Anam Tariq

NUST College of Electrical & Mechanical Engineering, Ralalpindi, Punjab, Pakistan

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Publications (39)7.47 Total impact

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    ABSTRACT: Diabetic Retinopathy (DR) is an eye abnormality in which the human retina is affected due to an increasing amount of insulin in blood. The early detection and diagnosis of DR is vital to save the vision of diabetes patients. The early signs of DR which appear on the surface of the retina are microaneurysms, haemorrhages, and exudates. In this paper, we propose a system consisting of a novel hybrid classifier for the detection of retinal lesions. The proposed system consists of preprocessing, extraction of candidate lesions, feature set formulation, and classification. In preprocessing, the system eliminates background pixels and extracts the blood vessels and optic disc from the digital retinal image. The candidate lesion detection phase extracts, using filter banks, all regions which may possibly have any type of lesion. A feature set based on different descriptors, such as shape, intensity, and statistics, is formulated for each possible candidate region: this further helps in classifying that region. This paper presents an extension of the m-Mediods based modeling approach, and combines it with a Gaussian Mixture Model in an ensemble to form a hybrid classifier to improve the accuracy of the classification. The proposed system is assessed using standard fundus image databases with the help of performance parameters, such as, sensitivity, specificity, accuracy, and the Receiver Operating Characteristics curves for statistical analysis.
    Computers in Biology and Medicine 02/2014; 45C:161-171. DOI:10.1016/j.compbiomed.2013.11.014 · 1.90 Impact Factor
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    ABSTRACT: Medical systems based on state of the art image processing and pattern recognition techniques are very common now a day. These systems are of prime interest to provide basic health care facilities to patients and support to doctors. Diabetic macular edema is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. It affects the central vision of the person and causes total blindness in severe cases. In this article, we propose an intelligent system for detection and grading of macular edema to assist the ophthalmologists in early and automated detection of the disease. The proposed system consists of a novel method for accurate detection of macula using a detailed feature set and Gaussian mixtures model based classifier. We also present a new hybrid classifier as an ensemble of Gaussian mixture model and support vector machine for improved exudate detection even in the presence of other bright lesions which eventually leads to reliable classification of input retinal image in different stages of macular edema. The statistical analysis and comparative evaluation of proposed system with existing methods are performed on publicly available standard retinal image databases. The proposed system has achieved average value of 97.3%, 95.9% and 96.8% for sensitivity, specificity and accuracy respectively on both databases.
    Computer methods and programs in biomedicine 01/2014; DOI:10.1016/j.cmpb.2014.01.010 · 1.09 Impact Factor
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    ABSTRACT: Microaneurysms (MAs) are the first visible sign of diabetic retinopathy (DR), a retinal abnormality which may lead to blindness in diabetes patients. In time and precise MAs detection is very important for early diagnosis of DR and can save patient's vision. In this paper, we present an automated system for accurate and reliable detection of MAs. The proposed system consists of preprocessing, feature extraction and classification stages. The preprocessing step extracts all possible regions which may be considered as MAs from input retinal image and feature extraction stage represents each region with a number of features. A novel hybrid classifier which combines Gaussian mixture model and support vector machine in an ensemble, finally classifies each region as MA or non-MAo The proposed system uses genetic algorithm in order to optimized the weights for hybrid classifier. The evaluation of proposed system is performed using publicly available retinal image database and results are compared with existing techniques to demonstrate the validity of proposed system.
    2013 International Conference on Electronics, Computer and Computation (ICECCO); 11/2013
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    ABSTRACT: Age related macular degeneration (ARMD) is an eye abnormality due to deposits of drusen on the retina and the disease may cause severe blindness. Early detection of ARMD using a computerized system can save patient's vision. The ophthalmologists can find this system useful for screening of ARMD. This paper presents a novel method for accurate detection of drusen in colored retinal images. The system uses filter bank to extract all possible drusen regions form retinal image and eliminates false pixels which appear because of resemblance of drusen with optic disc. The proposed system represents each region with a number of features and then applies support vector machine to classify these regions as drusen and non-drusen. The performance of the proposed system is evaluated by testing it on STARE database using parameters such as sensitivity, specificity and accuracy. The validity of the proposed method is also shown by comparing it with already published methods.
