Iris segmentation in non-ideal images using graph cuts

A non-ideal iris image segmentation approach based on graph cuts is presented that uses both the appearance and eye geometry information. A texture measure based on gradients is computed to discriminate between eyelash and non-eyelash regions, combined with image intensity differences between the iris, pupil, and the background (region surrounding the iris) are utilized as cues for segmentation. The texture and intensity distributions for the various regions are learned from histogramming and explicit sampling of the pixels estimated to belong to the corresponding regions. The image is modeled as a Markov Random Field and the energy minimization is achieved via graph cuts to assign each image pixel one of the four possible labels: iris, pupil, background, and eyelash. Furthermore, the iris region is modeled as an ellipse, and the best fitting ellipse to the initial pixel based iris segmentation is computed to further refine the segmented region. As a result, the iris region mask and the parameterized iris shape form the outputs of the proposed approach that allow subsequent iris recognition steps to be performed for the segmented irises. The algorithm is unsupervised and can deal with non-ideality in the iris images due to out-of-plane rotation of the eye, iris occlusion by the eyelids and the eyelashes, multi-modal iris grayscale intensity distribution, and various illumination effects. The proposed segmentation approach is tested on several publicly available non-ideal near infra red (NIR) iris image databases. We compare both the segmentation error and the resulting recognition error with several leading techniques, demonstrating significantly improved results with the proposed technique.     

基于主动轮廓线的虹膜定位方法

如何有效地定位虹膜在图象中的位置,是虹膜识别的关键问题之一。论文提出一种基于主动轮廓线的方法——Snake-Daugman(SD)法——来定位虹膜的边界,先用灰度投影的方法检测出瞳孔内的一点作为瞳孔的伪圆心,该点不要求一定在瞳孔中心附近,只要能落在瞳孔内部即可;然后以该点为中心,在其周围等间隔地取几个点作为初始的snake,按照snake的运行机制不断进化,直到虹膜的内边界为止;接着,计算进化后的snake的形心和snake上的控制点与该形心的距离,取其平均值作为瞳孔的半径,形心作为瞳孔的圆心,即可准确定位出虹膜内边界的位置;最后,以瞳孔的圆心为圆心,以瞳孔的半径为虹膜外边界的初始搜索半径,按照简化了的Daugman方法定位虹膜的外边界。实验表明,与常见的定位方法——Hough变换和Daugman的圆形检测算子——相比,文中的方法速度快,精度高,而且,该方法对瞳孔初始的伪圆心的要求并不高,鲁棒性更强。     

A new segmentation approach for iris recognition based on hand-held capture device

Iris segmentation is an important step for automatic iris recognition. This paper presents a new iris segmentation method for hand-held capture device. We use a geometrical method for pupil detection. The bottom point of pupil is used as the reference point for pupil localization because it is insensitive to pupil dilation and not affected by the top eyelid or eyelashes. To decrease computational cost, the outer (or limbus) boundary of iris is localized based on shrunk image using Hough transform and modified Canny edge detector. The lower part of iris pattern is used for recognition in order to reduce the occlusion by eyelashes and eyelids. Experimental results demonstrate that the proposed method has an encouraging performance.     

Effectiveness evaluation of iris segmentation by using geodesic active contour (GAC)

A novel iris segmentation technique based on active contour is proposed in this paper. Our approach uses innovative algorithms, including two important ones, pupil segmentation and iris circle calculation. With our algorithms, we are able to find the center position and radius of pupil correctly and segment the iris precisely. The accuracy of our proposed method for ICE dataset is around 92% and also reached high accuracy level of 79% for UBIRIS. Our results demonstrate that the proposed iris segmentation method can perform well with high accuracy and better efficacy for Iris segmentation in images. Through a relatively high-performance algorithm to further cut up the round out the picture of the pupil conversion cutting growth square picture in order to make the judgment for biometric applications.

