Detection of the Optic Nerve Head in Fundus Images of the Retina Using the Hough Transform for Circles

Detection of the optic nerve head (ONH) is a key preprocessing component in algorithms for the automatic extraction of the anatomical structures of the retina. We propose a method to automatically locate the ONH in fundus images of the retina. The method includes edge detection using the Sobel operators and detection of circles using the Hough transform. The Hough transform assists in the detection of the center and radius of a circle that approximates the margin of the ONH. Forty images of the retina from the Digital Retinal Images for Vessel Extraction (DRIVE) dataset were used to test the performance of the proposed method. The center and boundary of the ONH were independently marked by an ophthalmologist for evaluation. Free-response receiver operating characteristics (FROC) analysis as well as measures of distance and overlap were used to evaluate the performance of the proposed method. The centers of the ONH were detected with an average distance of 0.36 mm to the corresponding centers marked by the ophthalmologist; the detected circles had an average overlap of 0.73 with the boundaries of the ONH drawn by the ophthalmologist. FROC analysis indicated a sensitivity of detection of 92.5% at 8.9 false-positives per image. With an intensity-based criterion for the selection of the circle and a limit of 40 pixels (0.8 mm) on the distance between the center of the detected circle and the manually identified center of the ONH, a successful detection rate of 90% was obtained with the DRIVE dataset.     

Retinal vessel segmentation using a multi-scale medialness function

Recently, automated segmentation of retinal vessels in optic fundus images has been an important focus of much research. In this paper, we propose a multi-scale method to segment retinal vessels based on a weighted two-dimensional (2D) medialness function. The results of the medialness function are first multiplied by the eigenvalues of the Hessian matrix. Next, centerlines of vessels are extracted using noise reduction and reconnection procedures. Finally, vessel radii are estimated and retinal vessels are segmented. The proposed method is evaluated and compared with several recent methods using images from the DRIVE and STARE databases.     

Application of Morphological Bit Planes in Retinal Blood Vessel Extraction

The appearance of the retinal blood vessels is an important diagnostic indicator of various clinical disorders of the eye and the body. Retinal blood vessels have been shown to provide evidence in terms of change in diameter, branching angles, or tortuosity, as a result of ophthalmic disease. This paper reports the development for an automated method for segmentation of blood vessels in retinal images. A unique combination of methods for retinal blood vessel skeleton detection and multidirectional morphological bit plane slicing is presented to extract the blood vessels from the color retinal images. The skeleton of main vessels is extracted by the application of directional differential operators and then evaluation of combination of derivative signs and average derivative values. Mathematical morphology has been materialized as a proficient technique for quantifying the retinal vasculature in ocular fundus images. A multidirectional top-hat operator with rotating structuring elements is used to emphasize the vessels in a particular direction, and information is extracted using bit plane slicing. An iterative region growing method is applied to integrate the main skeleton and the images resulting from bit plane slicing of vessel direction-dependent morphological filters. The approach is tested on two publicly available databases DRIVE and STARE. Average accuracy achieved by the proposed method is 0.9423 for both the databases with significant values of sensitivity and specificity also; the algorithm outperforms the second human observer in terms of precision of segmented vessel tree.     

基于眼底结构特征的彩色眼底图像视盘定位方法

眼底图像中视盘的大小和形状等参数是判断眼底病变的重要辅助参数,视盘的检测和定位对眼科疾病的计算机辅助诊断具有重要意义。提出一种基于眼底结构特征的彩色眼底图像视盘定位方法。首先采用基于低帽运算的方法,提取眼底图像中的静脉血管;然后基于静脉血管的结构特征,采用最小二乘抛物线拟合法初步定位视盘;最后通过滑动窗口灰度扫描的方法,精确定位视盘。在4个公开的眼底图像数据库(DRIVE、DIABETED0、STARE和MESSIDOR)中,对所提出的视盘定位方法进行测试,定位准确率分别为100%、98.6%、93.8%、99.75%,验证了该方法的准确性和通用性。     

Estimation of the breast skin-line in mammograms using multidirectional Gabor filters

