Retinal Area Segmentation using Adaptive Superpixalation and its Classification using RBFN

Retinal disease is the very important issue in medical field. To diagnose the disease, it needs to detect the true retinal area. Artefacts like eyelids and eyelashes are come along with retinal part so removal of artefacts is the big task for better diagnosis of disease into the retinal part.  In this paper, we have proposed the segmentation and use machine learning approaches to detect the true retinal part. Preprocessing is done on the original image using Gamma Normalization which helps to enhance the image  that can gives detail information about the image. Then the segmentation is performed on the Gamma Normalized image by Superpixel method. Superpixel is the group of pixel into different regions which is based on compactness and regional size. Superpixel is used to reduce the complexity of image processing task and provide suitable primitive image pattern. Then feature generation must be done and machine learning approach helps to extract true retinal area. The experimental evaluation gives the better result with accuracy of 96%

Techniques for Ocular Biometric Recognition Under Non-ideal Conditions

The use of the ocular region as a biometric cue has gained considerable traction due to recent advances in automated iris recognition. However, a multitude of factors can negatively impact ocular recognition performance under unconstrained conditions (e.g., non-uniform illumination, occlusions, motion blur, image resolution, etc.). This dissertation develops techniques to perform iris and ocular recognition under challenging conditions. The first contribution is an image-level fusion scheme to improve iris recognition performance in low-resolution videos. Information fusion is facilitated by the use of Principal Components Transform (PCT), thereby requiring modest computational efforts. The proposed approach provides improved recognition accuracy when low-resolution iris images are compared against high-resolution iris images. The second contribution is a study demonstrating the effectiveness of the ocular region in improving face recognition under plastic surgery. A score-level fusion approach that combines information from the face and ocular regions is proposed. The proposed approach, unlike other previous methods in this application, is not learning-based, and has modest computational requirements while resulting in better recognition performance. The third contribution is a study on matching ocular regions extracted from RGB face images against that of near-infrared iris images. Face and iris images are typically acquired using sensors operating in visible and near-infrared wavelengths of light, respectively. To this end, a sparse representation approach which generates a joint dictionary from corresponding pairs of face and iris images is designed. The proposed joint dictionary approach is observed to outperform classical ocular recognition techniques. In summary, the techniques presented in this dissertation can be used to improve iris and ocular recognition in practical, unconstrained environments

Performances of proposed normalization algorithm for iris recognition

Iris recognition has very high recognition accuracy in comparison with many other biometric features. The iris pattern is not the same even right and left eye of the same person. It is different and unique. This paper proposes an algorithm to recognize people based on iris images. The algorithm consists of three stages. In the first stage, the segmentation process is using circular Hough transforms to find the region of interest (ROI) of given eye images. After that, a proposed normalization algorithm is to generate the polar images than to enhance the polar images using a modified Daugman’s Rubber sheet model. The last step of the proposed algorithm is to divide the enhance the polar image to be 16 divisions of the iris region. The normalized image is 16 small constant dimensions. The Gray-Level Co-occurrence Matrices (GLCM) technique calculates and extracts the normalized image’s texture feature. Here, the features extracted are contrast, correlation, energy, and homogeneity of the iris. In the last stage, a classification technique, discriminant analysis (DA), is employed for analysis of the proposed normalization algorithm. We have compared the proposed normalization algorithm to the other nine normalization algorithms. The DA technique produces an excellent classification performance with 100% accuracy. We also compare our results with previous results and find out that the proposed iris recognition algorithm is an effective system to detect and recognize person digitally, thus it can be used for security in the building, airports, and other automation in many applications

