Pectoral Muscle Detection in Mammograms Using Local Statistical Features

Mammography is a primary imaging method for breast cancer diagnosis. It is an important issue to accurately identify and separate pectoral muscles (PM) from breast tissues. Hough-transform-based methods are commonly adopted for PM detection. But their performances are susceptible when PM edges cannot be depicted by straight lines. In this study, we present a new pectoral muscle identification algorithm which utilizes statistical features of pixel responses. First, the Anderson–Darling goodness-of-fit test is used to extract a feature image by assuming non-Gaussianity for PM boundaries. Second, a global weighting scheme based on the location of PM was applied onto the feature image to suppress non-PM regions. From the weighted image, a preliminary set of pectoral muscles boundary components is detected via row-wise peak detection. An iterative procedure based on the edge continuity and orientation is used to determine the final PM boundary. Our results on a public mammogram database were assessed using four performance metrics: the false positive rate, the false negative rate, the Hausdorff distance, and the average distance. Compared to previous studies, our method demonstrates the state-of-art performance in terms of four measures.     

Radon-Domain Detection of the Nipple and the Pectoral Muscle in Mammograms

In this paper, methods are presented for automatic detection of the nipple and the pectoral muscle edge in mammograms via image processing in the Radon domain. Radon-domain information was used for the detection of straight-line candidates with high gradient. The longest straight-line candidate was used to identify the pectoral muscle edge. The nipple was detected as the convergence point of breast tissue components, indicated by the largest response in the Radon domain. Percentages of false-positive (FP) and false-negative (FN) areas were determined by comparing the areas of the pectoral muscle regions delimited manually by a radiologist and by the proposed method applied to 540 mediolateral-oblique (MLO) mammographic images. The average FP and FN were 8.99% and 9.13%, respectively. In the detection of the nipple, an average error of 7.4 mm was obtained with reference to the nipple as identified by a radiologist on 1,080 mammographic images (540 MLO and 540 craniocaudal views).     

Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection

In computer-aided diagnosis systems for breast mammography, the pectoral muscle region can easily cause a high false positive rate and misdiagnosis due to its similar texture and low contrast with breast parenchyma. Pectoral muscle region segmentation is a crucial pre-processing step to identify lesions, and accurate segmentation in poor-contrast mammograms is still a challenging task. In order to tackle this problem, a novel method is proposed to automatically segment pectoral muscle region in this paper. The proposed method combines genetic algorithm and morphological selection algorithm, incorporating four steps: pre-processing, genetic algorithm, morphological selection, and polynomial curve fitting. For the evaluation results on different databases, the proposed method achieves average FP rate and FN rate of 2.03 and 6.90% (mini MIAS), 1.60 and 4.03% (DDSM), and 2.42 and 13.61% (INBreast), respectively. The results can be comparable performance in various metrics over the state-of-the-art methods.     

Robust Automatic Pectoral Muscle Segmentation from Mammograms Using Texture Gradient and Euclidean Distance Regression

In computer-aided diagnosis (CAD) of mediolateral oblique (MLO) view of mammogram, the accuracy of tissue segmentation highly depends on the exclusion of pectoral muscle. Robust methods for such exclusions are essential as the normal presence of pectoral muscle can bias the decision of CAD. In this paper, a novel texture gradient-based approach for automatic segmentation of pectoral muscle is proposed. The pectoral edge is initially approximated to a straight line by applying Hough transform on Probable Texture Gradient (PTG) map of the mammogram followed by block averaging with the aid of approximated line. Furthermore, a smooth pectoral muscle curve is achieved with proposed Euclidean Distance Regression (EDR) technique and polynomial modeling. The algorithm is robust to texture and overlapping fibro glandular tissues. The method is validated with 340 MLO views from three databases—including 200 randomly selected scanned film images from miniMIAS, 100 computed radiography images and 40 full-field digital mammogram images. Qualitatively, 96.75 % of the pectoral muscles are segmented with an acceptable pectoral score index. The proposed method not only outperforms state-of-the-art approaches but also accurately quantifies the pectoral edge. Thus, its high accuracy and relatively quick processing time clearly justify its suitability for CAD.     

基于金字塔结构的乳腺肿块自动检测方法

在乳腺图像中,肿块大多被埋没在复杂的、高密度的腺体背景中难以检测.针对这一问题,提出了一种基于金字塔结构的乳腺肿块自动检测方法.文中对几种典型的金字塔结构的构造方法做了比较;提出了一种使用BP人工神经网络用于实现低分辨率图像中肿块种子区域检测的新方法;提出了一种新的权值差别规则,同时添加了标志锥,使得生长算法不再严格受限于肿块种子的面积和形状.实验结果证明这种方法对于辅助临床医生诊断乳腺病变是有效的.     

