Zahra Waheed

Diagnosis of dermoscopic skin lesions due to skin cancer is the most challenging task for the experienced dermatologists. In this context, dermoscopy is the non-invasive useful method for the detection of skin lesions which are not visible to naked human eye. Among different types of skin cancers, malignant melanoma is the most aggressive and deadliest form of skin cancer. Its diagnosis is crucial if not detected in early stage. This paper mainly aims to present an efficient machine learning approach for the detection of melanoma from dermoscopic images. It detects melanomic skin lesions based upon their discriminating properties. In first step of proposed method, different types of color and texture features are extracted from dermoscopic images based on distinguished structures and varying intensities of melanomic lesions. In second step, extracted features are fed to the classifier to classify melanoma out of dermoscopic images. Paper also focuses on the role of color and texture features in the context of detection of melanomas. Proposed method is tested on publicly available PH2 dataset in terms of accuracy, sensitivity, specificity and Area under ROC curve (AUC). It is observed that good results are achieved using extracted features, hence proving the validity of the proposed system.

In this competitive era, biometric systems provide more reliable security than traditional methods like passwords etc. Biometric systems perform person's authentication based on his physical traits. A number of biometric systems has been developed in the last few years such as fingerprints, hand and palm geometry, retina etc. Due to stability, uniqueness and non-replicable nature of vascular pattern, retinal recognition is the most stable biometric system. Retinal recognition performs person's identification based on the unique vasculature of retina. Generally, it is a three-step process, which includes pre-processing, segmentation and matching. Segmentation is the fundamental step, which becomes crucial in the presence of different pathological signs like exudates, lesions. If they are not removed in segmentation, then they produce false positives, hence leads to misclassification. To address this problem, this paper presents an efficient segmentation algorithm which aims to remove pathological effects from the diseased retinal images and improve matching results by reducing false recognition rate. Experimental results demonstrate the efficiency of proposed system.

Retinal blood vessels are the source to provide oxygen and nutrition to retina and any change in the normal structure may lead to different retinal abnormalities. Automated detection of vascular structure is very important while designing a computer aided diagnostic system for retinal diseases. Most popular methods for vessel segmentation are based on matched filters and Gabor wavelets which give good response against blood vessels. One major drawback in these techniques is that they also give strong response for lesion (exudates, hemorrhages) boundaries which give rise to false vessels. These false vessels may lead to incorrect detection of vascular changes. In this paper, we propose a new hybrid feature set along with new classification technique for accurate detection of blood vessels. The main motivation is to lower the false positives especially from retinal images with severe disease level. A novel region based hybrid feature set is presented for proper discrimination between true and false vessels. A new modified m-mediods based classification is also presented which uses most discriminating features to categorize vessel regions into true and false vessels. The evaluation of proposed system is done thoroughly on publicly available databases along with a locally gathered database with images of advanced level of retinal diseases. The results demonstrate the validity of the proposed system as compared to existing state of the art techniques.

Automated quantification of blood vessels in human retina is the fundamental step in designing any computer-aided diagnosis system for ophthalmic disorders. Detection and analysis of variations in blood vessels can be used to diagnose several ocular diseases like diabetic retinopathy. Diabetic Retinopathy is a progressive vascular disorder caused due to variations in blood vessels of retina. These variations bring different abnormalities like lesions, exudates, and hemorrhages in human retina which make the vessel detection problematic. Therefore, automated retinal analysis is required to cater the effect of lesions while segmenting blood vessels. The proposed framework presents two improved approaches to carry out vessel segmentation in the presence of lesions. The paper mainly aims to extract true vessels by reducing the effect of abnormal structures significantly. First method is a supervised approach which extracts true vessels by performing region based analysis of retinal imag...

Biometrics are the personal physiological and behavioral characteristics which are mostly used for personal recognition. Today, biometric based security systems such as fingerprint, iris and face recognition are used everywhere especially in high security areas. Human retina is another source of biometric system which provides the most reliable and stable means of authentication. In this paper, we present a system for recognition based on vascular pattern of human retina. The proposed algorithm consists of three stages; i.e. preprocessing, feature extraction and finally the matching process. In preprocessing, it extracts the vascular pattern from input retinal image and then it formulates the feature vector in feature extraction stage followed by vascular matching. The proposed method is tested on publicly available databases and experimental results show that the proposed method achieves high accuracies for vascular pattern extraction and matching.