    2013 International Conference on Electronics, Computer and Computation (ICECCO); 11/2013
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    ABSTRACT: Optical character recognition is an application of pattern recognition which automatically detects and recognizes the optical characters with out human intervention. All the characters are basically made up from three geometric entities, i.e. corners, endings and bifurcations which can be used to identify different characters. In this paper, we present a method for optical character recognition based on basic geometric features. The method uses a crossing number method to extract features from thinned character. The feature vector for each character consists of number of corners, endings and bifurcations. The classification stage recognizes a character by using a simple rule based method. The proposed system is tested using different samples for each character and the results show the validity of the proposed algorithm.
    2013 IEEE Symposium on Industrial Electronics & Applications (ISIEA); 09/2013
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    ABSTRACT: Automated character recognition is a wide field and current area of research in image processing and pattern recognition. It has its applications in optical character recognition, handwritten character recognition, postal code readers, car number plate identification and even in biometrics for identification of persons on basis of their handwritings. In this paper, we present an automated system for identification and classification of handwritten numeral characters. Our system consists of three stages i.e. preprocessing, feature extraction and classification. We propose intensity, shape and geometric based features for accurate representation of each numeral character. The system applies a Gaussian Mixture Model using expectation maximization for classification of input characters. In order to check the accuracy of proposed system, we use United States Postal Service (USPS) database and the results show the validity of proposed system.
    2013 IEEE Symposium on Industrial Electronics & Applications (ISIEA); 09/2013
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    ABSTRACT: Diabetic retinopathy is a progressive eye disease and one of the leading causes of blindness all over the world. New blood vessels (neovascularization) start growing at advance stage of diabetic retinopathy known as proliferative diabetic retinopathy. Early and accurate detection of proliferative diabetic retinopathy is very important and crucial for protection of patient's vision. Automated systems for detection of proliferative diabetic retinopathy should identify between normal and abnormal vessels present in digital retinal image. In this paper, we proposed a new method for detection of abnormal blood vessels and grading of proliferative diabetic retinopathy using multivariate m-Mediods based classifier. The system extracts the vascular pattern and optic disc using a multilayered thresholding technique and Hough transform respectively. It grades the fundus image in different categories of proliferative diabetic retinopathy using classification and optic disc coordinates. The proposed method is evaluated using publicly available retinal image databases and results show that the proposed system detects and grades proliferative diabetic retinopathy with high accuracy.
    Computerized medical imaging and graphics: the official journal of the Computerized Medical Imaging Society 07/2013; DOI:10.1016/j.compmedimag.2013.06.008 · 1.50 Impact Factor
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    ABSTRACT: Diabetic maculopathy is one of the retinal abnormalities in which a diabetic patient suffers from severe vision loss due to the affected macula. It affects the central vision of the person and causes blindness in severe cases. In this article, we propose an automated medical system for the grading of diabetic maculopathy that will assist the ophthalmologists in early detection of the disease. The proposed system extracts the macula from digital retinal image using the vascular structure and optic disc location. It creates a binary map for possible exudate regions using filter banks and formulates a detailed feature vector for all regions. The system uses a Gaussian Mixture Model-based classifier to the retinal image in different stages of maculopathy by using the macula coordinates and exudate feature set. The evaluation of proposed system is performed by using publicly available standard retinal image databases. The results of our system have been compared with other methods in the literature in terms of sensitivity, specificity, positive predictive value and accuracy. Our system gives higher values as compared to others on the same databases which makes it suitable for an automated medical system for grading of diabetic maculopathy.
    Journal of Digital Imaging 01/2013; 26(4). DOI:10.1007/s10278-012-9549-4 · 1.20 Impact Factor
  • 01/2013; DOI:10.7763/IJFCC.2013.V2.235
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    A. Tariq, M.U. Akram, M.Y. Javed
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    ABSTRACT: Automated lung cancer detection using computer aided diagnosis (CAD) is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.
