基于支持向量机的乳腺钙化点检测算法

针对乳腺X线图像微钙化点检测假阳性高的问题,提出一种微钙化点检测算法.算法首先以小波与Top-hat算子相结合的方法进行钙化点粗检测,然后以支持向量机(SVM:Support Vector Machine)为工具对粗检测结果进行真钙化点与假钙化点分类.对开放乳腺图像数据库MIAS的仿真实验表明,算法的检出率超过98%,错检率不足4%,达到理想的检测效果.     

基于二维最大熵阈值分割的钙化点检测算法

在数字乳腺X线图像中,钙化是早期乳腺癌的重要征象之一.为了提高钙化点检测的准确度及降低检测的假阳性率,提出了一种结合数学形态学滤波和二维最大熵阈值分割的钙化点检测算法.算法首先采用top-hat算子对图像的背景进行抑制,然后利用二维最大熵阈值分割算法得到可疑钙化点区域,最后采用SVM分类的方法去除假阳性区域,得到最终的钙化点检测结果,并采用MIAS乳腺影像库进行仿真实验,钙化点检测的敏感性为94.6 %,假阳性率为10.5%.实验结果表明,方法对钙化点的定位精确,具有较高的检出率及较低的假阳性率.     

基于混合智能分类方法的微钙化簇检测系统

乳腺癌的早期症状检测,可在患病妇女的乳腺X光片中找到微钙化簇,但那些小尺寸低对比度的微钙化簇容易被忽略或被医生误诊.对此,提出一种用于混合智能分类方法判断乳腺X光片中微钙化点的计算机辅助诊断系统,以帮助放射科医生分析乳腺X光片并作出诊断决定,帮助增加真阳性的检出率和减少假阳性的诊断.该系统主要包括三个模块:预处理和分割;感兴趣区(ROI)的说明;特征提取和分类.诊断结果通过ROC曲线的性能来体现,并且根据ROC曲线下方的面积(即AZ)来量化.     

基于先验模型和区域生长的乳腺图像钙化点检测

早期乳腺癌的一个重要特征就是钙化点,快速准确地找出乳腺图像中的钙化点是成功诊断的第一步。提出了一种先验模板和区域生长的钙化点快速检测方法。根据钙化点检测的临床经验,选用一直径为0.5mm的模板找出乳腺图像中的局部峰值点。以这些峰值点为初始种子点,进行区域生长;计算每个区域的面积、平均灰度、对比度,保留满足钙化点特征的区域。根据先验知识,对生长获得的钙化点是否成簇进行判别,保留成簇的微钙化点。实验表明,该算法实现了乳腺图像中钙化点的快速自动检测,提高了医生诊断的正确性。     

一种基于Top-hat的乳腺图像中钙化点的检测方法

钙化点是乳腺癌早期的一个主要放射学征象。为了实现数字乳腺图像中钙化点的自动检测，提出了一种基于Top—hat算子的钙化点检测方法。该方法采用形态学中的Top—hat算子对图像的背景进行抑制，然后结合孤立钙化点的灰度、纹理和对比度等特征对乳腺图像中的钙化点进行检测。与一般的检测方法相比，这种方法能够有效地检测到强背景中的弱小钙化点目标，而且检测结果贴近于钙化点病灶的真实形状。     

致密组织图像的图像增强算法

近年来乳腺癌有年轻化的趋势,年轻妇女的乳腺组织结构以致密组织为主,由于致密组织在乳腺X光片中也呈现出高亮度,所以致密组织很容易被误解为微钙化。文章主要针对这些图像,提出一种新的图像增强算法。试验证明,该算法可以改进CAD系统性能,并能更好地在致密乳腺组织图像中检测微钙化点。     

