Automatic detection of the breast border and nipple position on digital mammograms using genetic algorithm for asymmetry approach to detection of microcalcifications

The presence of microcalcifications in breast tissue is one of the most incident signs considered by radiologist for an early diagnosis of breast cancer, which is one of the most common forms of cancer among women. In this paper, the Genetic Algorithm (GA) is proposed for automatic look at commonly prone area the breast border and nipple position to discover the suspicious regions on digital mammograms based on asymmetries between left and right breast image. The basic idea of the asymmetry approach is to scan left and right images are subtracted to extract the suspicious region. The proposed system consists of two steps: First, the mammogram images are enhanced using median filter, normalize the image, at the pectoral muscle region is excluding the border of the mammogram and comparing for both left and right images from the binary image. Further GA is applied to magnify the detected border. The figure of merit is calculated to evaluate whether the detected border is exact or not. And the nipple position is identified using GA. The some comparisons method is adopted for detection of suspected area. Second, using the border points and nipple position as the reference the mammogram images are aligned and subtracted to extract the suspicious region. The algorithms are tested on 114 abnormal digitized mammograms from Mammogram Image Analysis Society database.     

Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

A novel fuzzy logic and histogram based algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm is proposed for enhancing the local contrast of digital mammograms. A digital mammographic image uses a narrow range of gray levels. The contrast of a mammographic image distinguishes its diagnostic features such as masses and micro calcifications from one another with respect to the surrounding breast tissues. Thus, contrast enhancement and brightness preserving of digital mammograms is very important for early detection and further diagnosis of breast cancer. The limitation of existing contrast enhancement and brightness preserving techniques for enhancing digital mammograms is that they limit the amplification of contrast by clipping the histogram at a predefined clip-limit. This clip-limit is crisp and invariant to mammogram data. This causes all the pixels inside the window region of the mammogram to be equally affected. Hence these algorithms are not very suitable for real time diagnosis of breast cancer. In this paper, we propose a fuzzy logic and histogram based clipping algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm, which automates the selection of the clip-limit that is relevant to the mammogram and enhances the local contrast of digital mammograms. The fuzzy inference system designed to automate the selection of clip-limit requires a limited number of control parameters. The fuzzy rules are developed to make the clip limit flexible and variant to mammogram data without human intervention. Experiments are conducted using the 322 digital mammograms extracted from MIAS database. The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE), Absolute Mean Brightness Coefficient (AMBC) and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that the proposed FC-CLAHE algorithm produces better results than several state-of-art algorithms.     

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。      

Performance Analysis of Breast Cancer Detection Method Using ANFIS Classification Approach

Breast cancer is one of the deadly diseases prevailing in women. Earlier detection and diagnosis might prevent the death rate. Effective diagnosis of breast cancer remains a significant challenge, and early diagnosis is essential to avoid the most severe manifestations of the disease. The existing systems have computational complexity and classification accuracy problems over various breast cancer databases. In order to overcome the above-mentioned issues, this work introduces an efficient classification and segmentation process. Hence, there is a requirement for developing a fully automatic methodology for screening the cancer regions. This paper develops a fully automated method for breast cancer detection and segmentation utilizing Adaptive Neuro Fuzzy Inference System (ANFIS) classification technique. This proposed technique comprises preprocessing, feature extraction, classifications, and segmentation stages. Here, the wavelet-based enhancement method has been employed as the preprocessing method. The texture and statistical features have been extracted from the enhanced image. Then, the ANFIS classification algorithm is used to classify the mammogram image into normal, benign, and malignant cases. Then, morphological processing is performed on malignant mammogram images to segment cancer regions. Performance analysis and comparisons are made with conventional methods. The experimental result proves that the proposed ANFIS algorithm provides better classification performance in terms of higher accuracy than the existing algorithms.     