Computer-Aided Detection of Architectural Distortion in Prior Mammograms of Interval Cancer

Architectural distortion is an important sign of breast cancer, but because of its subtlety, it is a common cause of false-negative findings on screening mammograms. This paper presents methods for the detection of architectural distortion in mammograms of interval cancer cases taken prior to the detection of breast cancer using Gabor filters, phase portrait analysis, fractal analysis, and texture analysis. The methods were used to detect initial candidates for sites of architectural distortion in prior mammograms of interval cancer and also normal control cases. A total of 4,224 regions of interest (ROIs) were automatically obtained from 106 prior mammograms of 56 interval cancer cases, including 301 ROIs related to architectural distortion, and from 52 prior mammograms of 13 normal cases. For each ROI, the fractal dimension and Haralick’s texture features were computed. Feature selection was performed separately using stepwise logistic regression and stepwise regression. The best results achieved, in terms of the area under the receiver operating characteristics curve, with the features selected by stepwise logistic regression are 0.76 with the Bayesian classifier, 0.73 with Fisher linear discriminant analysis, 0.77 with an artificial neural network based on radial basis functions, and 0.77 with a support vector machine. Analysis of the performance of the methods with free-response receiver operating characteristics indicated a sensitivity of 0.80 at 7.6 false positives per image. The methods have good potential in detecting architectural distortion in mammograms of interval cancer cases.     

Multiplexed Wavelet Transform Technique for Detection of Microcalcification in Digitized Mammograms

Wavelet transform (WT) is a potential tool for the detection of microcalcifications, an early sign of breast cancer. This article describes the implementation and evaluates the performance of two novel WT-based schemes for the automatic detection of clustered microcalcifications in digitized mammograms. Employing a one-dimensional WT technique that utilizes the pseudo-periodicity property of image sequences, the proposed algorithms achieve high detection efficiency and low processing memory requirements. The detection is achieved from the parent–child relationship between the zero-crossings [Marr-Hildreth (M-H) detector] /local extrema (Canny detector) of the WT coefficients at different levels of decomposition. The detected pixels are weighted before the inverse transform is computed, and they are segmented by simple global gray level thresholding. Both detectors produce 95% detection sensitivity, even though there are more false positives for the M-H detector. The M-H detector preserves the shape information and provides better detection sensitivity for mammograms containing widely distributed calcifications.Permission has been granted by the Departmental Research Committee, Department of Electronics, Cochin University of Science and Technology, for pursuing research in the field of automated detection of microcalcification in digitized mammograms.     

Quantitative Visually Lossless Compression Ratio Determination of JPEG2000 in Digitized Mammograms

The current study presents a quantitative approach towards visually lossless compression ratio (CR) threshold determination of JPEG2000 in digitized mammograms. This is achieved by identifying quantitative image quality metrics that reflect radiologists’ visual perception in distinguishing between original and wavelet-compressed mammographic regions of interest containing microcalcification clusters (MCs) and normal parenchyma, originating from 68 images from the Digital Database for Screening Mammography. Specifically, image quality of wavelet-compressed mammograms (CRs, 10:1, 25:1, 40:1, 70:1, 100:1) is evaluated quantitatively by means of eight image quality metrics of different computational principles and qualitatively by three radiologists employing a five-point rating scale. The accuracy of the objective metrics is investigated in terms of (1) their correlation (r) with qualitative assessment and (2) ROC analysis (A _z index), employing pooled radiologists’ rating scores as ground truth. The quantitative metrics mean square error, mean absolute error, peak signal-to-noise ratio, and structural similarity demonstrated strong correlation with pooled radiologists’ ratings (r, 0.825, 0.823, ?0.825, and ?0.826, respectively) and the highest area under ROC curve (A _z , 0.922, 0.920, 0.922, and 0.922, respectively). For each quantitative metric, the highest accuracy values of corresponding ROC curves were used to define metric cut-off values. The metrics cut-off values were subsequently used to suggest a visually lossless CR threshold, estimated to be between 25:1 and 40:1 for the dataset analyzed. Results indicate the potential of the quantitative metrics approach in predicting visually lossless CRs in case of MCs in mammography.     