基于小波与统计学的乳腺微钙化点检测方法

提出了一种基于小波与统计学检测乳腺X线片中微钙化点的新方法。首先对数字化X片进行小波分解。为了提高图像的对比度，采用了多尺度自适应增益的图像增强方法。然后对增强后的图像细节分量运用统计学中的偏度和峰度来选取感兴趣区。最后利用箱线图极端值检测法确定微钙化点的位置。采用本文的方法对实际的数字化乳腺X片进行实验，结果表明该方法具有图像增强效果明显和钙化点定位准确等特点。     

基于贡献矩阵的微钙化点检测

提出了基于贡献矩阵的特征提取方法。首先采用基于结构分析的统计方法构造贡献矩阵,利用贡献矩阵对图像预处理;通过二维主成分分析方法提取图像特征。将此算法用于微钙化点图像特征提取,利用支持向量机分类器进行分类。实验结果表明,该算法加快了训练速度,同时有效地降低了微钙化点检测的假阳性。     

乳腺X线图像结构扭曲检测的研究

针对乳腺X线图像结构扭曲 （Architectural distortion,AD）检测假阳性率偏高的问题,提出了一种新的乳腺X线图像结构扭曲 检测方法相似度收敛指数（Similarity convergence index,SCI）方法.首先利用马氏距离比计算出毛刺的相似度,然后通过计算相似度加权的收敛指数增强放射状毛 刺,最后提取出收敛指数的局部最大值作为候选点,并对这些候选点进行分类,检测出结构扭曲. 该方法在Mini-MIAS （Mammographic Image Analysis Society）乳腺图像和北京大学人民医院乳腺中心乳腺图像上进行验证,实验结果表明,本文提出的方法有效降低了假阳 性率,同时适用于脂肪型乳腺X线图像和致密型乳腺X线图像.     

基于可拒识-双层支持向量分类器的微钙化点检测

为克服医学图像微钙化点检测中假阳性高的缺点,构造了一种带拒识能力的双层支持向量模型分类器,用于钙化点检测.检测时,首先利用基于最大间隔超平面的支持向量分类器（SVC）对输入模式进行分类判决;然后通过求取真实钙化点样本特征空间最小的包含球形边界来得到钙化点样本的球形支持向量域表示（SVDD）;接着利用钙化点的支持向量域表示对输入模式进行拒识或接受处理;最后利用SVC与SVDD两个分类器的结果来进行综合判决.仿真实验结果表明,该算法在不影响微钙化点的检出率的情况下,可部分解决假阳性高的问题.     

An evaluation and ranking of evolutionary algorithms in segmenting abnormal masses in digital mammograms

Breast cancer is the most common cancer in women worldwide and the second main cause of cancer mortality after lung cancer. Up to now, there still no prevention nor early symptoms of breast cancer. Early detection can decrease significantly the mortality rate as the disease can be treated at an early stage. X-Ray is the current screening method that helps in detecting the most two common abnormalities of the breast, masses and micro-calcifications. However, interpreting mammograms is challenging in dense breasts as the abnormal masses and the normal glandular tissue of the breast have similar characteristics. Recently, the evolutionary algorithms have been widely used in image segmentation. In this paper, we evaluate and compare the performance of six most used evolutionary algorithms, invasive weed optimization (IWO), genetic algorithm (GA), particle swarm optimization (PSO), electromagnetism-like optimization (EMO), ant colony optimization (ACO), and artificial bee colony (ABC) in terms of clustering abnormal masses in the breast, particularly dense and extremely dense breasts. This evaluation is conducted based on quantitative metrics including Cohen’s Kappa, correlation, and false positive and false negative rates. The evolutionary algorithms are then ranked based on two multi-criteria decision analysis methods, the Preference Ranking Organization Method for the Enrichment of Evaluations (PROMETHEE) and the Graphical Analysis for Interactive Aid (GAIA).