Automated assessment of breast tissue density in digital mammograms

Mammographic density is known to be an important indicator of breast cancer risk. Classification of mammographic density based on statistical features has been investigated previously. However, in those approaches the entire breast including the pectoral muscle has been processed to extract features. In this approach the region of interest is restricted to the breast tissue alone eliminating the artifacts, background and the pectoral muscle. The mammogram images used in this study are from the Mini-MIAS digital database. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: (1) preprocessing, (2) feature extraction, and (3) classification. Gray level thresholding and connected component labeling is used to eliminate the artifacts and pectoral muscles from the region of interest. Statistical features are extracted from this region which signify the important texture features of breast tissue. These features are fed to the support vector machine (SVM) classifier to classify it into any of the three classes namely fatty, glandular and dense tissue.The classifier accuracy obtained is 95.44%.     

A novel automatic suspicious mass regions identification using Havrda & Charvat entropy and Otsu's N thresholding

Mass detection is a very important process for breast cancer diagnosis and computer aided systems. It can be very complex when the mass is small or invisible because of dense breast tissue. Therefore, the extraction of suspicious mass region can be very challenging. This paper proposes a novel segmentation algorithm to identify mass candidate regions in mammograms. The proposed system includes three parts: breast region and pectoral muscle segmentation, image enhancement and suspicious mass regions identification. The first two parts have been examined in previous studies. In this study, we focused on suspicious mass regions identification using a combination of Havrda & Charvat entropy method and Otsu's N thresholding method. An open access Mammographic Image Analysis Society (MIAS) database, which contains 59 masses, was used for the study. The proposed system obtained a 93% sensitivity rate for suspicious mass regions identification in 56 abnormal and 40 normal images.     

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

This article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low‐dose X‐ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low‐dose X‐ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low‐level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and visual quality in mammograms compared with other state‐of‐the‐art methods.     

Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN model to diagnose BCa from digitized mammogram images. The main objective of this research is to develop an accurate and efficient early diagnosis model for BCa detection. This proposed model is a multi-view-based computer-aided diagnosis (CAD) model, which performs the diagnosis of BCa on multi-views of mammogram images like medio-lateral-oblique (MLO) and cranio-caudal (CC). The digital mammogram images are collected from the digital database for screening mammography (DDSM) dataset. In preprocessing, median filter and contrast limited adaptive histogram equalization (CLAHE) techniques are utilized for image enhancement. After preprocessing, the segmentation is performed using the region growing (RG) algorithm. The feature extraction is carried out from the segmented images using a pyramidal histogram of oriented gradients (PHOG) and the AlextNet model. Finally, the classification is performed using the weighted k-nearest neighbor (WkNN) optimized with sequential minimal optimization (SMO). The classified images are evaluated based on accuracy, recall, precision, specificity, f1-score, and mathews correlation coefficient (MCC). Additionally, the false positive and error rates are evaluated. The proposed model obtained 98.57% accuracy, 98.61% recall, 99.25% specificity, 98.63% precision, 97.93% f1-score, 96.26% MCC, 0.0143 error rate, and 0.0075 false positive rate (FPR). Compared to the existing models, the research model has obtained better performances and outperformed the other models.     

Breast Mammogram Analysis and Classification Using Deep Convolution Neural Network

One of the fast-growing disease affecting women’s health seriously is breast cancer. It is highly essential to identify and detect breast cancer in the earlier stage. This paper used a novel advanced methodology than machine learning algorithms such as Deep learning algorithms to classify breast cancer accurately. Deep learning algorithms are fully automatic in learning, extracting, and classifying the features and are highly suitable for any image, from natural to medical images. Existing methods focused on using various conventional and machine learning methods for processing natural and medical images. It is inadequate for the image where the coarse structure matters most. Most of the input images are downscaled, where it is impossible to fetch all the hidden details to reach accuracy in classification. Whereas deep learning algorithms are high efficiency, fully automatic, have more learning capability using more hidden layers, fetch as much as possible hidden information from the input images, and provide an accurate prediction. Hence this paper uses AlexNet from a deep convolution neural network for classifying breast cancer in mammogram images. The performance of the proposed convolution network structure is evaluated by comparing it with the existing algorithms.     

