M. Younus Javed

Fuzzy case-based reasoning for facial expression recognition

Fuzzy logic (FL) and case-based reasoning (CBR) are two well-known techniques for the implementation of intelligent classification systems. Each technique has its own advantages and drawbacks. FL, for example, provides an intuitive user interface, simplifies the process of knowledge representation, and minimizes the systems computational complexity in terms of time and memory usage. On the other hand, FL has problems in knowledge elicitation which render it difficult to adopt for intelligent system implementation. CBR avoids these problems by making use of past input-output data to decide the system output for the present input. The accuracy of CBR system grows as the number of cases increase. However, more cases can mean added computational complexity in terms of space and time. In this paper we make the proposition that a hybrid system comprising a blend of FL and CBR can lead to a solution where the two approaches cover each others weaknesses and benefit from each others strengths. We support our claim by taking the problem of facial expression recognition from an input image. The facial expression recognition system presented in this paper uses a case base populated with fuzzy rules for recognizing each expression. Experimental results demonstrate that the system inherits the strengths of both methods.

Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy

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 patients 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.

Automated detection of exudates and macula for grading of diabetic macular edema

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.

Detection of neovascularization in retinal images using multivariate m-Mediods based classifier

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 patients 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.

A Survey on Sign Language Recognition

Sign Language (SL) recognition is getting more and more attention of the researchers due to its widespread applicability in many fields. This paper is based on the survey of the current research trends in the field of SL recognition to highlight the current status of different research aspects of the area. Paper also critically analyzed the current research to identify the problem areas and challenges faced by the researchers. This identification is aimed at providing guideline for the future advances in the field.

Discrete cosine transform (DCT) based face recognition in hexagonal images

In this paper a new approach to face recognition is presented which is based on processing of face images in hexagonal lattice. The importance of the hexagonal representation is that it possesses special computational features that are pertinent to the Human Vision process. Few advantages of processing images on hexagonal lattice are higher degree of circular symmetry, uniform connectivity, greater angular resolution, and a reduced need of storage and computation in image processing operations. Proposed methodology is a hybrid approach to face recognition. DCT is being applied to hexagonally converted images for dimensionality reduction and feature extraction. These features are stored in a database for recognition purpose. Artificial Neural Network (ANN) is being used for recognition. Experiments and testing were conducted over ORL, Yale and FERET databases. The proposed methodology has given better results in recognition over square pixel based approaches.

An Efficient Rule-Based Classification of Diabetes Using ID3, C4.5, a CART Ensembles

Conventional techniques for clinical decision support systems are based on a single classifier or simple combination of these classifiers used for disease diagnosis and prediction. Recently much attention has been paid on improving the performance of disease prediction by using ensemble-based methods. In this paper, we use multiple ensemble classification techniques for diabetes datasets. Three types of decision trees ID3, C4.5 and CART are used as the base classifiers. The ensemble techniques used are Majority Voting, Adaboost, Bayesian Boosting, Stacking and Bagging. Two benchmark diabetes datasets are used from UCI and Bio Stat repositories respectively. Experimental results and evaluation show that Bagging ensemble technique shows better performance as compared to single as well as other ensemble techniques.

An automated system for the grading of diabetic maculopathy in fundus images

Computer aided diagnosis systems are very popular now days as they assist doctors in early detection of the disease. Diabetic maculopathy is one such disease which affects the retina of the diabetic patients. It affects the central vision of the person and causes blindness in severe cases. In this paper, an automated system for the grading of diabetic maculopathy has been developed, that will assist the ophthalmologists in early detection of the disease. Here, we propose a novel computerized method for the grading of diabetic maculopathy in fundus images. Our proposed system comprises of preprocessing of retinal image followed by macula and exudate regions detection. This is followed by feature extractor module for the formulation of feature set. SVM classifier is then used to grade the diabetic maculopathy. The publicly available fundus image database MESSIDOR has been used for the validation of our algorithm. The results of our proposed system have been compared with other methods in the literature in terms of sensitivity and specificity. Our system gives higher values of sensitivity and specificity as compared to others on the same database.

EEAR: Efficient Energy Aware Routing in wireless sensor networks

The energy efficiency in wireless sensor networks plays a very important role because of the limited power of the battery used within nodes. This paper presents Efficient Energy Aware Routing (EEAR) algorithm for wireless sensor networks, that makes routing decisions on the basis of nodes residual energy. EEAR explores the optimized routing path by implementing a model which is function of the shortest distance and the residual energy of all nodes in a routing path. The proposed model also exploits the neighbors information of each node in the optimized path in order to reduce the probability of packet loss and to increase throughput. Simulation and comparative analysis show that EEAR gives higher packet delivery rate, less energy consumption with the maximum network life time as compared to traditional routing algorithms for wireless sensor networks.

Computer aided diagnostic system for grading of diabetic retinopathy

The automated detection and diagnosis of Diabetic Retinopathy (DR) is very critical to save the patients 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.