Iris segmentation for visible wavelength and near infrared eye images

State-of-the-art iris segmentation algorithms exhibit poor performance for non-ideal data, which is mainly because of the noise such as low contrast, non-uniform illumination, reflections, and among others. To address this issue, a robust iris segmentation scheme is proposed that includes the following: First, a set of the Seed-pixels in a preprocessed eye image is marked adaptively. Next, a two-fold scheme based on a Circu-differential accumulator (CDA) and gray statistics is adopted to localize coarse iris region robustly. Notably, the proposed CDA has close resemblance with the Hough transform; however, it consumes relatively less memory and is free from thresholding as well. Similarly, pupillary boundary is localized, which is verified through an intensity test as well. Next, a refine estimate for the limbic boundary is extracted. After that, iris boundaries are regularized using the Fourier series. Finally, the eyelids are localized using a Para-differential accumulator (PDA), and eyelashes and reflections are also localized adaptively in the polar form of iris. Experimental results on the near infrared (NIR) and visible wavelength (VW) iris databases show that the proposed technique outperforms contemporary approaches.     

A non-circular iris localization algorithm using image projection function and gray level statistics

Iris recognition technology identifies an individual from its iris texture with great precision. A typical iris recognition system comprises eye image acquisition, iris segmentation, feature extraction, and matching. However, the system precision greatly depends on accurate iris localization in the segmentation module. In this paper, we propose a reliable iris localization algorithm. First, we locate a coarse eye location in an eye image using integral projection function (IPF). Next, we localize the pupillary boundary in a sub image using a reliable technique based on the histogram-bisection, image statistics, eccentricity, and object geometry. After that, we localize the limbic boundary using a robust scheme based on the radial gradients and an error distance transform. Finally, we regularize the actual iris boundaries using active contours. The proposed algorithm is tested on public iris databases: MMU V1.0, CASIA-IrisV1, and the CASIA-IrisV3-Lamp. Experimental results demonstrate superiority of the proposed algorithm over some of the contemporary techniques.     

Localization of iris in gray scale images using intensity gradient

A new method of iris localization based on intensity value analysis is proposed in this paper. Iris recognition systems are mainly dependent on the performance of iris localization processing. Steps after localization involve normalization, feature extraction and matching. These steps are based on the accuracy and efficiency of localization of iris in human eye images. In the proposed scheme, the inner boundary of iris is calculated by finding the pupil center and radius using two methods. In the first method, selected region is adaptively binarized and centroid of the region utilized for obtaining pupil parameters. Edges are processed to detect radius and center of pupil during the second method. For outer iris boundary, a band is calculated within which iris outer boundary lies. Signals in one dimension are picked up along radial direction within determined band at different angles. Three points with maximum gradient are selected from each signal. Redundant points are deleted using Mahalanobis distance and remaining points are used to obtain the outer circle of the iris. Points for upper and lower eyelids are found in the same way as the iris outer boundary. Selected points are then statistically fitted to make parabolas and lastly eyelashes are removed from the image to completely localize the iris. Experimental results show that proposed method is very efficient and accurate.     

A robust level set method based on local statistical information for noisy image segmentation

The paper presents an improved local region-based active contour model for image segmentation, which is robust to noise. A data fitting energy functional is defined in terms of curves and the energy terms which are based on the differences between the local average intensity and the global intensity means. Then the energy is incorporated into a level set variational formulation, from which a curve evolution equation is derived for energy minimization. And then the level set function is regularized by Gaussian filter to keep smooth and eliminate the re-initialization. By using the local statistical information, the proposed model can handle with noisy images. The proposed model is first presented as a two-phase level set formulation and then extended to a multi-phase one. Experimental results show desirable performances of the proposed model for both noisy synthetic and real images, especially with high level noise.     

