Robust iris image segmentation

An end to end Deep Neural Network for iris segmentation in unconstrained scenarios

Nowadays under human observations iris recognition is playing an essential role, it observed that there are some points are very essential for this method like accuracy, efficiency & processing time. There are several problems has been recorded for this kind of technique. Under previous researches, many concepts have been reported for iris-based segmentation, classification & feature extraction techniques. This research dealt with implementing a novel framework of robust iris segmentation utilizing computer vision technique, FFNN classifier & hybrid feature extraction technique. For the non-iris images method of the novel iris, segmentation has been proposed in this paper. The technique of novel iris segmentation based on 2 methods regards pupil segmentation, after that fusion of enhancing & shrinking visible contour has been framed for segmentation of iris through collaborating novel burden forcing for visible contour model. Moreover, then UN wrapped iris segmentation performed well with the technique of normalization non-circular iris. Whatever, for better iris segmentation research, utilized a proposed method of feature extraction under Discrete Wavelet Transform, letter utilized geometric & texture features of the segmented image. Achieved features have collaborated through a vector of a hybrid feature. For the reason of recognition, we are utilizing a vision algorithm with an FFNN classifier. This paper focused on the concepts related to a robust iris recognition framework using a vision algorithm. Daugman demodulates the yield of the Gabor channels to pack the information. It separates the stage data into four levels and for every conceivable quadrant in-plane to get a minimal 256-byte layout, which takes into consideration proficient capacity and correlation of irises. Experimental outputs have been described as the proposed robust iris segmentation method adopted greater accuracy on the challenging segmentation algorithm thousand databases.

OR-Skip-Net: Outer residual skip network for skin segmentation in non-ideal situations

The face region immediately surrounding one, or both, eyes is called the periocular region. This paper presents an iris segmentation algorithm for challenging periocular images based on a novel iterative ray detection segmentation scheme. Our goal is to convey some of the difficulties in extracting the iris structure in images of the eye characterized by variations in illumination, eye-lid and eye-lash occlusion, de-focus blur, motion blur, and low resolution. Experiments on the Face and Ocular Challenge Series (FOCS) database from the U.S. National Institute of Standards and Technology (NIST) emphasize the pros and cons of the proposed segmentation algorithm.KeywordsIris segmentationray detectionperiocular images

The Segment Anything Model (SAM) has made breakthroughs in the domain of image segmentation, attaining high-quality segmentation results using input prompts like points and bounding boxes. However, utilizing a pretrained SAM model for iris segmentation has not achieved the desired results. This is mainly due to the substantial disparity between natural images and iris images. To address this issue, we have developed SAM-Iris. First, we designed an innovative plug-and-play adapter called IrisAdapter. This adapter allows us to effectively learn features from iris images without the need to comprehensively update the model parameters while avoiding the problem of knowledge forgetting. Subsequently, to overcome the shortcomings of the pretrained Vision Transformer (ViT) encoder in capturing local detail information, we introduced a Convolutional Neural Network (CNN) branch that works in parallel with it. This design enables the model to capture fine local features of iris images. Furthermore, we adopted a Cross-Branch Attention mechanism module, which not only promotes information exchange between the ViT and CNN branches but also enables the ViT branch to integrate and utilize local information more effectively. Subsequently, we adapted SAM for iris image segmentation by incorporating a broader set of input instructions, which included bounding boxes, points, and masks. In the CASIA.v4-distance dataset, the E1, F1, mIoU, and Acc of our model are 0.34, 95.15%, 90.88%, and 96.49%; in the UBIRIS.v2 dataset, the E1, F1, mIoU, and Acc are 0.79, 94.08%, 88.94%, and 94.97%; in the MICHE dataset, E1, F1, mIoU, and Acc were 0.67, 93.62%, 88.66%, and 95.03%. In summary, this study has improved the accuracy of iris segmentation through a series of innovative methods and strategies, opening up new horizons and directions for large-model-based iris-segmentation algorithms.

Tumors contain both microscopic and macroscopic areas of necrotic cells. These have abnormal cell membrane permeability, which allows passage of large macromolecules such as antibodies into the cell. 131I-chTNT-1/B antibody (Cotara) is a novel, single administration agent designed to target tumor necrosis that has completed a Phase II clinical trial for recurrent glioblastoma multiforme (GBM), showing 9.3 month median overall survival for patients. Since 131I-chTNT-1/B antibody is a high-molecular-mass protein, it cannot easily pass from the systemic circulation through the blood-brain barrier (BBB) and thus can only be delivered locally by an intratumoral catheter. We have developed a human ME-180 cervical carcinoma xenograft tumor model in nude mice to characterize the targeting of chTNT-1/B to necrotic areas using near infrared (NIR) optical imaging. The use of NIR optical imaging is a powerful translational tool to monitor in vivo antibody targeting. chTNT-1/B was labeled with a NIR fluorescent dye and kinetics of antibody retention in tumors was measured by NIR optical imaging following intratumoral injection of the NIR-chTNT-1/B. NIR-chTNT-1/B was retained in ME180 tumors for a longer duration compared to NIR dye alone. Labeling of chTNT-1/B with a low dye/protein ratio was necessary to demonstrate maximum specific targeting to tumor necrosis. Evaluation of in vivo antibody targeting to tumors using NIR imaging may facilitate improvements for treatment of patients with malignant glioma with Cotara. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2454. doi:1538-7445.AM2012-2454

