Relevance vector machine for automatic detection of clustered microcalcifications

Clustered microcalcifications (MC) in mammograms can be an important early sign of breast cancer in women. Their accurate detection is important in computer-aided detection (CADe). In this paper, we propose the use of a recently developed machine-learning technique--relevance vector machine (RVM)--for detection of MCs in digital mammograms. RVM is based on Bayesian estimation theory, of which a distinctive feature is that it can yield a sparse decision function that is defined by only a very small number of so-called relevance vectors. By exploiting this sparse property of the RVM, we develop computerized detection algorithms that are not only accurate but also computationally efficient for MC detection in mammograms. We formulate MC detection as a supervised-learning problem, and apply RVM as a classifier to determine at each location in the mammogram if an MC object is present or not. To increase the computation speed further, we develop a two-stage classification network, in which a computationally much simpler linear RVM classifier is applied first to quickly eliminate the overwhelming majority, non-MC pixels in a mammogram from any further consideration. The proposed method is evaluated using a database of 141 clinical mammograms (all containing MCs), and compared with a well-tested support vector machine (SVM) classifier. The detection performance is evaluated using free-response receiver operating characteristic (FROC) curves. It is demonstrated in our experiments that the RVM classifier could greatly reduce the computational complexity of the SVM while maintaining its best detection accuracy. In particular, the two-stage RVM approach could reduce the detection time from 250 s for SVM to 7.26 s for a mammogram (nearly 35-fold reduction). Thus, the proposed RVM classifier is more advantageous for real-time processing of MC clusters in mammograms.     

A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications

In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs). The classifier is part of a computer-aided diagnosis (CADx) scheme that is aimed to assisting radiologists in making more accurate diagnoses of breast cancer on mammograms. The methods we considered were: support vector machine (SVM), kernel Fisher discriminant (KFD), relevance vector machine (RVM), and committee machines (ensemble averaging and AdaBoost), of which most have been developed recently in statistical learning theory. We formulated differentiation of malignant from benign MCs as a supervised learning problem, and applied these learning methods to develop the classification algorithm. As input, these methods used image features automatically extracted from clustered MCs. We tested these methods using a database of 697 clinical mammograms from 386 cases, which included a wide spectrum of difficult-to-classify cases. We analyzed the distribution of the cases in this database using the multidimensional scaling technique, which reveals that in the feature space the malignant cases are not trivially separable from the benign ones. We used receiver operating characteristic (ROC) analysis to evaluate and to compare classification performance by the different methods. In addition, we also investigated how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD, and RVM) yielded the best performance (Az = 0.85, SVM), significantly outperforming a well-established, clinically-proven CADx approach that is based on neural network (Az = 0.80).     

SVM算法及其在乳腺X片微钙化点 自动检测中的应用

支持矢量机(SVM)是一种新的统计学习方法,其学习原则是使结构风险最小,而非经典学习方法所遵循经验风险最小原则.这使得SVM具有更强的泛化能力.并且,由于SVM求解的是凸二次优化问题,使之能保证所找到的极值解就是全局最优解.本文首次将SVM算法用于乳腺X影像微钙化点自动检测中,对临床实际病例的试用结果表明,同目前常用的基于经验风险最小的人工神经网络(ANN)分类方法相比,SVM具有更高的识别率,值得应用推广.     

一种改进的Laplacian SVM的SAR图像分割算法

当有标识的样本数量有限时,Laplacian SVM算法需要加入尽量多的无标识样本,以提高分类精度.但同时当无标识样本数很大时,算法的时间和空间复杂度将难以接受.为了将Laplacian SVM应用于SAR图像分割这样的大规模分类问题中,提出了一种改进的Laplacian支持向量机算法(Improved Laplaci...     

Optimal filter-based detection of microcalcifications

This paper deals with the problem of texture feature extraction in digital mammograms. We use the extracted features to discriminate between texture representing clusters of microcalcifications and texture representing normal tissue. Having a two-class problem, we suggest a texture feature extraction method based on a single filter optimized with respect to the Fisher criterion. The advantage of this criterion is that it uses both the feature mean and the feature variance to achieve good feature separation. Image compression is desirable to facilitate electronic transmission and storage of digitized mammograms. In this paper, we also explore the effects of data compression on the performance of our proposed detection scheme. The mammograms in our test set were compressed at different ratios using the Joint Photographic Experts Group compression method. Results from an experimental study indicate that our scheme is very well suited for detecting clustered microcalcifications in both uncompressed and compressed mammograms. For the uncompressed mammograms, at a rate of 1.5 false positive clusters/image our method reaches a true positive rate of about 95%, which is comparable to the best results achieved so far. The detection performance for images compressed by a factor of about four is very similar to the performance for uncompressed images.     