Adaptive noise reduction and code matching for IRIS pattern recognition system

Among all biometric modalities, iris is becoming more popular due to its high performance in recognizing or verifying individuals. Iris recognition has been used in numerous fields such as authentications at prisons, airports, banks and healthcare. Although iris recognition system has high accuracy with very low false acceptance rate, the system performance can still be affected by noise. Very low intensity value of eyelash pixels or high intensity values of eyelids and light reflection pixels cause inappropriate threshold values, and therefore, degrade the accuracy of system. To reduce the effects of noise and improve the accuracy of an iris recognition system, a robust algorithm consisting of two main components is proposed. First, an Adaptive Fuzzy Switching Noise Reduction (AFSNR) filter is proposed. This filter is able to reduce the effects of noise with different densities by employing fuzzy switching between adaptive median filter and filling method. Next, an Adaptive Weighted Shifting Hamming Distance (AWSHD) is proposed which improves the performance of iris code matching stage and level of decidability of the system. As a result, the proposed AFSNR filter with its adaptive window size successfully reduces the effects ofdifferent types of noise with different densities. By applying the proposed AWSHD, the distance corresponding to a genuine user is reduced, while the distance for impostors is increased. Consequently, the genuine user is more likely to be authenticated and the impostor is more likely to be rejected. Experimental results show that the proposed algorithm with genuine acceptance rate (GAR) of 99.98% and is accurate to enhance the performance of the iris recognition system

A Study of Segmentation and Normalization for Iris Recognition Systems

Iris recognition systems capture an image from an individual's eye. The iris in the image is then segmented and normalized for feature extraction process. The performance of iris recognition systems highly depends on segmentation and normalization. For instance, even an effective feature extraction method would not be able to obtain useful information from an iris image that is not segmented or normalized properly. This thesis is to enhance the performance of segmentation and normalization processes in iris recognition systems to increase the overall accuracy. The previous iris segmentation approaches assume that the boundary of pupil is a circle. However, according to our observation, circle cannot model this boundary accurately. To improve the quality of segmentation, a novel active contour is proposed to detect the irregular boundary of pupil. The method can successfully detect all the pupil boundaries in the CASIA database and increase the recognition accuracy. Most previous normalization approaches employ polar coordinate system to transform iris. Transforming iris into polar coordinates requires a reference point as the polar origin. Since pupil and limbus are generally non-concentric, there are two natural choices, pupil center and limbus center. However, their performance differences have not been investigated so far. We also propose a reference point, which is the virtual center of a pupil with radius equal to zero. We refer this point as the linearly-guessed center. The experiments demonstrate that the linearly-guessed center provides much better recognition accuracy. In addition to evaluating the pupil and limbus centers and proposing a new reference point for normalization, we reformulate the normalization problem as a minimization problem. The advantage of this formulation is that it is not restricted by the circular assumption used in the reference point approaches. The experimental results demonstrate that the proposed method performs better than the reference point approaches. In addition, previous normalization approaches are based on transforming iris texture into a fixed-size rectangular block. In fact, the shape and size of normalized iris have not been investigated in details. In this thesis, we study the size parameter of traditional approaches and propose a dynamic normalization scheme, which transforms an iris based on radii of pupil and limbus. The experimental results demonstrate that the dynamic normalization scheme performs better than the previous approaches

Pre-operative trichiatic eyelash pattern predicts post-operative trachomatous trichiasis

Importance Trichiasis surgery programs globally have faced high rates of poor surgical outcomes. Identifying correctable risk factors for improving long-term outcomes is essential for countries targeting elimination of trachoma as a public health problem. Objective To determine whether the location of trichiatic eyelashes prior to surgery influences development of post-operative trichiasis (PTT) within two years after surgery. Design Secondary data analysis of four randomized clinical trials evaluating methods to improve trichiasis surgery outcomes. These include the Surgery for Trichiasis, Antibiotics for Recurrence (STAR) trial, Partnership for Rapid Elimination of Trachoma (PRET-Surgery), absorbable versus silk sutures trial, and epilation versus surgery for minor trichiasis trial. Setting Primary trials were conducted in rural areas of Ethiopia and Tanzania Interventions or exposures Trichiasis surgery performed with either the bilamellar tarsal rotation procedure or posterior lamellar rotation procedure Main outcomes Prevalence of PTT within two years after surgery, location of trichiatic eyelashes pre-operatively and post-operatively Results 6,747 eyelids that underwent first-time trichiasis surgery were included. PTT rates varied by study, ranging from 10–40%. PTT was less severe (based on number of trichiatic eyelashes) than initial trichiasis for 72% of those developing PTT, and only 2% of eyelids were worse at follow up than pre-operatively. Eyelids with central only-trichiasis pre-operatively had lower rates of PTT than eyelids with peripheral only trichiasis in each of the three trials that included severe TT cases. 10% of eyelids with peripheral trichiasis pre-operatively that develop PTT have central TT post-operatively. Conclusions and relevance Pre-operative central trichiasis is less likely than peripheral trichiasis to be associated with subsequent PTT. Regardless of type of surgery, surgeon skill levels, or pre-operative trichiasis severity, the presence of peripheral trichiasis pre-operatively is associated with higher rates of PTT. Making an incision that extends the length of the eyelid and adequately rotating the nasal and temporal aspects of the eyelid when suturing may help to minimize the chance of developing peripheral PTT

Pre-operative trichiatic eyelash pattern predicts post-operative trachomatous trichiasis.

IMPORTANCE: Trichiasis surgery programs globally have faced high rates of poor surgical outcomes. Identifying correctable risk factors for improving long-term outcomes is essential for countries targeting elimination of trachoma as a public health problem. OBJECTIVE: To determine whether the location of trichiatic eyelashes prior to surgery influences development of post-operative trichiasis (PTT) within two years after surgery. DESIGN: Secondary data analysis of four randomized clinical trials evaluating methods to improve trichiasis surgery outcomes. These include the Surgery for Trichiasis, Antibiotics for Recurrence (STAR) trial, Partnership for Rapid Elimination of Trachoma (PRET-Surgery), absorbable versus silk sutures trial, and epilation versus surgery for minor trichiasis trial. SETTING: Primary trials were conducted in rural areas of Ethiopia and Tanzania. INTERVENTIONS OR EXPOSURES: Trichiasis surgery performed with either the bilamellar tarsal rotation procedure or posterior lamellar rotation procedure. MAIN OUTCOMES: Prevalence of PTT within two years after surgery, location of trichiatic eyelashes pre-operatively and post-operatively. RESULTS: 6,747 eyelids that underwent first-time trichiasis surgery were included. PTT rates varied by study, ranging from 10-40%. PTT was less severe (based on number of trichiatic eyelashes) than initial trichiasis for 72% of those developing PTT, and only 2% of eyelids were worse at follow up than pre-operatively. Eyelids with central only-trichiasis pre-operatively had lower rates of PTT than eyelids with peripheral only trichiasis in each of the three trials that included severe TT cases. 10% of eyelids with peripheral trichiasis pre-operatively that develop PTT have central TT post-operatively. CONCLUSIONS AND RELEVANCE: Pre-operative central trichiasis is less likely than peripheral trichiasis to be associated with subsequent PTT. Regardless of type of surgery, surgeon skill levels, or pre-operative trichiasis severity, the presence of peripheral trichiasis pre-operatively is associated with higher rates of PTT. Making an incision that extends the length of the eyelid and adequately rotating the nasal and temporal aspects of the eyelid when suturing may help to minimize the chance of developing peripheral PTT. TRIAL REGISTRATION: ClinicalTrials.gov PRET: NCT00886015; Suture: NCT005228560; Epilation: NCT00522912

Black hole algorithm along edge detector and circular hough transform based iris projection with biometric identification systems

The circular parameters between the pupil and the iris are found using current iris identification techniques but the accuracy creates an issue for the detection process during image processing. The procedure of extracting the iris region from an eye image using circular parameters can be improved via approximately too many approaches in literature but remain some portions under slightly unconstrained circumstances. In this study, we presented a Black Hole Algorithm (BHA) along the Canny edge detector and circular Hough transform-based optimization technique for circular parameter identification of iris segmentation. The iris boundary is discovered using the suggested segmentation approach and a computational model of the pixel value. The BHA looks for the central radius of the iris and pupil. The system uses MATLAB to test the CASIA-V3 database. The segmented images exhibit 98.71% accuracy. For all future access control applications, the segmentation-based BHA is effective at identifying the iris. The integration of the BHA with the Hough transforms and Canny edge detector is the main method by which the iris segmentation is accomplished. This novel technique improves the accuracy and effectiveness of iris segmentation, with potential uses in image analysis and biometric identification

Personal Identification Based on Live Iris Image Analysis

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