     

Quantitative analysis of morphological techniques for automatic classification of micro-calcifications in digitized mammograms

In this paper we present an evaluation of four different algorithms based on Mathematical Morphology, to detect the occurrence of individual micro-calcifications in digitized mammogram images from the mini-MIAS database. A morphological algorithm based on contrast enhancement operator followed by extended maxima thresholding retrieved most of micro-calcifications. In order to reduce the number of false positives produced in that stage, a set of features in the spatial, texture and spectral domains was extracted and used as input in a support vector machine (SVM). Results provided by TMVA (Toolkit for Multivariate Analysis) produced the ranking of features that allowed discrimination between real micro-calcifications and normal tissue. An additional parameter, that we called Signal Efficiency*Purity (denoted SE*P), is proposed as a measure of the number of micro-calcifications with the lowest quantity of noise. The SVM with Gaussian kernel was the most suitable for detecting micro-calcifications. Sensitivity was obtained for the three types of breast. For glandular, it detected 137 of 163 (84.0%); for dense tissue, it detected 74 of 85 (87.1%) and for fatty breast, it detected 63 of 71 (88.7%). The overall sensitivity was 85.9%. The system also was tested in normal images, producing an average of false positives per image of 13 in glandular tissue, 11 in dense tissue and 15 in fatty tissue.     

A New Image Mining Approach for Detecting Micro-Calcification in Digital Mammograms

Although mammography is typically the best method to detect breast cancer, it does not recognize 3–20% of the cancer cases. Mammography has established itself as the most efficient technique for detecting tiny cancerous tumor and micro-calcifications are the most difficult to detect since they are very small (0.1–1.0 mm) and they are almost contrasted against the images background. The main purpose of this paper is to provide a new method for the automatic diagnosis of micro-calcification in digital mammograms. It is based on image mining, and the results show 97.35% accuracy, which is improved than the previous works. Tests are based on the standard images data corpus, MIAS. The practical result of this research is registered as an invention in the Patents and Industrial Property Registration Organization numbered as 83119.     

多视角乳腺X线图像匹配方法综述

乳腺X线摄影术是目前乳腺疾病的主要检查方式之一,采用图像处理与模式识别的方法对乳腺X线图像进行分析,可以辅助医生发现漏检的病变,识别出假阳性组织,有效降低漏诊率和误诊率。基于图像处理的方法应模拟医生阅片机制,因而基于多视角的乳腺癌检测与分类方法更加适合临床的要求。多视角乳腺癌检测的基础是确定不同视角图像间的匹配关系,本文较为全面地讨论了乳腺X线图像多视角匹配方法。首先对现有乳头检测和胸肌分割方法进行回顾,并对比分析了不同方法之间的优缺点;然后讨论了现有双视角匹配以及双边匹配方法;最后对现有匹配方法存在的问题进行分析,并提出了改善措施。      

基于多特征的Android恶意软件检测方法

传统的基于权限的Android恶意软件检测方法检测率较高,但存在较高的误报率,而基于函数调用的检测方法特征提取困难,难以应用到移动平台上。因此,在保留传统权限特征的基础上,提出了以权限和资源文件多特征组合方式的朴素贝叶斯检测方法,该方法所选特征提取简便,且具有较低的误报率,有效弥补传统检测方法的不足。实验从4 396个恶意样本和4 500个正常样本中随机抽取5组恶意样本和5组正常样本集,分别作了基于权限和基于多特征的对比实验。实验结果表明,与基于权限的分类方法相比,基于多特征的分类方法能显著地降低误报率,因此基于多特征的检测方法效果更优。     

基于自联想神经网络的良恶性肿瘤分类技术

尽管乳腺癌的诊断和处理技术在不断进步,但乳腺病灶的早期检测仍然是阻止癌症的主要方法。乳腺组织中肿块的存在是乳腺癌的重要特征。通过使用自联想神经网络和多层感知器技术研究了良恶性肿瘤的分类方法。该研究的实验结果显示,在DDSM数据库上进行训练和测试,得到了较高的CAD系统的灵敏度（TP）和较低的假阳性率（FP）;在100%的训练分类率上获得了91%的测试分类率;ROC曲线下方面积最大可达约0.948。     