Fast Poissonian image segmentation with a spatially adaptive kernel

The variational models with the goal of minimizing the local variation are widely used for the segmentation of the intensity inhomogeneous images recently. Local variation is a good measure for the images corrupted by Gaussian noise. However, in many applications such as astronomical imaging, electronic microscopy and positron emission tomography, Poisson noise often occurs in the observed images. To deal with this kind of images, we develop a novel segmentation model based on minimizing local generalized Kullback–Leibler (KL) divergence with a spatially adaptive kernel. A fast algorithm based on the split-Bregman method is proposed to solve the corresponding optimization problem. Numerical experiments for synthetic and real images demonstrate that the proposed model outperforms most of the current state-of-the-art methods in the present of Poisson noise.     

基于特定感兴趣区采样的虹膜定位改进算法

从虹膜图像特点出发,以瞳孔形心为辅助点实现对虹膜图像特定感兴趣区采样.并且根据虹膜内外圆半径的生理特点设定Hough变换半径参量,依据虹膜内外圆近似同心圆来筛选与虹膜外圆最匹配的Hough变换结果.该算法在主动避开眼皮,睫毛干扰的同时又显著降低了Hough变换的计算量.通过对中科院自动化所CASIA虹膜数据库108组图像的虹膜定位测试结果表明,该方法平均定位时间0.83 s,定位准确率98.9%.     

Automatic localization of pupil using eccentricity and iris using gradient based method

This paper presents a novel approach for the automatic localization of pupil and iris. Pupil and iris are nearly circular regions, which are surrounded by sclera, eyelids and eyelashes. The localization of both pupil and iris is extremely important in any iris recognition system. In the proposed algorithm pupil is localized using Eccentricity based Bisection method which looks for the region that has the highest probability of having pupil. While iris localization is carried out in two steps. In the first step, iris image is directionally segmented and a noise free region (region of interest) is extracted. In the second step, angular lines in the region of interest are extracted and the edge points of iris outer boundary are found through the gradient of these lines. The proposed method is tested on CASIA ver 1.0 and MMU Iris databases. Experimental results show that this method is comparatively accurate.     

Automatic leukocyte nucleus segmentation by intuitionistic fuzzy divergence based thresholding

The paper proposes a robust approach to automatic segmentation of leukocyte's nucleus from microscopic blood smear images under normal as well as noisy environment by employing a new exponential intuitionistic fuzzy divergence based thresholding technique. The algorithm minimizes the divergence between the actual image and the ideally thresholded image to search for the final threshold. A new divergence formula based on exponential intuitionistic fuzzy entropy has been proposed. Further, to increase its noise handling capacity, a neighborhood-based membership function for the image pixels has been designed. The proposed scheme has been applied on 110 normal and 54 leukemia (chronic myelogenous leukemia) affected blood samples. The nucleus segmentation results have been validated by three expert hematologists. The algorithm achieves an average segmentation accuracy of 98.52% in noise-free environment. It beats the competitor algorithms in terms of several other metrics. The proposed scheme with neighborhood based membership function outperforms the competitor algorithms in terms of segmentation accuracy under noisy environment. It achieves 93.90% and 94.93% accuracies for Speckle and Gaussian noises, respectively. The average area under the ROC curves comes out to be 0.9514 in noisy conditions, which proves the robustness of the proposed algorithm.     

基于深度玻尔兹曼模型的红外与可见光图像融合

为了克服红外与可见光图像融合时噪声干扰及易产生伪影导致目标轮廓不鲜明、对比度低的缺点,提出一种基于深度模型分割的图像融合方法.首先,采用深度玻尔兹曼机学习红外与可见光的目标和背景轮廓先验,构建轮廓的深度分割模型,通过Split Bregman迭代算法获取最优能量分割后的红外与可见光图像轮廓;然后再使用非下采样轮廓波变换对源图像进行分解,并针对所分割的背景轮廓采用结构相似度的规则进行系数组合;最后进行非下采样轮廓波反变换重构出融合图像.数值试验证明,该算法可以有效获取目标和背景轮廓均清晰的融合图像,融合结果不但具有较高的对比度,还能抑制噪声影响,具有有效性.     