基于前景增强与背景抑制的显著性物体检测

显著性物体检测的关键在于准确地突出前景区域,多数传统方法在处理复杂背景图像时效果不理想。针对上述问题,提出了一种基于前景增强与背景抑制的显著性物体检测方法。首先,利用简单线性迭代聚类(SLIC)将图像进行分割得到多个超像素区域,通过区域间的对比和边界信息分别获得图像的显著区域与背景种子,并通过计算得到基于区域间对比和基于背景的两幅显著图。然后,在两幅图像中运用Seam Carving和Graph based的图像分割法区分显著与非显著区域,进而得到前景增强与背景抑制模板。最终,融合两幅显著图与模板得到最终的显著图。在公开数据集MSRA 1000上对算法进行验证,结果表明,所提算法与7种主流算法相比具有更好的查准率和查全率。     

An improved GVF snake based breast region extrapolation scheme for digital mammograms

To accurately extrapolate the breast region from a mammogram is a crucial stage of breast mass analysis. It significantly influences the overall analysis accuracy and processing speed of the whole breast mass analysis. In this paper, a novel edge map adjusting gradient vector flow snake (EMA GVF snake) algorithm for extrapolation of breast region from mammograms is proposed. In the proposed algorithm, the median filter is used to filter out the noise in a mammogram, the scale down stage is used to resize down the mammogram size (hence speeding up the extrapolation). The binarization processing stage and the morphological erosion processing stage are used to find a rough breast border. Then a novel gradient adjusting stage is applied to get a modified edge map and the gradient vector flow snake (GVF snake) is used to get the accurate breast border from the rough breast border. The proposed algorithm is tested on 322 digital mammograms from the Mammogram Image Analysis Society database. The mean error function, misclassification error function and the relative foreground area error function are conducted to evaluate the results of the detected breast border and the extracted breast region. Experimental results show that the breast border extrapolated by the proposed algorithm approximately follows the breast border extrapolated by an expert radiologist. Experimental results also show that the proposed algorithm is more robust and precise than the traditional GVF snake scheme for the breast extrapolation on mammograms.     

Two graph theory based methods for identifying the pectoral muscle in mammograms

Two image segmentation methods based on graph theory are used in conjunction with active contours to segment the pectoral muscle in screening mammograms. One method is based on adaptive pyramids (AP) and the other is based on minimum spanning trees (MST). The algorithms are tested on a public data set of mammograms and results are compared with previously reported methods. In 80% of the images, the boundary of the segmented regions has average error less than 2 mm. In 82 of 84 images, the boundary of the pectoral muscle found by the AP algorithm has average error less than 5 mm.     

A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry. First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation scheme. Features for breast density categorization are extracted, including a new fractal dimension-related feature, and support vector machines (SVMs) are employed for classification, achieving accuracy of up to 85.7%. Most of these properties are used to extract a new set of statistical features for each breast; the differences among these feature values from the two images of each pair of mammograms are used to detect breast asymmetry, using an one-class SVM classifier, which resulted in a success rate of 84.47%. This composite methodology has been applied to the miniMIAS database, consisting of 322 (MLO) mammograms -including 15 asymmetric pairs of images-, obtained via a (noisy) digitization procedure. The results were evaluated by expert radiologists and are very promising, showing equal or higher success rates compared to other related works, despite the fact that some of them used only selected portions of this specific mammographic database. In contrast, our methodology is applied to the complete miniMIAS database and it exhibits the reliability that is normally required for clinical use in CAD systems.     

Breast cancer image assessment using an adaptative network-based fuzzy inference system

In Brazil breast cancer is the foremost cause of fatality by cancer for women. Given that the causes are unidentified, it cannot be prevented. Mammography is one of the most reliable exams for breast cancer detection and it is based on image analysis by radiologists. Early detection is the key issue for breast cancer control and computer-aided diagnosis system can help ra diologists in detection and diagnosing breast abnormalities. Hybrid neuro-fuzzy systems are suitable for pattern recognition tasks and therefore useful for medical diagnosis support through pattern identification in mammographic images. This study presents an Adaptative Network-based Fuzzy Inference System (ANFIS) that classifies the mammographic images calcification region of interest as benign or malign and provides an important tool for breast cancer image assessment. The ANFIS model, utilized in the mammogram region of interest’s classification phase, reached a maximum accuracy rate of 99.75%.     