A comparison of different Gabor feature extraction approaches for mass classification in mammography

We investigate the performance of six different approaches for directional feature extraction for mass classification problem in digital mammograms. These techniques use a bank of Gabor filters to extract the directional textural features. Directional textural features represent structural properties of masses and normal tissues in mammograms at different orientations and frequencies. Masses and micro-calcifications are two early signs of breast cancer which is a major leading cause of death in women. For the detection of masses, segmentation of mammograms results in regions of interest (ROIs) which not only include masses but suspicious normal tissues as well (which lead to false positives during the discrimination process). The problem is to reduce the false positives by classifying ROIs as masses and normal tissues. In addition, the detected masses are required to be further classified as malignant and benign. The feature extraction approaches are evaluated over the ROIs extracted from MIAS database. Successive Enhancement Learning based weighted Support Vector Machine (SELwSVM) is used to efficiently classify the generated unbalanced datasets. The average accuracy ranges from 68 to 100 % as obtained by different methods used in our paper. Comparisons are carried out based on statistical analysis to make further recommendations.     

医学CAD系统中降低假阳性率的解决方案

如何提高乳腺癌计算机辅助诊断系统（CAD）中的灵敏度一直是众多学者研究的热点,特别是针对亚洲女性及年轻妇女的致密组织图像的检测。尽管之前已经提出了针对该类图像的解决方法,实验也表明,该方法可以提高系统的灵敏度（真阳性率TP）,但人们发现随着TP的提高也伴随了假阳性率（FP）的增长。所以,本文的研究目的是在前续研究的基础上,即保证CAD系统的灵敏度的同时尽可能地降低假阳性率。     

一种新颖的乳腺X线影像中钙化点检测方法

乳腺癌是妇女常见恶性肿瘤之一,早期诊断和早期治疗是降低乳腺癌患者死亡率的关键。微钙化是乳腺癌早期的一个重要标志,因此,快速准确地找出乳腺X光片中的钙化点成为成功诊断的第一步。现有多种方法能用于检测钙化点并各有优缺点,其中典型的高斯-拉普拉斯算子（LOG）是有效方法之一,尽管其能较精确地检出钙化点的位置但检测效率低。级联形态学滤波算子的LOG改进了LOG的效率,但仍无法满足大规模普查的高效率要求。通过提出一维和二维LOG相级联的方法来实现高效实时的钙化点的检测,并通过实验证实了所提检测方法的有效性。     

BagGMM: Calling copy number variation by bagging multiple Gaussian mixture models from tumor and matched normal next-generation sequencing data

Copy number variations (CNVs) contribute significantly to human genomic variability, some of which lead to diseases. However, effective detection of CNVs from whole genome next generation sequencing data (NGS) remains challenging. Here, we present BagGMM, a new method to call CNVs using tumor-normal matched samples from NGS data. BagGMM extracts read depth ratios of tumor samples to normal samples, divides the genomic sequences into segments by sliding windows to count the average coverage ratio of each segment, filters candidate deletions and duplications based on a coarse criterion of coverage ratio, and then builds Gaussian Mixture Model (GMM) for remaining ratios to identify the remaining ambiguous copy number states after filtration. Bagging multiple GMMs makes false positive calls descent instead of using one GMM, thus enhancing the detection power of BagGMM. Considering the computation speed of GMMs and false positive calls, we employ a segmentation procedure “large window and then small windows”, which is also helpful to determine boundary of CNV regions. We apply BagGMM to three simulation datasets and two groups of human whole genome sequencing (WGS) data for breast cancer patients and ovarian cancer patients to identify CNVs, respectively. All performed experiments demonstrate that BagGMM has the capability of robustly identification of CNVs with different sizes and states. The performance of this tool is compared to four peer existing CNV detection methods. BagGMM shows a significant improvement in both sensitivity and specificity for detecting both copy number gains and losses.