     

Automated Segmentation of Left Ventricle Using Local and Global Intensity Based Active Contour and Dynamic Programming

The aim of this work is to develop an improved region based active contour and dynamic programming based method for accurate segmentation of left ventricle (LV) from multi-slice cine short axis cardiac magnetic resonance (MR) images. Intensity inhomogeneity and weak object boundaries present in MR images hinder the segmentation accuracy. The proposed active contour model driven by a local Gaussian distribution fitting (LGDF) energy and an auxiliary global intensity fitting energy improves the accuracy of endocardial boundary detection. The weightage of the global energy fitting term is dynamically adjusted using a spatially varying weight function. Dynamic programming scheme proposed for the segmentation of epicardium considers the myocardium probability map and a distance weighted edge map in the cost matrix. Radial distance weighted technique and conical geometry are employed for segmenting the basal slices with left ventricle outflow tract (LVOT) and most apical slices. The proposed method is validated on a public dataset comprising 45 subjects from medical image computing and computer assisted interventions (MICCAI) 2009 segmentation challenge. The average percentage of good endocardial and epicardial contours detected is about 99%, average perpendicular distance of the detected good contours from the manual reference contours is 1.95 mm, and the dice similarity coefficient between the detected contours and the reference contours is 0.91. Correlation coefficient and the coefficient of determination between the ejection fraction measurements from manual segmentation and the automated method are respectively 0.978 1 and 0.956 7, for LV mass these values are 0.924 9 and 0.855 4. Statistical analysis of the results reveals a good agreement between the clinical parameters determined manually and those estimated using the automated method.     

Frame detection using gradients fuzzy logic and morphological processing for distant color eye images in an intelligent iris recognition system

The capture of an eye image with the occlusion of spectacles in a non-cooperative environment compromises the accuracy in identifying a person in an iris recognition system. This is due to the obstruction of the iris by the frame which tends to produce an incorrect estimation of the initial center of the iris and the pupil during the iris segmentation process. In addition, it also causes incorrect localization of the upper eyelid during the process of iris segmentation and sometimes, the edges of the frame are wrongly identified as the edges of the upper eyelid. A frame detection method which involves the combination of two gradients, namely the Sobel operator and high pass filter, followed by fuzzy logic and the dilation operation of morphological processing is proposed to identify the frame on the basis of different frame factors in the capture of a distant eye image. In addition, a different color space is applied and only a single channel is used for the process of frame detection. The proposed frame detection method provides the highest frame detection rate compared to the other methods, with a detection rate of more than 80.0%. For the accuracy of the iris localization, upper eyelid localization and iris recognition system, the proposed method gives more than 96.5% accuracy compared to the other methods. The index of decidability showed that the proposed method gives more than 2.35 index compared to the existing methods.     

A disk expansion segmentation method for ultrasonic breast lesions

Automatically extracting lesion boundaries in ultrasound images is difficult due to the variance in shape and interference from speckle noise. An effective scheme of removing speckle noise can facilitate the segmentation of ultrasonic breast lesions, which can be performed with an iterative disk expansion method. In this study, a disk expansion segmentation method is proposed to semi-automatically find lesion contours in ultrasonic breast image. To evaluate the performance of the proposed method, the simulations with seven types of cysts, three in vitro phantom images and 10 clinical breast images are introduced. The mean normalized true positive area overlap between simulated contours and contours obtained by the proposed method is over 85% in simulation results. A strong correlation exists between physicians’ manual delineations and detected contours in clinical breast images. In addition, the method is also verified to be able to simultaneously contour multiple lesions in a single image. In comparison with the conventional active contour model, our proposed method does not require any initial seed within a lesion and thus, it is more convenient and applicable.     

Iris localization in frontal eye images for less constrained iris recognition systems

Commercial iris recognition systems do not perform well for non-ideal data, because their iris localization algorithms are specifically developed for controlled data. This paper presents a robust iris localization algorithm for less constrained data. It includes: (i) suppressing specular reflections; (ii) localizing the iris inner (pupil circle) and outer (iris circle) boundaries in a two-phase strategy. In the first phase, we use Hough transform, gray level statistics, adaptive thresholding, and a geometrical transform to extract the pupil circle in a sub-image containing a coarse pupil region. After that, we localize iris circle in a sub-image centered at the pupil circle. However, if the first phase fails, the second phase starts, where first we localize a coarse iris region in the eye image. Next, we extract pupil circle within the coarse iris region by reusing procedure of first phase. Following that, we localize iris circle. In either of the two phases, we validate the pupil location by using an effective occlusion transform; and (iii) regularizing the iris circular boundaries by using radial gradients and the active contours. Experimental results show that the proposed technique is tolerant to off-axis eye images, specular reflections, non-uniform illumination; glasses, contact lens, hair, eyelashes, and eyelids occlusions.     