Automatic Detection of Pectoral Muscle Using Average Gradient and Shape Based Feature

In medio-lateral oblique view of mammogram, pectoral muscle may sometimes affect the detection of breast cancer due to their similar characteristics with abnormal tissues. As a result pectoral muscle should be handled separately while detecting the breast cancer. In this paper, a novel approach for the detection of pectoral muscle using average gradient- and shape-based feature is proposed. The process first approximates the pectoral muscle boundary as a straight line using average gradient-, position-, and shape-based features of the pectoral muscle. Straight line is then tuned to a smooth curve which represents the pectoral margin more accurately. Finally, an enclosed region is generated which represents the pectoral muscle as a segmentation mask. The main advantage of the method is its' simplicity as well as accuracy. The method is applied on 200 mammographic images consisting 80 randomly selected scanned film images from Mammographic Image Analysis Society (mini-MIAS) database, 80 direct radiography (DR) images, and 40 computed radiography (CR) images from local database. The performance is evaluated based upon the false positive (FP), false negative (FN) pixel percentage, and mean distance closest point (MDCP). Taking all the images into consideration, the average FP and FN pixel percentages are 4.22%, 3.93%, 18.81%, and 6.71%, 6.28%, 5.12% for mini-MIAS, DR, and CR images, respectively. Obtained MDCP values for the same set of database are 3.34, 3.33, and 10.41 respectively. The method is also compared with two well-known pectoral muscle detection techniques and in most of the cases, it outperforms the other two approaches.     

独立分量分析在乳腺钼靶X片感兴趣区域提取中的应用

为乳腺癌早期诊断和乳腺X线影像微钙化点计算机辅助检测的前期预处理,本研究提出基于独立分量分析（ICA）的自动提取新算法并且将其应用于乳腺图像感兴趣区域的自动提取.其具体思路是：（1）将乳腺区域图像提取成等大的子图像作为待测乳腺图像感兴趣区域;（2）将ICA应用于乳腺图像感兴趣区域得到基图像;（3）将待识别乳腺图像感兴趣区域在基图像所构成的子空间进行投影求得待测乳腺图像感兴趣区域的特征矢量;（4）用人工神经网络分类方法进行乳腺图像感兴趣区域的模式判别.对临床实际病例的试验结果表明,该方法的检出率为91%,与同类研究检出率相当.本研究方法简单有效,并具有较高的智能性,为ROI的自动提取提供了新的研究思路.     

Effects of Monochromatic Light on Developmental Changes in Satellite Cell Population of Pectoral Muscle in Broilers During Early Posthatch Period

To investigate the effects of various monochromatic lights on early posthatch changes in satellite cell mitotic activity of pectoral muscle, a total of 416 newly hatched broilers were exposed to blue light (BL), green light (GL), red light (RL), and white light (WL) by light emitting diode system for 3 weeks, respectively. Both, in culture and in vivo studies showed that after hatching, the relative number of satellite cells altered in correlation. The enhancement of satellite cell mitotic activity peaked at post‐hatching day (P) 3 and then declined with age concomitantly with the rise in satellite cell differentiation and reduction of satellite cell proliferation. These alterations became more obvious in GL than in RL. The data suggested that early posthatch changes in satellite cell population of broilers occurred through the two different processes, i.e., cellular generation (before P3) and cellular degeneration (after P3). GL promoted significantly the broiler satellite cells to proliferate before P3 and to differentiate after P3. In addition, the circulating insulin‐like growth factor‐I (IGF‐I) levels were higher in GL and BL groups versus WL and RL groups at P3 and P5 indicating that IGF‐I plays a central role for GL illumination promoting broiler satellite cell myogenic processes during early posthatch stages. Anat Rec 293:1315–1324, 2010. © 2010 Wiley‐Liss, Inc.     

乳腺钼靶X线影像中结构扭曲的特征提取研究

本研究以灰度共生矩阵描述乳腺钼靶X线影像中结构扭曲的纹理特征.对学习样本（乳腺结构扭曲样本44个,正常样本78个）,计算五个反映纹理性质的特征参数,根据相应的Fisher系数,确定最适合作为分类依据的特征参数或特征参数组合.用线性判别分析对测试样本（乳腺结构扭曲样本43个,正常样本78个）进行分类.分类结果表明本研究确定的纹理特征熵（ENT）是识别乳腺结构扭曲的最佳统计参数（分类正确率达78.5%、ROC曲线下的面积为0.786）.     

Marker-Controlled Watershed for Lesion Segmentation in Mammograms

Lesion segmentation, which is a critical step in computer-aided diagnosis system, is a challenging task as lesion boundaries are usually obscured, irregular, and low contrast. In this paper, an accurate and robust algorithm for the automatic segmentation of breast lesions in mammograms is proposed. The traditional watershed transformation is applied to the smoothed (by the morphological reconstruction) morphological gradient image to obtain the lesion boundary in the belt between the internal and external markers. To automatically determine the internal and external markers, the rough region of the lesion is identified by a template matching and a thresholding method. Then, the internal marker is determined by performing a distance transform and the external marker by morphological dilation. The proposed algorithm is quantitatively compared to the dynamic programming boundary tracing method and the plane fitting and dynamic programming method on a set of 363 lesions (size range, 5–42 mm in diameter; mean, 15 mm), using the area overlap metric (AOM), Hausdorff distance (HD), and average minimum Euclidean distance (AMED). The mean ± SD of the values of AOM, HD, and AMED for our method were respectively 0.72 ± 0.13, 5.69 ± 2.85 mm, and 1.76 ± 1.04 mm, which is a better performance than two other proposed segmentation methods. The results also confirm the potential of the proposed algorithm to allow reliable segmentation and quantification of breast lesion in mammograms.     