Automatic nipple detection in breast thermograms

Breast cancer is one of the most dangerous diseases for women. Detecting breast cancer in its early stage may lead to a reduction in mortality. Although the study of mammographies is the most common method to detect breast cancer, it is outperformed by the analysis of thermographies in dense tissue (breasts of young women). In the last two decades, several computer-aided diagnosis (CAD) systems for the early detection of breast cancer have been proposed. Breast cancer CAD systems consist of many steps, such as segmentation of the region of interest, feature extraction, classification and nipple detection. Indeed, the nipple is an important anatomical landmark in thermograms. The location of the nipple is invaluable in the analysis of medical images because it can be used in several applications, such as image registration and modality fusion. This paper proposes an unsupervised, automatic, accurate, simple and fast method to detect nipples in thermograms. The main stages of the proposed method are: human body segmentation, determination of nipple candidates using adaptive thresholding and detection of the nipples using a novel selection algorithm. Experiments have been carried out on a thermograms dataset to validate the proposed method, achieving accurate nipple detection results in real-time. We also show an application of the proposed method, breast cancer classification in dynamic images, where the new nipple detection technique is used to segment the region of the two breasts from the infrared image. A dataset of dynamic thermograms has been used to validate this application, achieving good results.     

基于解剖学特征的乳腺X线图像胸肌分割

提出了基于解剖学特征(纹理特征和形状特征)的乳腺X线图像胸肌区域分割方法. 融合边缘信息到谱聚类算法得到过分割图像. 根据区域的亮度分布和胸肌的三角形状特征,提出区域聚合算法, 从过分割图像中识别出胸肌边缘.该方法在322幅mini-MIAS (Mammographic image analysis society)乳腺图像和50幅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,该方法对不同大小、形状和亮度的胸肌分割具有较强的鲁棒性.     

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 novel image mining technique for classification of mammograms using hybrid feature selection

The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign, and malignant class. Total of 26 features including histogram intensity features and gray-level co-occurrence matrix features are extracted from mammogram images. A hybrid approach of feature selection is proposed, which approximately reduces 75% of the features, and new decision tree is used for classification. The most interesting one is that branch and bound algorithm that is used for feature selection provides the best optimal features and no where it is applied or used for gray-level co-occurrence matrix feature selection from mammogram. Experiments have been taken for a data set of 300 images taken from MIAS of different types with the aim of improving the accuracy by generating minimum number of rules to cover more patterns. The accuracy obtained by this method is approximately 97.7%, which is highly encouraging.     

融合多特征与先验信息的显著性目标检测

目的 图像的显著性目标检测是计算机视觉领域的重要研究课题。针对现有显著性目标检测结果存在的纹理细节刻画不明显和边缘轮廓显示不完整的问题,提出一种融合多特征与先验信息的显著性目标检测方法,该方法能够高效而全面地获取图像中的显著性区域。方法 首先,提取图像感兴趣的点集,计算全局对比度图,利用贝叶斯方法融合凸包和全局对比度图获得对比度特征图。通过多尺度下的颜色直方图得到颜色空间图,根据信息熵定理计算最小信息熵,并将该尺度下的颜色空间图作为颜色特征图。通过反锐化掩模方法提高图像清晰度,利用局部二值算子（LBP）获得纹理特征图。然后,通过图形正则化（GR）和流行排序（MR）算法得到中心先验图和边缘先验图。最后,利用元胞自动机融合对比度特征图、颜色特征图、纹理特征图、中心先验图和边缘先验图获得初级显著图,再通过快速引导滤波器优化处理得到最终显著图。结果 在2个公开的数据集MSRA10K和ECSSD上验证本文算法并与12种具有开源代码的流行算法进行比较,实验结果表明,本文算法在准确率-召回率（PR）曲线、受试者工作特征（ROC）曲线、综合评价指标（F-measure）、平均绝对误差（MAE）和结构化度量指标（S-measure）等方面有显著提升,整体性能优于对比算法。结论 本文算法充分利用了图像的对比度特征、颜色特征、纹理特征,采用中心先验和边缘先验算法,在全面提取显著性区域的同时,能够较好地保留图像的纹理信息和细节信息,使得边缘轮廓更加完整,满足人眼的层次要求和细节要求,并具有一定的适用性。     

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms.