian curvature measurements, and local shape-based conditions. The proposed procedure was tested on 566 mammographic images consisting of 372 randomly selected scanned films from two public databases (mini-MIAS and DDSM), and 194 digital mammograms acquired with a GE Senographe 2000D FFDM system. A radiologist independently marked the centers of the nipples for evaluation of the results. The average error obtained was 6.7 mm (22 pixels) with reference to the center of the nipple as identified by the radiologist. Only two out of the 566 detected nipples (0.35 %) had an error larger than 50 mm. The method was also directly compared with two other techniques for the detection of the nipple. The results indicate that the proposed method outperforms other algorithms presented in the literature and can be used to identify accurately the nipple on various types of mammographic images.
    Journal of Digital Imaging 03/2013; 26(5). DOI:10.1007/s10278-013-9587-6 · 1.20 Impact Factor
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    ABSTRACT: Breast cancer is an abnormal growth of cells in the breast, usually in the inner lining of the milk ducts or lobules. It is currently the most common type of cancer in women in developed and developing countries. The number of women affected by breast cancer is gradually increasing and remains as a significant health concern. Researchers are continuously working to develop novel techniques to detect early stages of breast cancer. This book covers breast cancer detection, diagnosis, and treatment using different imaging modalities such as mammography, magnetic resonance imaging, computed tomography, positron emission tomography, ultrasonography, infrared imaging, and other modalities. The information and methodologies presented will be useful to researchers, doctors, teachers, and students in biomedical sciences, medical imaging, and engineering.
    Edited by E. Y. K. Ng; U. Rajendra Acharya; Rangaraj M. Rangayyan; Jasjit S. Suri, 03/2013; SPIE Press., ISBN: 978-0-8194-9294-4
  • Source
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    ABSTRACT: The nipple is an important landmark in mammograms. Detection of the nipple is useful for alignment and registration of mammograms in computer-aided diagnosis of breast cancer. In this paper, a novel approach is proposed for automatic detection of the nipple based on the oriented patterns of the breast tissues present in mammograms. The Radon transform is applied to the oriented patterns obtained by a bank of Gabor filters to detect the linear structures related to the tissue patterns. The detected linear structures are then used to locate the nipple position using the characteristics of convergence of the tissue patterns towards the nipple. The performance of the method was evaluated with 200 scanned-film images from the mini-MIAS database and 150 digital radiography (DR) images from a local database. Average errors of 5:84 mm and 6:36 mm were obtained with respect to the reference nipple location marked by a radiologist for the mini-MIAS and the DR images, respectively.
    Proceedings of SPIE - The International Society for Optical Engineering 02/2013; DOI:10.1117/12.2006787 · 0.20 Impact Factor
  • Paulo Mazzoncini de Azevedo-Marques, Rangaraj Mandayam Rangayyan
    Synthesis Lectures on Biomedical Engineering 01/2013; 8(1):1-143. DOI:10.2200/S00469ED1V01Y201301BME048
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    ABSTRACT: We present new feature descriptors specifically designed to quantify angular nonstationarity and angular dependence of pixel values in sectors of mammographic lesions. A key novelty of this work is that the proposed measures characterize the texture of masses without relying on accurate determination of their contours. An artificial neural network based on radial basis functions was used to predict the diagnosis of 120 benign masses and 26 malignant tumors in a database of full-field digital mammograms. Features were selected using stepwise logistic regression and the leave-one-patient-out method was used for cross-validation of results. An area under the receiver operating characteristic curve of 0.9890 ± 0.0114 was obtained using randomly selected centroids and an expected size of the masses. Results indicate that the use of the proposed contour-independent features can be an effective approach for computer-aided classification of mammographic lesions.
    E-Health and Bioengineering Conference (EHB), 2013; 01/2013
  • Rangaraj M Rangayyan, Shantanu Banik, J E Leo Desautels
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    ABSTRACT: We demonstrate methods for the detection of architectural distortion in prior mammograms of interval-cancer cases based on analysis of the orientation of breast tissue patterns in mammograms. We hypothesize that architectural distortion modifies the normal orientation of breast tissue patterns in mammographic images before the formation of masses or tumors. In the initial steps of our methods, the oriented structures in a given mammogram are analyzed using Gabor filters and phase portraits to detect node-like sites of radiating or intersecting tissue patterns. Each detected site is then characterized using the node value, fractal dimension, and a measure of angular dispersion specifically designed to represent spiculating patterns associated with architectural distortion. Our methods were tested with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases using the features developed for the characterization of architectural distortion, pattern classification via quadratic discriminant analysis, and validation with the leave-one-patient out procedure. According to the results of free-response receiver operating characteristic analysis, our methods have demonstrated the capability to detect architectural distortion in prior mammograms, taken 15 months (on the average) before clinical diagnosis of breast cancer, with a sensitivity of 80% at about five false positives per patient.
    Journal of Visualized Experiments 01/2013; DOI:10.3791/50341
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    ABSTRACT: Detection of blood vessels in retinal fundus images is an important initial step in the development of systems for computer-aided diagnosis of pathologies of the eye. In this study, we perform multifeature analysis for the detection of blood vessels in retinal fundus images. The techniques implemented include multiscale vesselness measures and Gabor filters. The selection of an appropriate threshold is crucial for accurate detection of retinal blood vessels. We propose an adaptive threshold selection method for this purpose. We also propose a postprocessing technique for removal of false-positive pixels around the optic nerve head. Values of the area under the receiver operating characteristic curve of up to 0.9616 were obtained using the 20 test images of the DRIVE database.
    E-Health and Bioengineering Conference (EHB), 2013; 01/2013
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    ABSTRACT: Chronic wounds, lesions, or ulcers due to venous insufficiency and other conditions typically have a nonuniform mixture of red granulation, yellow fibrin (slough), black necrotic eschar (scar), and white hyperkeratotic tissue (callous). Such a red-yellow-black-white (RYKW) model is used by clinicians as a descriptive tool. To facilitate the analysis of the tissue composition of a lesion, we propose color imaging and image processing methods. Methods based on clustering of color components in hue-saturation histograms and mathematical morphology are proposed for the segmentation of a given image into regions corresponding to red, yellow, black, and white tissue. Tests with 172 images indicated an average Jaccard coefficient of 0.56 with a standard deviation of 0.22 between the lesion area obtained computationally and the same lesion region manually delineated by a dermatologist. More importantly, a low average root-mean-squared error of 4% with a standard deviation of 5% was obtained between the tissue composition of lesions estimated using the proposed segmentation method and manual analysis.
    Electrical and Computer Engineering (CCECE), 2013 26th Annual IEEE Canadian Conference on; 01/2013
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    ABSTRACT: We propose a method to analyze bilateral asymmetry in mammograms based upon systematic comparison of paired mammographic strips of the right and left breasts of a patient. Similarity between corresponding directional structures was quantified by applying measures of similarity to the masked strips. A novel application of Moran's index was designed to measure the angular covariance between rose diagrams related to the phase and magnitude responses of multidirectional Gabor filters. A set of 128 mammograms from the DDSM database, including 32 normal and 32 asymmetric pairs, was used to validate the procedure. The leave-one-patient-out method was used for cross-validation of the results. The best result, with an area under the receiver operating characteristic curve of 0.8435, was achieved using similarity measures on craniocaudal views and Fisher-linear discriminant analysis. The results indicate that the proposed techniques can be applied for computer-aided detection of bilateral asymmetry.
    E-Health and Bioengineering Conference (EHB), 2013; 01/2013