光学小波包变换及其滤波器的研究

基于对光学小波变换必要条件的分析,提出光学小波包变换的概念.选出虹膜图库的联合最优小波包基,利用最优基的线性组合生成相应的复合光学小波包滤波器.将滤波器用于光电混合虹膜识别系统中对待识别输入进行小波包特征提取预处理,模拟结果不仅证明引入该滤波器可明显提升系统的识别效果,也证明了光学小波包变换提出的意义.     

Iris localization based on the Hough transform,a radial-gradient operator,and the gray-level intensity

The iris biometric recognizes a human based on his/her iris texture, which is a stable and unique feature for every individual. A typical iris biometric system performs better for the ideal data, which is acquired under controlled conditions. However, its performance degrades when localizing iris in non-ideal data containing the noisy issues, e.g., the non-uniform illumination, defocus, and non-circular iris boundaries. This study proposes a reliable algorithm to localize iris in such images robustly. First, a small region containing the coarse location of iris is localized. Next, the pupillary boundary is extracted within this small region using an iterative-scheme comprising an adaptive binarization and a pupil location verification test. Following that, the limbic boundary is localized by reusing the Hough accumulator. The iris location is also verified through a gray-level test. After that, the pupillary and limbic boundaries are regularized by applying an enhanced method comprising a Radial-gradient operator (RGO), an error-transform (ET), and the Fourier series. Experimental results, obtained on the CASIA-IrisV3, CASIA-IrisV4, MMU V1.0, and MMU(new) V2.0 iris databases, show superiority of the proposed technique over some of the contemporary techniques.     

Region-based SIFT approach to iris recognition

Traditional iris recognition systems transfer iris images to polar (or log-polar) coordinates and have performed very well on data that tends to have a centered gaze. The patterns of an iris are part of a 3-D structure that is captured as a two-dimensional (2-D) image and cooperative iris recognition systems are capable of correctly matching these 2-D representations of iris features. However, when the gaze of an eye changes with respect to the camera lens, many times the size, shape, and detail of iris patterns will change as well and cannot be matched to enrolled images using traditional methods. Additionally, the transformation of off-angle eyes to polar coordinates becomes much more challenging and noncooperative iris algorithms will require a different approach. The direct application of the scale-invariant feature transform (SIFT) method would not work well for iris recognition because it does not take advantage of the characteristics of iris patterns. We propose the region-based SIFT approach to iris recognition. This new method does not require polar transformation, affine transformation or highly accurate segmentation to perform iris recognition and is scale invariant. This method was tested on the iris challenge evaluation (ICE), WVU and IUPUI noncooperative databases and results show that the method is capable of cooperative and noncooperative iris recognition.     

基于C-V模型的改进快速水平集图像分割法

针对水平集方法计算复杂度高,无法满足实时系统要求的缺陷,提出一种改进的快速水平集算法。该算法对快速水平集算法进行简化,采用单链表表示轮廓曲线。利用C-V模型的二值拟合项来设计曲线演化的速度函数,保留了C-V模型的全局优化特性。还给出了一个基于单链表中轮廓点个数变化的水平集演化终止准则。该算法不仅明显提高了分割速度,且对噪声图像也能实现高效的分割。     

水下激光目标的统计对消分割法

水下激光目标的识别是一个崭新的研究领域,有许多问题需要解决,其中,目标分割是关键.水下激光图像中夹杂着严重的散斑噪声,受其影响,要识别水下激光目标,就要对图像进行有效的消噪,然后进行目标分割.本文依据具有相似统计特征的噪声可抵消图像中的相应噪声这一基理,结合小波变换和统计法,提出了一种水下激光目标的统计对消分割法,以去除噪声,提取目标.实验结果表明该方法是有效可行的.     

