Top-hat与SVM在乳腺微钙化点检测中的应用

乳腺X线图像中微钙化点的检测对于乳腺癌的早期诊断非常有意义,然而目前常用的钙化点检测方法普遍存在假阳性高的缺点。采用小波与Top-hat算子相结合的方法对乳腺图像进行钙化点粗检测,并在此基础上,用SVM对钙化点粗检结果进一步甄别,去假存真。这样做可以在基本不降低真阳性率的情况下,大大降低假阳性率。仿真实验证明,该方法的钙化点检出率达到98.46%,错检率仅为3.597%,说明该方法能够有效地从复杂背景中提取出微钙化点。     

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Mexican hat函数的图像特征提取和配准算法

使用Mexican hat函数作为特征检测算子,对图像进行局部区域提取和特征点提取,在此基础上,提出了一种结合局部区域和特征点的图像配准算法。使用Mexican hat检测算子和零交汇点检测的方法获得局部区域特征并进行初步匹配,然后使用基于不同尺度空间的Mexican hat检测算子进行特征点提取。将特征点按照局部区域进行分组,再对每一组内的特征点进行匹配操作。最后使用基于分组的随机采样一致性检验进行变换矩阵求解。算法使用Mexican hat特征检测算子进行两种图像特征的提取,对两种特征分别进行匹配,完成图像配准操作。实验结果表明,给出的Mexican hat特征检测算子在配准精度上不亚于当前主流检测方法,配准算法具有复现率高和运算速度快的优点。     

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.     

基于同态滤波及Top-hat变换的乳腺钙化点提取

针对乳腺数字图像中有些钙化点与周围背景对比度较小，计算机自动提取钙化点具有一定难度的问题，提出了采用同态滤波与形态学Top-hat变换相结合提取钙化点的算法。选用适当的滤波函数及参数通过同态滤波使得钙化点与周围背景的对比度得到加强；再采用适当的结构元素对增强后的乳腺图像进行Top-hat变换以定位乳腺钙化点：然后对图像进行二值化提取出钙化点。实验表明．采用本方法提取乳腺钙化点对于某类乳腺片效果良好．能较好地满足计算机辅助乳腺诊断的要求。     

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。     

一种改进的细胞图像分割算法的研究

针对细胞图像的特点,提出一种改进的基于分水岭算法的细胞图像分割方法。在该方法中．对细胞图像进行数学形态学变换,即采用Top—hat变换后的图像与原始图像相加再减去Bottom—hat变换后的图像以得到最大对比度的图像．继而进行距离变换,最后运用分水岭算法进行分割。实验证明,该改进方法能够得到较好的分割结果。     

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。     

乳腺X射线数字影像中钙化点感兴趣区域提取方法

为乳腺X射线影像计算机辅助诊断做前期预处理,研究了乳腺图像处理中钙化点感兴趣区域提取的问题。在对乳腺X射线图像进行基本的背景分割后,首先运用改进的区域扩张法实现了对乳腺图像中乳腺区域的提取,然后对乳腺区域部分采用改进的反锐化掩模法进行图像增强,突出钙化点区域,再根据含钙化点的特征选取合适的阈值提取出可能含有钙化点的感兴趣区域（ROI）。试验表明,该方法可完成对乳腺影像的ROI提取处理,有助于提高乳腺疾病诊断的准确率。     

基于形状选择性滤波和自适应背景抑制的乳腺钙化图像增强算法

钙化信息是乳腺癌早期诊断的一个重要依据,针对乳腺图像钙化信息受背景组织以及噪声影响而可视性差的问题,提出一种基于形状选择性滤波和自适应背景抑制的乳腺钙化图像增强算法。首先利用形状选择性滤波器提取出潜在非线状钙化信息,将钙化图像分为前景和背景区域;然后对背景信息的对比度进行自适应抑制,同时对前景钙化信息进行对比度增强处理,最后达到有选择地实现乳腺钙化图像中关键信息的可视化增强。实验结果表明,该方法可有针对的选择钙化区域进行对比度增强,同时可有效抑制背景图像中血管、组织等正常区域对钙化区域的影响。     

一种基于二维粒子的自动检测乳腺钼靶片上微钙化点簇的方法

乳腺钼靶片上的微钙化点簇是早期乳腺癌的重要信号,目前,无论是采用人工阅片或是计算机辅助诊断系统都很难对微钙化点簇进行可靠的检测.提出了一种基于二维粒子的自动检测乳腺钼靶片上微钙化点簇的方法,以二维粒子为单位进行可疑区域的提取和微钙化点的判别,很好地克服了传统的基于像素级别的检测方法容易受到干扰和基于数学形态学的检测方法很难确定合适结构元素的问题.提出的快速多元分割算法克服了基于经典Fast Marching的多元分割算法在乳腺钼靶片上进行二维粒子分割时运算时间过长的问题,显著提高了二维粒子的分割速度.在DDSM数据库上的实验结果表明,新的检测方法具有比较满意的检测精度和处理速度.     