Serving as an effective texture description operator, local binary pattern (LBP) has been applied in the visible face recognition successfully. In order to enhance the recognition performance, the technology of face recognition depending on the near infrared (NIR) image has attracted extensive attention in the recent years. Although the characteristics of LBP have been researched in optical image thoroughly, the development of these do not catch enough attention in the NIR image at all. Therefore, in our paper, we study the characteristics of LBP from the following two aspects in NIR image. On one hand, we come to the probability distribution of various patterns of LBP in the NIR facial image. On the other hand, we discuss the influence to LBP caused by the illumination change in facial image.

Traditional iris segmentation methods give good results when the iris images are taken under ideal imaging conditions. However, the segmentation accuracy of an iris recognition system significantly influences its performance especially in nonideal iris images. This paper proposes a novel segmentation method for nonideal iris images. Two algorithms are proposed for pupil segmentation in iris images which are captured under visible and near infrared light. Then, a fusion of an expanding and a shrinking active contour is developed for iris segmentation by integrating a new pressure force to the active contour model. Thereafter, a noncircular iris normalization scheme is adopted to effectively unwrap the segmented iris. In addition, a novel method for closed eye detection is proposed. The proposed scheme is robust in finding the exact iris boundary and isolating the eyelids of the iris images. Experimental results on CASIA V4.0, MMU2, UBIRIS V1, and UBIRIS V2 iris databases indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris segmentation algorithms revealed considerable improvement in segmentation accuracy and recognition performance while being computationally more efficient.

Converting near-infrared (NIR) images into color images is a challenging task due to the different characteristics of visible and NIR images. Most methods of generating color images directly from a single NIR image are limited by the scene and object categories. In this paper, we propose a novel approach to recovering object colors from multi-spectral NIR images using gray information. The multi-spectral NIR images are obtained by a 2-CCD NIR/RGB camera with narrow NIR bandpass filters of different wavelengths. The proposed approach is based on multi-spectral NIR images to estimate a conversion matrix for NIR to RGB conversion. In addition to the multi-spectral NIR images, a corresponding gray image is used as a complementary channel to estimate the conversion matrix for NIR to RGB color conversion. The conversion matrix is obtained from the ColorChecker's 24 color blocks using polynomial regression and applied to real-world scene NIR images for color recovery. The proposed approach has been evaluated by a large number of real-world scene images, and the results show that the proposed approach is simple yet effective for recovering color of objects.

This paper proposes an accurate iris localization algorithm for the iris images acquired under near infrared (NIR) illuminations and having noise due to eyelids, eyelashes, lighting reflections, non-uniform illumination, eyeglasses and eyebrow hair etc. The two main contributions in the paper are an edge map generation technique for pupil boundary detection and an adaptive circular Hough transform (CHT) algorithm for limbic boundary detection, which not only make the iris localization more accurate but faster also. The edge map for pupil boundary detection is generated on intersection (logical AND) of two binary edge maps obtained using thresholding, morphological operations and Sobel edge detection, which results in minimal false edges caused by the noise. The adaptive CHT algorithm for limbic boundary detection searches for a set of two arcs in an image instead of a full circle that counters iris-occlusions by the eyelids and eyelashes. The proposed CHT and adaptive CHT implementations for pupil and limbic boundary detection respectively use a two-dimensional accumulator array that reduces memory requirements. The proposed algorithm gives the accuracies of 99.7% and 99.38% for the challenging CASIA-Iris-Thousand (version 4.0) and CASIA-Iris-Lamp (version 3.0) databases respectively. The average time cost per image is 905 msec. The proposed algorithm is compared with the previous work and shows better results.