An improved Chan–Vese model by regional fitting for infrared image segmentation

In this paper, a regional fitting method is proposed for infrared image segmentation. In our model, the intensity of each pixel in a region is described by using the sum of the class center and the weighted variance of the region, in order to build energy function for encouraging the similarity pixels to be clustered together. The adoption of such way can thereby eliminate the issue associated with the drift of the class center that is existed in Chan–Vese model. Particularly, followed by incorporating energy function into the level set evolution without re-initialization framework, the variational formulation can force the level set function to be closed to object boundaries. Experiments on some representative and real infrared images have demonstrated that our model has higher performance of segmentation in comparison with Chan–Vese model without re-initialization, and some existing methods, including LBF and LCV model.     

Stochastic segmentation of severely degraded images using gibbs random fields

This paper deals with segmentation of noisy images using Gibbs random field (GRF) with an emphasis on modeling of the region process. For noisy image segmentation using the multi-level logistic (MLL) model with the second-order neighborhood system, which is commonly used in image processing, the segmentation performance is degraded significantly in case of low signal to noise ratio. By comparison with the Ising model that explains the magnetic properties of ferromagnetic material, it is evident that the characteristics of the region process modeled using the MLL model with the second-order neighborhood system are different in nature from the expected characteristics of a region. To solve this problem we added the term of the magnetic energy associated with the magnetic field of a spin system (or image) to the energy function of GRF. Using the modified model for the region process, the result of image segmentation was improved and did not depend on the cooling schedule in simulated annealing.     

图像分割中采用自适应三次B样条修饰轮廓线

大多数由像素灰度值或灰度相关参数获得图像轮廓线的方法由于受到图像噪声、量化误差以及灰阶的梯度分布等方面的影响 ,获得的边缘轮廓线是不光滑的 ,带有小而密集的不规则锯齿或毛刺 ,这不仅不符合实际情况 ,而且会给进一步的图像处理带来困难。为了获得连续光滑的轮廓线 ,提出了一种方法 :先以动态规划算法提取全局最优的轮廓线 ,然后用一种自适应三次B样条对获得的轮廓线进行修饰和平滑处理。该样条可根据轮廓线不同处的曲率变化情况 ,自适应地调整控制点的分布。在各类图像上的试验表明 ,该方法即有效的消除了轮廓线上的小锯齿 ,又保留了轮廓线的特征细节     

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.     

Hepatic vessel segmentation using variational level set combined with non-local robust statistics

Hepatic vessel segmentation is a challenging step in therapy guided by magnetic resonance imaging (MRI). This paper presents an improved variational level set method, which uses non-local robust statistics to suppress the influence of noise in MR images. The non-local robust statistics, which represent vascular features, are learned adaptively from seeds provided by users. K-means clustering in neighborhoods of seeds is utilized to exclude inappropriate seeds, which are obviously corrupted by noise. The neighborhoods of appropriate seeds are placed in an array to calculate the non-local robust statistics, and the variational level set formulation can be constructed. Bias correction is utilized in the level set formulation to reduce the influence of intensity inhomogeneity of MRI. Experiments were conducted over real MR images, and showed that the proposed method performed better on small hepatic vessel segmentation compared with other segmentation methods.     

融合改进直方图PDE和二维Tsallis熵多阈值SAR分割

针对合成孔径雷达(SAR)图像中存在大量的相干斑噪声,对SAR图像进行分割易出现分割不精、边缘模糊等问题,融合改进的直方图PDE和二维Tsallis熵多阈值,提出了一种SAR图像分割算法。根据PDE直方图均衡化方法,将图像去噪与图像增强加权融合,利用各自权值调整去噪项与图像增强项;同时将二维Tsallis熵单阈值分割方法扩展到多阈值分割, 建立基于多阈值的选取方法,并引入萤火虫算法来求解最优阈值对,实现了二维Tsallis熵多阈值对去噪增强SAR图像的有效分割。仿真结果表明:与其他3种分割算法相比,该文算法在处理噪声大、灰度差值小的图像时具有较高的分割精度,PRI至少提升2.53%、VOI降低8.48%、GCE降低11.14%。