Segmentation of the breast region is a fundamental step in any system for computerized analysis of mammograms. In this work, we propose a novel procedure for the estimation of the breast skin-line based upon multidirectional Gabor filtering. The method includes an adaptive values-of-interest (VOI) transformation, extraction of the skin–air ribbon by Otsu's thresholding method and the Euclidean distance transform, Gabor filtering with 18 real kernels, and a step for suppression of false edge points using the magnitude and phase responses of the filters. On a test set of 361 images from different acquisition modalities (screen-film and full-field digital mammograms), the average Hausdorff and polyline distances obtained were 2.85 mm and 0.84 mm, respectively, with reference to the ground-truth boundaries provided by an expert radiologist. When compared with the results obtained by other state-of-the-art methods on the same set of images and with respect to the same ground-truth boundaries, our method mostly outperformed the other approaches. The results demonstrate the effectiveness and robustness of the proposed algorithm.     

On the comparison of FROC curves in mammography CAD systems

We present a novel method for assessing the performance of computer-aided detection systems on unseen cases at a given sensitivity level. The sampling error introduced when training the system on a limited data set is captured as the uncertainty in determining the system threshold that would yield a certain predetermined sensitivity on unseen data sets. By estimating the distribution of system thresholds, we construct a confidence interval for the expected number of false positive markings per image at a given sensitivity. We present two alternative procedures for estimating the probability density functions needed for the construction of the confidence interval. The first is based on the common assumption of Poisson distributed number of false positive markings per image. This procedure also relies on the assumption of independence between false positives and sensitivity, an assumption that can be relaxed with the second procedure, which is nonparametric. The second procedure uses the bootstrap applied to the data generated in the leave-one-out construction of the FROC curve, and is a fast and robust way of obtaining the desired confidence interval. Standard FROC curve analysis does not account for the uncertainty in setting the system threshold, so this method should allow for a more fair comparison of different systems. The resulting confidence intervals are surprisingly wide. For our system a conventional FROC curve analysis yields 0.47 false positive markings per image at 90% sensitivity. The 90% confidence interval for the number of false positive markings per image is (0.28, 1.02) with the parametric procedure and (0.27, 1.04) with the nonparametric bootstrap. Due to its computational simplicity and its allowing more fair comparisons between systems, we propose this method as a complement to the traditionally presented FROC curves.     

基于高斯过程和Radon变换的视网膜血管管径测量

视网膜血管管径的异常变化与糖尿病、高血压等心脑血管疾病发展进程息息相关,眼底图像中视网膜血管信息的提取是计算机辅助分析和诊断相关疾病的重要步骤。本研究提出一种视网膜血管管径测量方法。首先对眼底图像进行图像预处理,然后基于高斯过程和Radon变换准确跟踪血管中心线和方向,最后利用二维高斯过程回归技术测量血管管径。在DRIVE和STARE这两个眼底图像数据库中进行测试。结果表明不论是对于曲率较小的近似直线型血管段、曲率较大的弯曲型血管段,还是对于管径发生变化的血管段,本文方法都能较好地检测出血管管径宽度,且标准差低、运算速度快。     

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images

Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively.     

Maximum likelihood analysis of free-response receiver operating characteristic (FROC) data

Receiver operating characteristic (ROC) methodology is widely used in evaluating medical imaging modalities. While appropriate in some cases, it has several drawbacks when the detection task, e.g., nodule detection, involves localizing the abnormality. Free-response receiver operating characteristic (FROC) methodology offers a more natural framework to describe observer performance in such studies and has other advantages. Due to the lack of a statistical analysis procedure comparable to the maximum likelihood procedure (ROCFIT program) available for ROC studies, the FROC method has not gained widespread acceptance. This work presents and solves a two parameter model for the statistical analysis of FROC data. The model assumes that the probability density of the signal stimuli is normally distributed, as is the probability density for producing one or more false positives per image. A program (FROCFIT) is described for estimating the parameters and their uncertainties from experimental data. An index of performance is proposed to quantify observer performance in FROC experiments. Application of this methodology to several FROC data sets produced good to excellent fits.     