基于小波钝化的嵌入式图像处理算法研究

注塑模具保护系统普遍采用模板匹配图像处理算法,其存在实时性不强、精确度不高,以及对运行环境要求严苛等问题。论文提出了基于小波钝化的嵌入式模具保护图像处理算法,该算法运行于嵌入式平台,采用小波钝化的方法凸显待测残留物,无需进行模板匹配、图像配准和较正;提出了基于像素值统计的支持向量机检测算法,以适应嵌入式平台内存小的特点;引入支持向量机分类,有效解决了图像偏移带来的误差问题。MATLAB实验测得,该算法的模腔残留物检测平均准确率为85.71%,残留物检出平均耗时0.910s。结果表明,采用小波钝化和支持向量机的嵌入式图像处理算法,无论在算法检测精度还是算法响应速度方面均优于以灰度共生矩阵匹配算法和差影法为代表的图像匹配算法。     

Application of Artificial Neural Networks to Medical Image Pattern Recognition: Detection of Clustered Microcalcifications on Mammograms and Lung Cancer on Chest Radiographs

Three neural network models were employed to evaluate their performances in the recognition of medical image patterns associated with lung cancer and breast cancer in radiography. The first method was a pattern match neural network. The second was a conventional backpropagation neural network. The third method was a backpropagation trained neocognitron in which the signal propagation is operated with the convolution calculation from one layer to the next. In the convolution neural network (CNN) experiment, several output association methods and trainer imposed driving functions in conjunction with the convolution neural network are proposed for general medical image pattern recognition. An unconventional method of applying rotation and shift invariance is also used to enhance the performance of the neural nets.We have tested these methods for the detection of microcalcifications on mammograms and lung nodules on chest radiographs. Pre-scan methods were previously described in our early publications. The artificial neural networks act as final detection classifiers to determine if a disease pattern is presented on the suspected image area. We found that the convolution neural network, which internally performs feature extraction and classification, achieves the best performance among the three neural network models. These results show that some processing associated with disease feature extraction is a necessary step before a classifier can make an accurate determination.     

多特征分类的PolSAR图像飞机目标检测方法

针对目前有关极化合成孔径雷达(Polarimetric Synthetic Aperture Radar, PolSAR)的飞机目标检测算法虚警较多、自适应性较差的问题,给出一种复杂大场景中PolSAR图像多特征分类的飞机目标检测方法。该方法分为线下分类器训练和飞机目标检测两部分。使用Filter特征选择结合穷举法筛选出分类性能高的飞机极化特征训练SVM (Support Vector Machine, SVM)分类器;利用异化散射功率提取疑似飞机目标,进一步提取多个极化特征送入SVM分类获得检测结果。利用UAVSAR系统采集的多幅实测数据进行实验,并与现有的PolSAR图像飞机目标检测算法进行对比,结果表明该方法能够有效检测出飞机目标,并且虚警和漏警较少,方法自适应性有所提高。      

一种基于二值粒子群优化和支持向量机的目标检测算法

针对复杂场景下目标检测和目标检测中特征选择问题,该文将二值粒子群优化算法(BPSO)用于特征选择,结合支持向量机(SVM)技术提出了一种新颖的基于BPSO-SVM特征选择的自动目标检测算法。该算法将目标检测转化为目标识别问题,采用wrapper特征选择模型,以SVM为分类器,通过样本训练分类器,根据分类结果,利用BPSO算法在特征空间中进行全局搜索,选择最优特征集进行分类。基于BPSO-SVM的特征选择方法降低了特征维数,显著提高了分类器性能。实验结果表明,该文算法不仅有效提高了复杂场景下目标姿态、尺度、光照变化和局部被遮挡时的检测准确率,还大大缩短了检测时间。     

一种基于PSOSVM的盲隐写分析方法

针对盲隐写分析中的特征选择问题,提出了结合粒子群优化算法（PSO）的支持向量机分类器进行特征选择的方法。该方法使用非线性支持向量机作为分类器,使用PSO为支持向量机寻找最优的图像特征集合作为训练集和测试集,同时选择最优的支持向量机参数,进而利用最优的特征集和支持向量机参数对隐写图像进行检测。实验结果表明,该优化方法明显优于Farid。ANOVA和F—score方法,提高了检测隐写图像的成功率和系统检测效率。     

Optimizing Case-based detection performance in a multiview CAD system for mammography

When reading mammograms, radiologists combine information from multiple views to detect abnormalities. Most computer-aided detection (CAD) systems, however, use primitive methods for inclusion of multiview context or analyze each view independently. In previous research it was found that in mammography lesion-based detection performance of CAD systems can be improved when correspondences between MLO and CC views are taken into account. However, detection at case level detection did not improve. In this paper, we propose a new learning method for multiview CAD systems, which is aimed at optimizing case-based detection performance. The method builds on a single-view lesion detection system and a correspondence classifier. The latter provides class probabilities for the various types of region pairs and correspondence features. The correspondence classifier output is used to bias the selection of training patterns for a multiview CAD system. In this way training can be forced to focus on optimization of case-based detection performance. The method is applied to the problem of detecting malignant masses and architectural distortions. Experiments involve 454 mammograms consisting of four views with a malignant region visible in at least one of the views. To evaluate performance, five-fold cross validation and FROC analysis was performed. Bootstrapping was used for statistical analysis. A significant increase of case-based detection performance was found when the proposed method was used. Mean sensitivity increased by 4.7% in the range of 0.01-0.5 false positives per image.     

一种基于遗传算法的SVM决策树多分类方法

在当前的机器学习领域,如何利用支持向量机（SVM）对多类目标进行分类,同时提高分类器的分类效率已经成为研究的热点之一,有效地解决此问题对于提高目标的识别概率具有较大意义。本文针对SVM多分类问题提出了一种基于遗传算法的SVM最优决策树生成算法。算法以随机生成的决策树构建的SVM分类器对同一测试样本的分类正确率作为遗传算法的适应度函数,通过遗传算法寻找到最优决策树,再以最优决策树构建SVM分类器,最终实现SVM的多分类。将该算法应用于低空飞行声目标识别问题,实验结果表明,新方法比传统的1-a-1、1-a-r、SVM-DL和GADT-SVM方法有更高的分类精度和更短的分类时间。      

基于自动秩估计的黎曼优化矩阵补全算法及其在图像补全中的应用

矩阵补全(MC)作为压缩感知(CS)的推广,已广泛应用于不同领域。近年来,基于黎曼优化的MC算法因重构精度高、计算速度快的特点,引起了广泛关注。针对基于黎曼优化的MC算法需假设原矩阵秩固定已知,且随机选择迭代起点的特点,该文提出一种基于自动秩估计的黎曼优化MC算法。该算法通过优化包含秩正则项的目标函数,迭代获取秩估计值和预重构矩阵。在估计所得秩对应的矩阵空间上以预重构矩阵为迭代起点,利用基于黎曼流形的共轭梯度法进行矩阵补全,从而提高重构精度。实验结果表明,与几种经典的图像补全方法相比,该文算法图像重构精度显著提高。     

基于多分类的虹膜匹配方法

在传统的虹膜识别系统中,虹膜匹配被认为是一个二分类问题：类内匹配和类间匹配。许多已存在的方法简单地利用距离来执行虹膜匹配。由于这些方法不能很好地利用虹膜特征,所以会产生很高的拒识率和误识率,且鲁棒性不强。为了解决这些问题,提出把虹膜匹配当作一个多分类问题,采用一种新颖的蕨算法（Ferns）分类器来完成该工作。相比支持向量机（SVM）分类器,在执行虹膜匹配时,Ferns分类器有诸多优点。为了对提出的算法给出全面评价,实验中分别在认证和识别这2种模式下对该算法进行测试。实验结果证明,提出的方法可以极大地改善虹膜识别系统的性能。     

A novel approach to microcalcification detection using fuzzy logictechnique

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach     

基于SVM后验概率的红外弱小目标检测

针对复杂云层背景中背景边缘干扰严重的问题,提出基于支持向量机(SVM)后验概率的红外弱小目标检测算法。该算法将红外弱小目标检测视作目标与背景的二分类问题,根据红外图像特性,以各像素点8个方向的梯度作为目标和背景的分类依据,选取能够表现目标和背景特征的梯度作为SVM训练样本的主要参考量,设定训练集,并通过训练获得SVM分类模型。基于SVM后验概率的检测算法将待测样本各像素点的8方向梯度作用于分类模型,获得的SVM后验概率作为检测输出。实验结果证明了该算法的有效性。     

一种基于二叉树决策分类的乳腺肿块自动检测方法

乳腺癌是严重威胁女性健康的重要疾病,乳腺癌计算机辅助诊断能够提高乳腺普查的效率和精度。乳腺肿块的自动检测是实现乳腺癌计算机辅助诊断的重要一步。由于肿块和背景之间的对比度低,肿块大小、位置、灰度不确定等,肿块的准确检测非常困难。预处理、疑似区域分割、特征提取以及分类器设计是乳腺肿块分割的关键。本文对经过增强的乳腺X光图像采用一种自适应阈值方法分割出疑似区域,提取疑似区域表征乳腺肿块的面积、紧凑度、圆形度、灰度方差、灰度均值以及偏离度六种特征,最后利用二叉决策树把疑似区域分为两类:肿块和正常乳腺组织。利用50幅图像测试系统的性能,肿块的检测率(TP)为86.18%,且每幅图像的平均误检(FP)为1.18个。实验结果证明了本文提出方法的有效性。     

Efficient Total Variation Minimization Methods for Color Image Restoration

In this paper, we consider and study a total variation minimization model for color image restoration. In the proposed model, we use the color total variation minimization scheme to denoise the deblurred color image. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. We show the convergence of the alternating minimization algorithm and demonstrate that the algorithm is very efficient. Our experimental results show that the quality of restored color images by the proposed method are competitive with the other tested methods.     

基于扩展马尔科夫特征的Seam-Carving篡改检测

针对能够用于图像篡改的Seam-Carving技术,提出了一种基于扩展的马尔科夫特征的Seam-Carving篡改识别算法。该算法充分考虑了Seam-Carving操作导致的图像频域特征的变化,将传统的利用马尔科夫转移概率矩阵求取的图像特征和基于扩展的马尔科夫转移概率特征进行融合,而后利用支持向量机进行分类训练,从而达到有效识别基于Seam-Carving的图像篡改。实验结果表明,提出的方案性能优于传统的基于马尔科夫转移矩阵的特征选择方法以及现有的一些该类图像篡改检测方法。     

Detection of Clustered Microcalcifications on Mammograms Using Surrounding Region Dependence Method and Artificial Neural Network

Clustered microcalcifications on X-ray mammograms are an important sign in the detection of breast cancer. A statistical texture analysis method, called the surrounding region dependence method (SRDM), is proposed for the detection of clustered microcalcifications on digitized mammograms. The SRDM is based on the second-order histogram in two surrounding regions. This method defines four textural features to classify region of interests (ROIs) into positive ROIs containing clustered microcalcifications and negative ROIs of normal tissues. The database is composed of 64 positive and 76 negative ROI images, which are selected from digitized mammograms with a pixel size of 100 × 100 m² and 12 bits per pixel. An ROI is selected as an area of 128 × 128 pixels on the digitized mammograms. In order to classify ROIs into the two types, a three-layer backpropagation neural network is employed as a classifier. A segmentation of individual microcalcifications is also proposed to show their morphologies. The classification performance of the proposed method is evaluated by using the round-robin method and a free-response receiver operating-characteristics (FROC) analysis. A receiver operating-characteristics (ROC) analysis is employed to present the results of the round-robin testing for the case of several hidden neurons. The area under the ROC curve, A _z, is 0.997, which is achieved in the case of 4 hidden neurons. The FROC analysis is performed on 20 cropped images. A cropped image is selected as an area of 512 × 512 pixels on the digitized mammograms. In terms of the FROC, a sensitivity of more than 90% is obtained with a low false-positive (FP) detection rate of 0.67 per cropped image.