A new iris segmentation method for non-ideal iris images

Many researchers have studied iris recognition techniques in unconstrained environments, where the probability of acquiring non-ideal iris images is very high due to off-angles, noise, blurring and occlusion by eyelashes, eyelids, glasses, and hair. Although there have been many iris segmentation methods, most focus primarily on the accurate detection with iris images which are captured in a closely controlled environment. This paper proposes a new iris segmentation method that can be used to accurately extract iris regions from non-ideal quality iris images. This research has following three novelties compared to previous works; firstly, the proposed method uses AdaBoost eye detection in order to compensate for the iris detection error caused by the two circular edge detection operations; secondly, it uses a color segmentation technique for detecting obstructions by the ghosting effects of visible light; and thirdly, if there is no extracted corneal specular reflection in the detected pupil and iris regions, the captured iris image is determined as a “closed eye” image.     

PI-Unet：异质虹膜精确分割神经网络模型的研究

在虹膜识别系统中,异质虹膜图像（可见光和红外图像）的分割是最重要且最有挑战性的一个任务,该任务的难点在于针对异质虹膜图像,要同时兼顾虹膜分割的准确率和快速性。提出了适用于异质虹膜分割的神经网络模型PI-Unet（Precise Iris Unet）以及用于训练该网络模型的数据增强方法和损失函数。对PI-Unet的Encoder和Decoder进行实验探索,得出能同时兼顾准确率和快速性的网络结构,将提出的数据增强方法和损失函数用于该网络进行训练,在CASIA-iris-intervel-v4和UBIRIS.v2虹膜图像数据库上测试该网络的准确率、参数量和计算量。测试结果表明,提出的数据增强方法和损失函数能有效提高异质虹膜分割准确率,PI-Unet与传统虹膜分割算法和其他虹膜分割神经网络相比,对异质虹膜图像的分割准确率更高且参数量和计算量更少,能够适用于低性能的边缘计算设备。     

Robust iris segmentation on uncalibrated noisy images using mathematical morphology

This paper proposes a new approach for fast iris segmentation that relies on the closed nested structures of iris anatomy (the sclera is brighter than the iris, and the iris is brighter than the pupil) and on its polar symmetry. The described method applies mathematical morphology for polar/radial-invariant image filtering and for circular segmentation using shortest paths from generalized grey-level distances. The proposed algorithm obtained good results on the NICE-I contest and showed a very robust behavior, especially when dealing with half-closed eyes, different skin colours/illumination or subjects wearing glasses.     

基于活动轮廓的多分辨率自适应图像分割

本文在活动轮廓模型的基础上,提出了一种自适应图像分割方法,引入了新的图象统计信息、梯度信息有关的加权外部能量,使得分割结果与模型的初始位置无关,不受噪声影响;利用ACD方法使模型自适应地改变其拓扑结构;为了提高图象分的速度和鲁棒性,提出了多分辨率图象分割算法,利用该方法对一些形状、拓扑结构复杂的物体进行了分割实验,结果验证了该方法有效性。     

Active contours with selective local or global segmentation: A new formulation and level set method

A novel region-based active contour model (ACM) is proposed in this paper. It is implemented with a special processing named Selective Binary and Gaussian Filtering RegularizedLevel Set(SBGFRLS) method, which first selectively penalizes the level set function to be binary, and then uses a Gaussian smoothing kernel to regularize it. The advantages of our method are as follows. First, a new region-based signed pressure force (SPF) function is proposed, which can efficiently stop the contours at weak or blurred edges. Second, the exterior and interior boundaries can be automatically detected with the initial contour being anywhere in the image. Third, the proposed ACM with SBGFRLS has the property of selective local or global segmentation. It can segment not only the desired object but also the other objects. Fourth, the level set function can be easily initialized with a binary function, which is more efficient to construct than the widely used signed distance function (SDF). The computational cost for traditional re-initialization can also be reduced. Finally, the proposed algorithm can be efficiently implemented by the simple finite difference scheme. Experiments on synthetic and real images demonstrate the advantages of the proposed method over geodesic active contours (GAC) and Chan–Vese (C–V) active contours in terms of both efficiency and accuracy.     

Detecting precise iris boundaries by circular shortest path method

The problem of detecting precise pupil border in eye image given its initial circular approximation is addressed with circular shortest path method. Brightness gradient direction is employed to choose image pixels, which may belong to pupil boundary. Using initial approximate circles allows the method to work in a narrow ring, which contains only single pupil contour. Under these conditions the method allows to correctly handle almost all images used for iris recognition tasks and appears to be more precise than human expert in marking the pupil border. The method was tested with public domain iris databases, containing more than 80000 images totally. Experiments show that refinement of pupil border increases precision of iris recognition.     