Selective enhancement filters for nodules,vessels, and airway walls in two- and three-dimensional CT scans

Computer-aided diagnostic (CAD) schemes have been developed to assist radiologists in the early detection of lung cancer in radiographs and computed tomography (CT) images. In order to improve sensitivity for nodule detection, many researchers have employed a filter as a preprocessing step for enhancement of nodules. However, these filters enhance not only nodules, but also other anatomic structures such as ribs, blood vessels, and airway walls. Therefore, nodules are often detected together with a large number of false positives caused by these normal anatomic structures. In this study, we developed three selective enhancement filters for dot, line, and plane which can simultaneously enhance objects of a specific shape (for example, dot-like nodules) and suppress objects of other shapes (for example, line-like vessels). Therefore, as preprocessing steps, these filters would be useful for improving the sensitivity of nodule detection and for reducing the number of false positives. We applied our enhancement filters to synthesized images to demonstrate that they can selectively enhance a specific shape and suppress other shapes. We also applied our enhancement filters to real two-dimensional (2D) and three-dimensional (3D) CT images to show their effectiveness in the enhancement of specific objects in real medical images. We believe that the three enhancement filters developed in this study would be useful in the computerized detection of cancer in 2D and 3D medical images.     

A Metric for Reducing False Positives in the Computer-Aided Detection of Breast Cancer from Dynamic Contrast-Enhanced Magnetic Resonance Imaging Based Screening Examinations of High-Risk Women

Magnetic resonance imaging (MRI)-enabled cancer screening has been shown to be a highly sensitive method for the early detection of breast cancer. Computer-aided detection systems have the potential to improve the screening process by standardizing radiologists to a high level of diagnostic accuracy. This retrospective study was approved by the institutional review board of Sunnybrook Health Sciences Centre. This study compares the performance of a proposed method for computer-aided detection (based on the second-order spatial derivative of the relative signal intensity) with the signal enhancement ratio (SER) on MRI-based breast screening examinations. Comparison is performed using receiver operating characteristic (ROC) curve analysis as well as free-response receiver operating characteristic (FROC) curve analysis. A modified computer-aided detection system combining the proposed approach with the SER method is also presented. The proposed method provides improvements in the rates of false positive markings over the SER method in the detection of breast cancer (as assessed by FROC analysis). The modified computer-aided detection system that incorporates both the proposed method and the SER method yields ROC results equal to that produced by SER while simultaneously providing improvements over the SER method in terms of false positives per noncancerous exam. The proposed method for identifying malignancies outperforms the SER method in terms of false positives on a challenging dataset containing many small lesions and may play a useful role in breast cancer screening by MRI as part of a computer-aided detection system.     

基于区域建议策略的视盘定位方法

视盘定位对利用眼底图像进行眼科疾病的计算机辅助诊疗十分重要。提出一种基于区域建议策略的视盘定位方法。首先,将眼底图像从像素域映射到特征域,在得到的特征图上利用区域建议策略生成视盘的初始候选区域;然后,按照一定准则对候选区域进行采样,构建全连接层对其进行深层特征提取,并利用损失函数的约束实现候选区域的位置精修;最后,通过置信度阈值的过滤对视盘可见性进行判断,若视盘可见,则将置信度最大的候选区域中心作为该眼底图像的视盘坐标,从而实现视盘的正确定位。在3个公开的眼底图像数据库(DRIVE(40张)、MESSIDOR(1 200张)和STARE(400张))中进行实验,定位准确率分别为100%、99.9%和98.8%。实验证明,该方法能够实现视盘的准确、快速、鲁棒定位,优于现有的视盘定位方法,且预先进行视盘可见性的判断更符合实际应用的要求,能够辅助眼底疾病的诊断处理。     

基于Gabor滤波器的糖尿病视网膜新生血管检测

目的:采用Gabor滤波器实现眼底图像中新生血管检测,帮助医生准确确定糖尿病视网膜病变的分期。 方法:对眼底图像进行预处理,并使用不同尺度参数和方向参数Gabor滤波器作用于预处理图像,并在尺度参数确定的情况下取各方向输出结果的最大值作为最后Gabor滤波器的输出。 结果:对比分析不同尺度参数的Gabor滤波器的结果,发现小尺度参数的Gabor滤波器在新生血管部分具有较强的输出。 结论:本研究提出的Gabor滤波器可以很好地区分眼底图像中正常血管与新生血管结构。     