Microcalcification detection using fuzzy logic and scale space approaches

Breast cancer is one of the leading causes of women mortality in the world. Since the causes are unknown, breast cancer cannot be prevented. It is difficult for radiologists to provide both accurate and uniform evaluation over the enormous number of mammograms generated in widespread screening. Computer-aided mammography diagnosis is an important and challenging task. Microcalcifications and masses are the early signs 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 and scale space techniques is presented. First, we employ fuzzy entropy principal and fuzzy set theory to fuzzify the images. Then, we enhance the fuzzified image. Finally, scale-space and Laplacian-of-Gaussian filter techniques are used to detect the sizes and locations of microcalcifications. A free-response operating characteristic curve is used to evaluate the performance. The major advantage of the proposed method is its ability to detect microcalcifications even in the mammograms of very dense breasts. A data set of 40 mammograms (Nijmegen database) containing 105 clusters of microcalcifications is studied. Experimental results demonstrate that the microcalcifications can be accurately and efficiently detected using the proposed approach. It can produce lower false positives and false negatives than the existing methods.     

Comparative Evaluation of Two Neural Network Based Techniques for the Classification of Microcalcifications in Digital Mammograms

This paper investigates two neural network based techniques for the classification of microcalcifications in digital mammograms. Both techniques extract suspicious areas containing microcalcifications from digital mammograms and classify them into two categories: whether they contain benign or malignant clusters. The centroids and radii provided by expert radiologists are being used to locate and extract suspicious areas. Two neural networks based on iterative and non-iterative training methods are used to classify them into benign or malignant. The proposed techniques have been implemented in C++ on the SP2 supercomputer. The database from the Department of Radiology at the University of Nijmegen has been used for the experiments. The comparative results are very interesting and promising. Some of them are included in this paper.     

A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms

Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research in this paper proposes a neural-genetic algorithm for feature selection to classify microcalcification patterns in digital mammograms. It aims to develop a step-wise algorithm to find the best feature set and a suitable neural architecture for microcalcification classification. The obtained results show that the proposed algorithm is able to find an appropriate feature subset, which also produces a high classification rate.     

Microcalcification diagnosis in digital mammography using extreme learning machine based on hidden Markov tree model of dual-tree complex wavelet transform

Diagnosis of benign and malignant microcalcifications in digital mammography using Computer-aided Diagnosis (CAD) system is critical for the early diagnosis of breast cancer. Wavelet transform based diagnosis methods are effective to accomplish this task, but limited by representing the correlation within each wavelet scale, these methods neglect the correlation between wavelet scales. In this paper, we apply the hidden Markov tree model of dual-tree complex wavelet transform (DTCWT-HMT) for microcalcification diagnosis in digital mammography. DTCWT-HMT can effectively capture the correlation between different wavelet coefficients and model the statistical dependencies and non-Gaussian statistics of real signals, is used to characterize microcalcifications for the diagnosis of benign and malignant cases. The combined features which consist of the DTCWT-HMT features and the DTCWT features are optimized by genetic algorithm (GA). Extreme learning machine (ELM), an efficient learning theory is employed as the classifier to diagnose the benign and malignant microcalcifications. The validity of the proposed method is evaluated on the Nijmegen, MIAS and DDSM datasets using area under curve (AUC) of receiver operating characteristic (ROC). The AUC values of 0.9856, 0.9941 and 0.9168 of the proposed method are achieved on Nijmegen, MIAS and DDSM, respectively. We compare the proposed method with state-of-the-art diagnosis methods, and the experimental results show the effectiveness of the proposed method for the diagnosis of the benign and malignant microcalcifications in mammograms in terms of the accuracy and stability.     

Improved watershed transform for tumor segmentation: Application to mammogram image compression

In this study, an automatic image segmentation method is proposed for the tumor segmentation from mammogram images by means of improved watershed transform using prior information. The segmented results of individual regions are then applied to perform a loss and lossless compression for the storage efficiency according to the importance of region data. These are mainly performed in two procedures, including region segmentation and region compression. In the first procedure, the canny edge detector is used to detect the edge between the background and breast. An improved watershed transform based on intrinsic prior information is then adopted to extract tumor boundary. Finally, the mammograms are segmented into tumor, breast without tumor and background. In the second procedure, vector quantization (VQ) with competitive Hopfield neural network (CHNN) is applied on the three regions with different compression rates according to the importance of region data so as to simultaneously reserve important tumor features and reduce the size of mammograms for storage efficiency. Experimental results show that the proposed method gives promising results in the compression applications.     

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.     

Detection of clustered microcalcifications in small field digital mammography

The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture with a filter that is sensitive to microcalcification contrast shape. Then, we enhance the mammogram contrast by using wavelet-based sharpening algorithm. Afterwards, we present to radiologist, for visual analysis, such a contrast-enhanced mammogram with suggested positions of microcalcification clusters. We have evaluated the usefulness of the system with the help of four experienced radiologists, who found that it significantly improves the detection of microcalcifications in small field digital mammography.