Fractal Analysis of Contours of Breast Masses in Mammograms

Fractal analysis has been shown to be useful in image processing for characterizing shape and gray-scale complexity. Breast masses present shape and gray-scale characteristics that vary between benign masses and malignant tumors in mammograms. Limited studies have been conducted on the application of fractal analysis specifically for classifying breast masses based on shape. The fractal dimension of the contour of a mass may be computed either directly from the 2-dimensional (2D) contour or from a 1-dimensional (1D) signature derived from the contour. We present a study of four methods to compute the fractal dimension of the contours of breast masses, including the ruler method and the box counting method applied to 1D and 2D representations of the contours. The methods were applied to a data set of 111 contours of breast masses. Receiver operating characteristics (ROC) analysis was performed to assess and compare the performance of fractal dimension and four previously developed shape factors in the classification of breast masses as benign or malignant. Fractal dimension was observed to complement the other shape factors, in particular fractional concavity, in the representation of the complexity of the contours. The combination of fractal dimension with fractional concavity yielded the highest area (A ( z )) under the ROC curve of 0.93; the two measures, on their own, resulted in A ( z ) values of 0.89 and 0.88, respectively.     

神经电信号中眼轮匝肌功能状态的识别

目的:提取能反映眼轮匝肌功能活动的狗面神经颧支的神经电信号(ENG),通过对ENG信号的分析,识别出眼轮匝肌的功能状态。方法:利用植入到狗面神经颧支周围的Cuff电极提取有闭眼动作发生期间的神经电信号,采用幅度阈值法,通过对ENG信号的分析,识别出闭眼动作发生时眼轮匝肌的收缩动作。结果:研究中我们提取到了能反映闭眼动作发生的ENG信号,并且通过对信号的分析,识别出了眼轮匝肌收缩动作的发生。结论:可以通过对眼轮匝肌支配神经上ENG信号的分析,监控眼轮匝肌的功能状态。     

Perceived Sufficiency of Full-Field Digital Mammograms With and Without Irreversible Image Data Compression for Comparison with Next-Year Mammograms

Problems associated with the large file sizes of digital mammograms have impeded the integration of digital mammography with picture archiving and communications systems. Digital mammograms irreversibly compressed by the novel wavelet Access Over Network (AON) compression algorithm were compared with lossless-compressed digital mammograms in a blinded reader study to evaluate the perceived sufficiency of irreversibly compressed images for comparison with next-year mammograms. Fifteen radiologists compared the same 100 digital mammograms in three different comparison modes: lossless-compressed vs 20:1 irreversibly compressed images (mode 1), lossless-compressed vs 40:1 irreversibly compressed images (mode 2), and 20:1 irreversibly compressed images vs 40:1 irreversibly compressed images (mode 3). Compression levels were randomly assigned between monitors. For each mode, the less compressed of the two images was correctly identified no more frequently than would occur by chance if all images were identical in compression. Perceived sufficiency for comparison with next-year mammograms was achieved by 97.37% of the lossless-compressed images and 97.37% of the 20:1 irreversibly compressed images in mode 1, 97.67% of the lossless-compressed images and 97.67% of the 40:1 irreversibly compressed images in mode 2, and 99.33% of the 20:1 irreversibly compressed images and 99.19% of the 40:1 irreversibly compressed images in mode 3. In a random-effect analysis, the irreversibly compressed images were found to be noninferior to the lossless-compressed images. Digital mammograms irreversibly compressed by the wavelet AON compression algorithm were as frequently judged sufficient for comparison with next-year mammograms as lossless-compressed digital mammograms.     

基于双阈值的乳腺X线图像的微钙检测

在世界范围内乳腺癌已成为导致妇女死亡的最主要癌症之一。由于乳腺癌诱因不明，目前对其的早期发现成为预防和治疗的最重要措施。微钙簇是恶性乳癌主要的早期标志，在数字化乳腺X线图像中，微钙不仅面积小，且与背景的亮度差小，因此检测出所有微钙点是当前所面临的一个技术挑战。我们提出了一种基于LoG边缘检测的双闽值微钙检测方法，使微钙点得以准确定位。文中提出了两个准则以确定两个不同的阈值。实验结果显示，该算法可以在抑制噪声的同时准确定位微钙点。