Computerized Breast Mass Detection Using Multi-Scale Hessian-Based Analysis for Dynamic Contrast-Enhanced MRI

This study aimed to investigate a computer-aided system for detecting breast masses using dynamic contrast-enhanced magnetic resonance imaging for clinical use. Detection performance of the system was analyzed on 61 biopsy-confirmed lesions (21 benign and 40 malignant lesions) in 34 women. The breast region was determined using the demons deformable algorithm. After the suspicious tissues were identified by kinetic feature (area under the curve) and the fuzzy c-means clustering method, all breast masses were detected based on the rotation-invariant and multi-scale blob characteristics. Subsequently, the masses were further distinguished from other detected non-tumor regions (false positives). Free-response operating characteristics (FROC) curve and detection rate were used to evaluate the detection performance. Using the combined features, including blob, enhancement, morphologic, and texture features with 10-fold cross validation, the mass detection rate was 100 % (61/61) with 15.15 false positives per case and 91.80 % (56/61) with 4.56 false positives per case. In conclusion, the proposed computer-aided detection system can help radiologists reduce inter-observer variability and the cost associated with detection of suspicious lesions from a large number of images. Our results illustrated that breast masses can be efficiently detected and that enhancement and morphologic characteristics were useful for reducing non-tumor regions.     

An improved matched filter for blood vessel detection of digital retinal images

The matched filter has been widely used in the detection of blood vessels of the human retina digital image. In this paper, the matched filter response to the detection of blood vessels is increased by proposing better filter parameters. These filter parameters are found by using an optimization procedure on 20 retina images of the DRIVE database. Comparisons with other approaches show that the matched filter that uses the newly found parameters outperforms the matched filter that uses the classical filter parameters as well as some vessel detection techniques. A technique is also discussed to find the best threshold value for the continuous matched filter output image and hence the best segmented vessel image.     

Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography

In this study, we investigated a pattern-recognition technique based on an artificial neural network (ANN), which is called a massive training artificial neural network (MTANN), for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography (CT) images. The MTANN consists of a modified multilayer ANN, which is capable of operating on image data directly. The MTANN is trained by use of a large number of subregions extracted from input images together with the teacher images containing the distribution for the "likelihood of being a nodule." The output image is obtained by scanning an input image with the MTANN. The distinction between a nodule and a non-nodule is made by use of a score which is defined from the output image of the trained MTANN. In order to eliminate various types of non-nodules, we extended the capability of a single MTANN, and developed a multiple MTANN (Multi-MTANN). The Multi-MTANN consists of plural MTANNs that are arranged in parallel. Each MTANN is trained by using the same nodules, but with a different type of non-nodule. Each MTANN acts as an expert for a specific type of non-nodule, e.g., five different MTANNs were trained to distinguish nodules from various-sized vessels; four other MTANNs were applied to eliminate some other opacities. The outputs of the MTANNs were combined by using the logical AND operation such that each of the trained MTANNs eliminated none of the nodules, but removed the specific type of non-nodule with which the MTANN was trained, and thus removed various types of non-nodules. The Multi-MTANN consisting of nine MTANNs was trained with 10 typical nodules and 10 non-nodules representing each of nine different non-nodule types (90 training non-nodules overall) in a training set. The trained Multi-MTANN was applied to the reduction of false positives reported by our current computerized scheme for lung nodule detection based on a database of 63 low-dose CT scans (1765 sections), which contained 71 confirmed nodules including 66 biopsy-confirmed primary cancers, from a lung cancer screening program. The Multi-MTANN was applied to 58 true positives (nodules from 54 patients) and 1726 false positives (non-nodules) reported by our current scheme in a validation test; these were different from the training set. The results indicated that 83% (1424/1726) of non-nodules were removed with a reduction of one true positive (nodule), i.e., a classification sensitivity of 98.3% (57 of 58 nodules). By using the Multi-MTANN, the false-positive rate of our current scheme was improved from 0.98 to 0.18 false positives per section (from 27.4 to 4.8 per patient) at an overall sensitivity of 80.3% (57/71).     