    Computational Intelligence in Medical Imaging (CIMI), 2013 IEEE Fourth International Workshop on; 01/2013
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    ABSTRACT: Digital fundus images are one of the modern and advanced approaches of creating image of inner surface of human eye emphasizing retina. These fundus images are really helpful in diagnosis of possible abnormalities and severe diseases like diabetic macular edema and its various types. Research has shown that early detection and treatment can prevent total vision loss and severe impacts on human visual system. Hence an automated system for diagnosing macular edema will help the ophthalmologists and patients. In this paper, we have proposed a novel method for diagnosing macular edema using fundus images. The technique has four steps which constitutes of preprocessing, macula detection, feature extraction of possible exudates region followed by classification using Naïve Bayes classifier. The proposed system is tested using MESSIDOR database and results show that our method outperformed others in terms of accuracy.
    Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference on; 01/2013
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    A. Tariq, M.U. Akram, M.Y. Javed
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    ABSTRACT: The automated detection and diagnosis of Diabetic Retinopathy (DR) is very critical to save the patient's vision and to help the ophthalmologists in mass screening of diabetes sufferers. DR is a progressive eye disease and should be detected as early as possible. In this paper, we present a new system for detection and classification of different DR lesions i.e. Microaneurysms (MAs), Haemorrhage (H), Hard Exudates (HE) and Cotton Wool Spots (CWS). We proposed a three stage system in which first stage extracts all possible candidate lesions present in a fundus image suing filter bank. Then feature sets are computed for each candidate lesion using different properties and features followed by classification of lesions. The evaluation of proposed system is performed using retinal image databases with the help of different performance matrices and the results show the validity of proposed system.
    Computational Intelligence in Medical Imaging (CIMI), 2013 IEEE Fourth International Workshop on; 01/2013
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    ABSTRACT: Age related macular degeneration (ARMD) is a medical condition which results in deterioration of human retina and in particularly macula. It is caused due to deposits of drusen on the retina and the disease may cause severe blindness. It is important to detect ARMD in its early stages to save patient's vision. This paper proposes a new technique for drusen detection from fundus images by using Gabor kernel based filter bank and eliminating spurious regions which may be confused with drusen. The proposed system represents each region with a number of features and then applies hybrid classifier as an ensemble of Naive Bayes and Support Vector Machine to classify these regions as drusen and non-drusen. The proposed system is evaluated by testing it on STARE database using performance factors like sensitivity, specificity and accuracy. The results show the comparison and validity of proposed system with existing techniques.
    Applied Electrical Engineering and Computing Technologies (AEECT), 2013 IEEE Jordan Conference on; 01/2013
  • 01/2013; DOI:10.7763/IJFCC.2013.V2.238
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    ABSTRACT: In medical imaging, digital images are analyzed to develop computer aided diagnostic (CAD) systems using state of the art image processing and pattern recognition techniques. Diabetic maculopathy is one of the retinal abnormalities in which diabetic patient suffers from severe vision loss due to affected macula. In this paper, we propose an automated system for the grading of diabetic maculopathy to assist the ophthalmologists in early detection of the disease. We present a three stage system consisting of macula detection, exudate extraction and grading of maculopathy. First stage uses optic disc and blood vessels to extract macula from retinal image. Exudate extraction stage extracts all possible exudates from retina using filter bank and support vector machines. Finally, the system grades the input image in different stages of maculopathy by using the macular coordinates and exudate feature set. The evaluation of proposed system is performed by using publicly available standard retinal image databases.
    2012 Cairo International Biomedical Engineering Conference (CIBEC); 12/2012
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    ABSTRACT: Medical image analysis is a very popular research area these days in which digital images are analyzed for the diagnosis and screening of different medical problems. Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness. An automated system for early detection of DR can save a patient's vision and can also help the ophthalmologists in screening of DR. The background or nonproliferative DR contains four types of lesions, i.e., microaneurysms, hemorrhages, hard exudates, and soft exudates. This paper presents a method for detection and classification of exudates in colored retinal images. We present a novel technique that uses filter banks to extract the candidate regions for possible exudates. It eliminates the spurious exudate regions by removing the optic disc region. Then it applies a Bayesian classifier as a combination of Gaussian functions to detect exudate and nonexudate regions. The proposed system is evaluated and tested on publicly available retinal image databases using performance parameters such as sensitivity, specificity, and accuracy. We further compare our system with already proposed and published methods to show the validity of the proposed system.