This meta-analysis was conducted to evaluate the diagnostic value of near-infrared (NIR) or fluorescent indocyanine green (ICG) guided sentinel lymph node (SLN) mapping in gastric cancer (GC). This meta-analysis was registered at the PROSPERO. Clinical studies were retrieved from the electronic database Pubmed, Embase, Medline, Web of science, and the Cochrane Library. Quality assessment was conducted by an adapted checklist of QUADAS-2. A bivariate mixed-effects model was used to pool the data. Evaluation of articles quality, analysis for publication bias, summary receiver operator characteristic (SROC) curves, and meta-regression were also performed. Subgroup analysis was used to explain the heterogeneities. A total of 13 clinical studies (971 patients) were included. The NIR or fluorescent imaging (FI) involved infrared ray electronic endoscopy (IREE), infrared ray laparoscopic system (IRLS), and FI system. Significant evidence of heterogeneity was found for sensitivity and specificity (I 2 = 91.1% and I 2 = 98.2%), respectively. The pooled SLN sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were 0.94(95%CI: 0.80-0.99), 1.00(95%CI: 0.60-1.00), 34.0(95%CI: 9.25-125.29), 0.06(95%CI:0.02-0.22), and 252.50(95%CI: 94.93-671.61), respectively. Area under curve (AUC) of SROC curve was 1.00 (95%CI: 0.99-1.00), and the summary operating point (cut-off value) was SENS = 0.94(95%CI: 0.80-0.99) and SPEC = 1.00(95%CI: 0.60-1.00). Subgroup analysis showed that NIR imaging, imaging performed 20 minutes after intraoperative injection, preoperative injection (especially for FI imaging), stained with immunohistochemistry (IHC) (+hematoxylin-eosin [HE]), cT1 stage, submucosa injection (especially for cT1), mean number of SLN ≥ 5, study size > 26 were associated with higher SLN sensitivity. In terms of ICG concentration, diluted ICG concentration that 0.5 mg/mL (compared with 5 mg/mL) in NIR imaging and 0.05 mg/mL (compared with 0.5 mg/mL) in FI system showed higher sensitivities. However, the differences in tumor diameter (≤30 mm vs >30 mm), gastrectomy methods (opening vs laparoscopy), lymphadenectomy methods (LBD vs pick-up), and publication year (≥2010 vs <2010) did not achieve statistical significance. ICG combined with NIR or FI guided SLN mapping is technically feasible for GC. Based on the small sample size evidence, the IREE and IRLS devices may have higher sensitivity than FI in current clinical studies; and there may be an excessive ICG concentration used for current SLN mapping in GC. However, well-designed further studies with large sample size are needed to confirm the best procedure and suitable criteria. This meta-analysis was registered at PROSPERO. Clinical studies on this topic were retrieved from the electronic database Pubmed, Embase, Medline, Web of Science, and the Cochrane Library. The NIR or FI involved IREE, IRLS, and FI techniques. A total of 13 clinical studies (971 patients) were included. Based on the small sample size evidence, NIR or FI ICG guided SLN mapping in GC is technically feasible.

Near-infrared (NIR) brain imaging is one of the most promising techniques for brain research in recent years. As a significant supplement to the clinical imaging technique, such as CT and MRI, the NIR technique can achieve a fast, non-invasive, and low cost imaging of the brain, which is widely used for the brain functional imaging and hematoma detection. NIR imaging can achieve an imaging depth up to only several centimeters due to the reduced optical attenuation. The structure of the human brain is so particularly complex, from the perspective of optical detection, the measurement light needs go through the skin, skull, cerebrospinal fluid (CSF), grey matter, and white matter, and then reverses the order reflected by the detector. The more photons from the Depth of Interest (DOI) in brain the detector capture, the better detection accuracy and stability can be obtained. In this study, the Equivalent Signal to Noise Ratio (ESNR) was defined as the proportion of the photons from the DOI to the total photons the detector evaluated the best Source and Detector (SD) separation. The Monte-Carlo (MC) simulation was used to establish a multi brain layer model to analyze the distribution of the ESNR along the radial direction for different DOIs and several basic brain optical and structure parameters. A map between the best detection SD separation, in which distance the ESNR was the highest, and the brain parameters was established for choosing the best detection point in the NIR brain imaging application. The results showed that the ESNR was very sensitivity to the SD separation. So choosing the best SD separation based on the ESNR is very significant for NIR brain imaging application. It provides an important reference and new thinking for the brain imaging in the near infrared.

A fusion approach to unconstrained iris recognition

This research introduces a noise-resistant and computational efficient segmentation approach towards less constrained iris recognition. The UBIRIS.v2 database which contains close-up eye images taken under visible light is used to test the proposed algorithm. The proposed segmentation approach is based on a modified and fast Hough transform augmented with a newly developed strategy to define iris boundaries with multi-arcs and multi-lines. This optimized iris segmentation approach achieves excellent results in both accuracy (2% error) and execution speed (≤0.5s / image) using a 2.4GHz Intel® Q6600 processor with 2GB of RAM. This 2% error is an Exclusive-OR function in term of disagreeing pixels between the correct iris considered by the NICE.I committee and the segmented results from the proposed approach. The segmentation performance was independently evaluated in the “Noisy Iris Challenge Evaluation”, involving 97 participants worldwide, and ranking this research group in the top 6.

Generation of NIR Spectral Band from RGB Image with Wavelet Domain Spectral Extrapolation Generative Adversarial Network