眼底图像中硬性渗出物检测算法

目的利用眼底图像中硬性渗出物(hard exudates,HE)的亮度与边缘特征,提出一种基于Canny边缘检测算法与形态学重构相结合的HE自动检测方法,以解决目前算法灵敏度低、检测结果中视盘和血管的干扰等问题,对糖尿病视网膜病变(diabetic retinopathy,DR)的自动筛查具有重要意义。方法检测算法包括4个步骤。步骤一,图像预处理,主要包括RGB通道选取、基于形态学的图像对比度增强。步骤二,视网膜图像关键结构的消除,利用基于Gabor滤波的血管分割方法,消除血管边缘对HE检测的影响。将本文视杯分割算法应用在眼底图像红色通道上实现视盘自动分割,消除视盘及其边缘对HE检测的影响。步骤三,利用改进的Canny边缘检测算法和形态学重构方法对HE进行提取。步骤四,基于形态学的图像后处理,消除眼底图像边缘部分假阳性区域。最后利用该算法测试公开数据库中的40幅图像(35幅HE病变图像,5幅正常图像)。结果该算法对基于病变的灵敏性(sensitivity,SE)和阳性预测值(positive predictive value,PPV)分别为93.18%和79.26%,基于图像的灵敏性、特异性(specificity,SP)和准确率(accuracy,ACC)分别为97.14%、80.00%和95.00%。结论与其他方法对比,基于Canny边缘检测算法与形态学重构相结合的HE自动检测算法具有较好的可行性。     

Computerized analysis of shadowing on breast ultrasound for improved lesion detection

Sonography is being considered for the screening of women at high risk for breast cancer. We are developing computerized detection methods to aid in the localization of lesions on breast ultrasound images. The detection scheme presented here is based on the analysis of posterior acoustic shadowing, since posterior acoustic shadowing is observed for many malignant lesions. The method uses a nonlinear filtering technique based on the skewness of the gray level distribution within a kernel of image data. The database used in this study included 400 breast ultrasound cases (757 images) consisting of complicated cysts, solid benign lesions, and malignant lesions. At a false-positive rate of 0.25 false positives per image, a detection sensitivity of 80% by case (66% by image) was achieved for malignant lesions. The performance for the overall database (at 0.25 false positives per image) was less at 42% sensitivity by case (30% by image) due to the more limited presence of posterior acoustic shadowing for benign solid lesions and the presence of posterior acoustic enhancement for cysts. Our computerized method for the detection of lesion shadows alerts radiologists to lesions that exhibit posterior acoustic shadowing. While this is not a characterization method, its performance is best for lesions that exhibit posterior acoustic shadowing such as malignant and, to a lesser extent, benign solid lesions. This method, in combination with other computerized sonographic detection methods, may ultimately help facilitate the use of ultrasound for breast cancer screening.     

A novel method for retinal optic disc detection using bat meta-heuristic algorithm

Normally, the optic disc detection of retinal images is useful during the treatment of glaucoma and diabetic retinopathy. In this paper, the novel preprocessing of a retinal image with a bat algorithm (BA) optimization is proposed to detect the optic disc of the retinal image. As the optic disk is a bright area and the vessels that emerge from it are dark, these facts lead to the selected segments being regions with a great diversity of intensity, which does not usually happen in pathological regions. First, in the preprocessing stage, the image is fully converted into a gray image using a gray scale conversion, and then morphological operations are implemented in order to remove dark elements such as blood vessels, from the images. In the next stage, a bat algorithm (BA) is used to find the optimum threshold value for the optic disc location. In order to improve the accuracy and to obtain the best result for the segmented optic disc, the ellipse fitting approach was used in the last stage to enhance and smooth the segmented optic disc boundary region. The ellipse fitting is carried out using the least square distance approach. The efficiency of the proposed method was tested on six publicly available datasets, MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB. The optic disc segmentation average overlaps and accuracy was in the range of 78.5–88.2% and 96.6–99.91% in these six databases. The optic disk of the retinal images was segmented in less than 2.1 s per image. The use of the proposed method improved the optic disc segmentation results for healthy and pathological retinal images in a low computation time.

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