    Applied Optics 07/2012; 51(20):4858-66. DOI:10.1364/AO.51.004858 · 1.78 Impact Factor
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    ABSTRACT: Diabetic retinopathy is one of the leading cause of blindness caused due to increase of insulin in blood. It is a progressive disease and needs an early detection and treatment. Proliferative diabetic retinopathy is an advance stage and causes severe visual impairments. Early and accurate detection of proliferative diabetic retinopathy is very important and crucial for protection of patient's vision. Automated systems for screening of proliferative diabetic retinopathy should accurately detect the blood vessels to identify vascular abnormalities. In this paper, we present a method for screening of proliferative diabetic retinopathy using blood vessel structure. The method extracts the vascular pattern by enhancing the blood vessels using wavelet response and segmenting the blood vessels using a multilayered thresholding technique. It uses a Gaussian mixture model based classifier for detection of neovascularization. The proposed method is evaluated using publicly available retinal image databases and results show that the proposed system identifies the vascular abnormalities with high accuracy.
    Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II; 06/2012
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    Anam Tariq, M Usman Akram
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    ABSTRACT: Biometric authentication for personal identification is very popular now a days. Human ear recognition system is a new technology in this field. The change of appearance with the expression was a major problem in face biometrics but in case of ear biometrics the shape and appearance is fixed. That is why it is advantageous to use it for personal identification. In this paper, we have proposed a new approach for an automated system for human ear identification. Our proposed method consists of three stages. In the first stage, preprocessing of ear image is done for its contrast enhancement and size normalization. In the second stage, features are extracted through Haar wavelets followed by ear identification using fast normalized cross correlation in the third stage. The proposed method is applied on USTB ear image database and IIT Delhi. Experimental results show that our proposed system achieves an average accuracy of 97.2% and 95.2% on these databases respectively.
    06/2012; 10(2). DOI:10.12928/telkomnika.v10i2.46
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    M Usman Akram, Ibaa Jamal, Anam Tariq
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    ABSTRACT: Diabetic retinopathy is an eye disease caused by the increase of insulin in blood and it is one of the main cuases of blindness in idusterlized countries. It is a progressive disease and needs an early detection and treatment. Vascular pattern of human retina helps the ophthalmologists in automated screening and diagnosis of diabetic retinopathy. In this article, we present a method for vascular pattern ehnacement and segmentation. We present an automated system which uses wavelets to enhance the vascular pattern and then it applies a piecewise threshold probing and adaptive thresholding for vessel localization and segmentation respectively. The method is evaluated and tested using publicly available retinal databases and we further compare our method with already proposed techniques.
    06/2012; 10(2). DOI:10.11591/telkomnika.v10i2.686
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    ABSTRACT: Retinal image analysis is very effective in early detection and diagnosis of diabetic retinopathy. Diabetic retinopathy is a progressive disease and is broadly classify into two stages i.e. Non proliferative diabetic retinopathy (NPDR) and Proliferative diabetic retinopathy (PDR). A sign of PDR is the appearance of new blood vessels in fundus area and inside optic disc known as neovascularization. The study of blood vessel is very important for detection of neovascularization. In this paper, we present a method for accurate blood vessel detection which can be used for detection of neovascularization. The paper presents a new method for vessel segmentation using a multilayered thresholding technique. The method is tested using two publicly available retinal image databases and experimental results show the significance of proposed work.

Publication Stats

107 Citations
7.47 Total Impact Points

Institutions

  • 2007–2014
    • NUST College of Electrical & Mechanical Engineering
      Ralalpindi, Punjab, Pakistan
  • 2008–2013
    • National University of Science and Technology
      • Department of Computer Engineering
      Islāmābād, Islāmābād, Pakistan
  • 2012
    • Bahria University
      • Department of Computer and Software Engineering
      Islāmābād, Islāmābād, Pakistan
  • 2009–2011
    • Fatima Jinnah Women University
      Ralalpindi, Punjab, Pakistan