Publication Stats

4k Citations
286.57 Total Impact Points


  • 1988–2014
    • The University of Calgary
      • • Schulich School of Engineering
      • • Department of Electrical and Computer Engineering
      • • Sport Medicine Centre
      Calgary, Alberta, Canada
  • 2010
    • Università degli Studi di Bari Aldo Moro
      • Dipartimento di Matematica
      Bari, Apulia, Italy
  • 2003–2010
    • University of São Paulo
      • • Faculdade de Medicina de Ribeirão Preto (FMRP)
      • • Departamento de Engenharia de Sistemas Eletrônicos (PSI) (POLI)
      • • Departamento de Física Matemática (FMA) (São Paulo)
      São Paulo, Estado de Sao Paulo, Brazil
  • 2000–2010
    • Ryerson University
      • Department of Electrical and Computer Engineering
      Toronto, Ontario, Canada
  • 1999–2008
    • Universidade Federal de Uberlândia (UFU)
      • Faculty of Computing (FACOM)
      UDI, Minas Gerais, Brazil
  • 2007
    • University of Liverpool
      • Department of Electrical Engineering and Electronics
      Liverpool, ENG, United Kingdom
  • 1996
    • Alexandria University
      • Department of Electrical Engineering
      Alexandria, Alexandria, Egypt
  • 1989
    • University of Lethbridge
      • Department of Biological Sciences
      Lethbridge, Alberta, Canada