Optimization of the threshold value for segmentation of objects of a priori known forms

This paper proposes a method of finding the threshold value for segmenting objects of a priori known form. The problem is formulated as an optimization problem. The segmentation of the human pupil (the first stage of the iris segmentation in the iris recognition process) is given as an example of implementing the approach.     

DADCNet: Dual attention densely connected network for more accurate real iris region segmentation

Most existing performance evaluation standards for iris segmentation algorithms, such as the typical recall, precision, and F-measure (RPF-measure) protocol, are based on a pixel-to-pixel comparison between the mask image obtained after segmentation and the corresponding ground truth (GT) image. However, one of the most important problems is that if the published GT images have errors when locating the iris region, then the reference value of these evaluation indicators will be reduced, which is not conducive to the development of iris recognition technology. To address this problem, this paper proposes to use a mask image segmented by a deep learning method to replace the corresponding GT image. The main work of this paper is as follows. First, a dual attention densely connected network (DADCNet) containing two attention modules and an improved skip connection is proposed to segment the real iris region more accurately than the corresponding GT image. Second, the recognition performance of the two input classes obtained from the mask image after DADCNet segmentation and the corresponding published GT image in the same recognition network is utilized to further show that the former is more reliable in positioning the real iris than the latter. To make the proposed network more convincing, extensive experiments are conducted on four representative and challenging iris databases, which is obtained under different spectral conditions. These results show that the proposed DADCNet achieves state-of-the-art performance and that the mask image obtained after DADCNet segmentation can replace the published corresponding GT image.     

Noisy iris segmentation with boundary regularization and reflections removal

The paper presents an innovative algorithm for the segmentation of the iris in noisy images, with boundaries regularization and the removal of the possible existing reflections. In particular, the method aims to extract the iris pattern from the eye image acquired at the visible wavelength, in an uncontrolled environment where reflections and occlusions can also be present, on-the-move and at variable distance. The method achieves the iris segmentation by the following three main steps. The first step locates the centers of the pupil and the iris in the input image. Then two image strips containing the iris boundaries are extracted and linearizated. The last step locates the iris boundary points in the strips and it performs a regularization operation by achieving the exclusion of the outliers and the interpolation of missing points. The obtained curves are then converted into the original image space in order to produce a first segmentation output. Occlusions such as reflections and eyelashes are then identified and removed from the final area of the segmentation. Results indicate that the presented approach is effective and suitable to deal with the iris acquisition in noisy environments. The proposed algorithm ranked seventh in the international Noisy Iris Challenge Evaluation (NICE.I).     

融合超像素和CNN的CT图像分割方法

提出了一种融合超像素和CNN的CT图像器官主动轮廓分割方法。用超像素SLIC方法将CT图像网格化并分配标签;将网格化后图像作为数据集训练CNN网络分割出器官（如肝脏、肺部等）边界超像素,并将这些超像素的种子点连接成为粗分割边界;将粗分割边界作为初始轮廓,进行模糊主动轮廓分割得到CT图像中器官的边界。经过实验对比,该方法对肺部CT图像的分割平均DC系数达到97%、平均ASD系数达到1.23 mm。在肝脏CT图像方面与参考算法进行相比,在保证分割精度的前提下,VOE系数平均减少1%,切片图像的分割时间平均提高10 s。     

A new effective and powerful medical image segmentation algorithm based on optimum path snakes

Novel segmentation methods based on models of deformable active contours are constantly proposed and validated in different fields of knowledge, with the aim to make the detection of the regions of interest standard. This paper propose a new method called Optimum Path Snakes (OPS), a new adaptive algorithm and free of parameters to define the total energy of a active contour model with automatic initialization and stop criteria. In the experimental assessment, the OPS is compared against some approaches commonly used in the following fields, such as vector field convolution, gradient vector flow, and other specialists methods for lung segmentation using thorax computed tomography images. The segmentation of regions with stroke was carried out with methods based on region growing, watershed and a specialist level set approach. Statistical validations metrics using Dice coefficient (DC) and Hausdorff distance (HD) were also evaluated, as well as the processing time. The results showed that the OPS is a promising tool for image segmentation, presenting satisfactory results for DC and HD, and, many times, superior to the other algorithms it was compared with, including those generated by specialists. Another advantage of the OPS is that it is not restricted to specific types of images, neither applications.