Anam Tariq

National University of Science and Technology, Islāmābād, Islāmābād, Pakistan

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Publications (31)6.66 Total impact

  • [Show abstract] [Hide abstract]
    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. · 1.27 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; · 1.56 Impact Factor
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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; · 1.04 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; · 1.10 Impact Factor
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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
  • 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
  • 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: 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
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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. · 1.69 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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    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.
    01/2012;
  • Amna Saeed, Anam Tariq, Usman Jawaid
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    ABSTRACT: This research revolves around the fingerprint image enhancement. It is used for automated fingerprint identification systems (AFIS) for extracting the best quality fingerprint images. Accurate feature extraction and identification is the basic theme of this enhancement. This paper is on the fingerprint image enhancement using wavelets. Wavelets are famous for their special localization property and orientation flow estimation. The proposed technique is basically comprises of three main steps: segmentation followed by image sharpening and then Gabor wavelet filtering. Segmentation distinguishes between image background and foreground which in turn reduces processing time. Our sharpening stage of enhancement algorithm sharpens the edges and features by using prewitt mask followed by Gabor wavelet in order to enhance the feature of sharpened image. Gabor filters require orientation estimation and frequency for the improvement of the fingerprint image. But our algorithm is self sufficient. We have tested our algorithm on Fingerprint Verification Competition (FVC) 2004 database. Experimental results show that our algorithm proved to be effective in enhancing the fingerprint image quality.
    01/2011;
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    ABSTRACT: Biometrics are used for personal recognition based on some physiologic or behavioral characteristics. In this era, biometric security systems are widely used which mostly include fingerprint recognition, face recognition, iris and speech recognition etc. Retinal recognition based security systems are very rare due to retina acquisition problem but still it provides the most reliable and stable mean of biometric identification. This paper presents a four stage personal identification system using vascular pattern of human retina. In first step, it acquires and preprocesses the colored retinal image. Then blood vessels are enhanced and extracted using 2-D wavelet and adaptive thresholding respectively. In third stage, it performs feature extraction and filtration followed by vascular pattern matching in forth step. The proposed method is tested on three publicly available databases i.e DRIVE, STARE and VARIA. Experimental results show that the proposed method achieved an accuracy of 0.9485 and 0.9761 for vascular pattern extraction and personal recognition respectively.
    01/2011;
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    ABSTRACT: Automated fingerprint identification system (AFIS) is very popular now days for biometric security because of the uniqueness of individual's fingerprint. The need for fingerprint classification arises due to very large fingerprint databases resulting in long response time which is unsuitable for real time applications. Hence in order to reduce number of comparisons fingerprint classification is necessary. It also plays a key role in identifying fingerprints. In this paper we have proposed a new classification technique based on the detection of singular points (core and delta points) consisting of four stages. In the first stage, preprocessing of input fingerprint image is done followed by fine orientation field estimation in second stage. In the third stage, singular points are located using modified Poincare index technique and hence in the fourth stage, classification is done on the basis of these singular points. The proposed technique was tested on NIST 4 database and the results show a significant improvement in classification of different types of fingerprints.
    01/2011;
  • A. Tariq, M.U. Akram
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    ABSTRACT: Retinal images are used for the automated diagnosis of diabetic retinopathy. The retinal image quality must be improved for the detection of features and abnormalities and for this purpose segmentation of retinal images is vital. In this paper, we present a novel automated approach for segmentation of colored retinal images. Our segmentation technique smoothes and strengthens images by separating the background and noisy area from the overall image thus resulting in retinal image enhancement and lower processing time. It contains coarse segmentation and fine segmentation. Standard retinal images databases Diaretdb0 and Diaretdb1 are used to test the validation of our segmentation technique. Experimental results indicate our approach is effective and can get higher segmentation accuracy.
    Industrial Electronics & Applications (ISIEA), 2010 IEEE Symposium on; 11/2010
  • M.U. Akram, A. Tariq, S.A. Khan
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    ABSTRACT: The appearance and structure of blood vessels in retinal images play an important role in diagnosis of eye diseases. This paper proposes a method for segmentation of blood vessels in color retinal images. We present a method that uses 2-D Gabor wavelet to enhance the vascular pattern. We locate and segment the blood vessels using adaptive thresholding. The technique is tested on publicly available DRIVE database of manually labeled images which has been established to facilitate comparative studies on segmentation of blood vessels in retinal images. The proposed method achieves an area under the receiver operating characteristic curve of 0.963 on DRIVE database.
    Information and Communication Technologies, 2009. ICICT '09. International Conference on; 09/2009
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    ABSTRACT: Retinal image vessel segmentation and their branching pattern are used for automated screening and diagnosis of diabetic retinopathy. Vascular pattern is normally not visible in retinal images. We present a method that uses 2-D Gabor wavelet and sharpening filter to enhance and sharpen the vascular pattern respectively. Our technique extracts the vessels from sharpened retinal image using edge detection algorithm and applies morphological operation for their refinement. This technique is tested on publicly available DRIVE database of manually labeled images. The validation of our retinal image vessel segmentation technique is supported by experimental results.
    Computational Intelligence for Image Processing, 2009. CIIP '09. IEEE Symposium on; 05/2009
  • M. Usman Akram, Anam Tariq
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    ABSTRACT: The effective cooperation and coordination of nodes is necessary for performance improvement in the P2P network. In this paper, we focus on the problem of maintaining considerable amount of cooperation and friendship in P2P networks of selfish peers. For this purpose we will analyze the performance of SLAC. It is a simple algorithm that maintains excessive levels of cooperation and friendship in a network while performing tasks. A simulation model ldquoPrisoners' Dilemmardquo is presented that puts this algorithm to work in a Peersim environment. For providing random sampling, an existing protocol called ldquoNEWSCASTrdquo has been used. The extensive computer simulations show that the technique is scalable, robust and decentralized.
    01/2009;
  • M. Usman Akram, Anam Tariq
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    ABSTRACT: Automated localization and detection of the optic disc (OD) is an essential step in the analysis of digital diabetic retinopathy systems. Accurate localization and detection of optic disc boundary is very useful in proliferative diabetic retinopathy where fragile vessels develop in the retina. In this paper, we propose an automated system for optic disk localization and detection. Our method localizes optic disk using average filter and thresholding, extracts the region of interest (ROI) containing optic disk to save time and detects the optic disk boundary using Hough transform. This method can be used in computerized analysis of retinal images, e.g., in automated screening for diabetic retinopathy. The technique is tested on publicly available DRIVE, STARE, diarectdb0 and diarectdb1 databases of manually labeled images which have been established to facilitate comparative studies on localization and detection of optic disk in retinal images. The proposed method achieves an average accuracy of 96.7% for localization and an average area under the receiver operating characteristic curve of 0.958 for optic detection.
    FIT '09, 7th International Conference on Frontiers of Information Technology, Abbottabad, Pakistan, December 16-18, 2009; 01/2009
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    ABSTRACT: An important step in fingerprint recognition is the segmentation of the region of interest (ROI). The objective of fingerprint segmentation is to extract the region of interest (ROI) which contains the desired fingerprint impression. Fingerprint image segmentation highly influences the performances of automatic fingerprint identification system (AFIS). We present in this paper, a Modified Gradient Based Method to extract ROI. The distinct feature of our technique is that it gives high accurate segmentation percentage for fingerprint images even in case of low quality fingerprint images. The proposed algorithm is applied on FVC2004 database. Experimental results demonstrate the improved performance of the proposed scheme.
    Electrical and Computer Engineering, 2008. CCECE 2008. Canadian Conference on; 06/2008