Identification of clustered microcalcifications on digitized mammograms using morphology and topography-based computer-aided detection schemes. A preliminary experiment

RATIONALE AND OBJECTIVES: A mathematical morphology-based computer-aided detection (CAD) scheme for the identification of clustered microcalcifications was developed and tested. The potential for improving either sensitivity or specificity by combining the results with those previously reported was investigated. METHODS: The CAD scheme presented here is based on mathematical morphology and a series of simple rule-based criteria for the identification of clustered microcalcifications. A database of 105 digitized mammograms was used for training and rule setting of the scheme. A test set of 191 digitized mammograms was used to evaluate its performance. The same test set had been used to evaluate a multilayer, topography-based scheme. The results obtained by the two schemes were then combined using logical OR and AND operations. RESULTS: The morphology-based and topography-based CAD schemes performed at sensitivities of 82.9% and 89.5%, with false-positive detection rates of 1.3 and 0.4 per image, respectively. A logical OR operation resulted in 95.4% sensitivity. An AND operation achieved 76.2% sensitivity, with no false identifications on 93% of images. CONCLUSIONS: By combining the results of the morphology-based and the topography-based schemes, either sensitivity or specificity can be improved.     

An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms

A shift-invariant artificial neutral network (SIANN) has been applied to eliminate the false-positive detections reported by a rule-based computer aided-diagnosis (CAD) scheme developed in our laboratory. Regions of interest (ROIs) were selected around the centers of the rule-based CAD detections and analyzed by the SIANN. In our previous study, background-trend correction and pixel-value normalization were used as the preprocessing of the ROIs prior to the SIANN. A ROI is classified as a positive ROI, if the total number of microcalcifications detected in the ROI is greater than a certain number. In this study, modifications were made to improve the performance of the SIANN. First, the preprocessing is removed because the result of the background-trend correction is affected by the size of ROIs. Second, image-feature analysis is employed to the output of the SIANN in an effort to eliminate some of the false detections by the SIANN. In order to train the SIANN to detect microcalcifications and also to extract image features of microcalcifications, the zero-mean-weight constraint and training-free-zone techniques have been developed. A cross-validation training method was also applied to avoid the overtraining problem. The performance of the SIANN was evaluated by means of ROC analysis using a database of 39 mammograms for training and 50 different mammograms for testing. The analysis yielded an average area under the ROC curve (A(z)) of 0.90 for the testing set. Approximately 62% of false-positive clusters detected by the rule-based scheme were eliminated without any loss of the true-positive clusters by using the improved SIANN with image feature analysis techniques.     

Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms

We are developing a computer-aided diagnosis (CAD) scheme for detection of clustered microcalcifications in digital mammograms. The use of an empirically chosen wavelet and scale combination for detection of microcalcifications as an initial step of the CAD scheme has been reported by us previously. In this study, we developed a technique for optimizing the weights at individual scales in the wavelet transform to improve the performance of our CAD scheme based on the supervised learning method. In the learning process, an error function was formulated to represent the difference between a desired output and the reconstructed image obtained from weighted wavelet coefficients for a given mammogram. The error function was then minimized by modifying the weights for wavelet coefficients by means of a conjugate gradient algorithm. The Least Asymmetric Daubechies' wavelets were optimized with 297 regions of interest (ROIs) as a training set by a jackknife method. The performance of the optimally weighted wavelets was evaluated by means of receiver-operating characteristic (ROC) analysis by use of the above set of ROIs. The analysis yielded an average area under the ROC curve of 0.92, which outperforms the difference-image technique used in our existing CAD scheme, as well as the partial reconstruction method used in our previous study.     

Digital optical card. A promising technology for documentation and communication of images

PURPOSE: To assess a patient-oriented digital optical card (OC) for documentation and communication of images using the analysis of breast microcalcifications to illustrate its resolution power. METHODS: Fifty film mammograms with histologically proved clustered microcalcifications were digitized using a 5 lp/mm CCD-scanner. A region of interest containing the cluster was selected for documentation on an OC as an overview OC-image and as a magnified OC-image (5 lp/mm). The shape (spherical/nonspherical) as well as the total number of microcalcifications were quantitatively analyzed by 2 radiologists. RESULTS: The detection rate for total number of overall and spherical microcalcifications using digital media was significantly reduced (p < 0.01) compared to analog mammography. There were no significant differences in the detection rate of nonspherical microcalcifications between film mammograms (100%) and magnified section OC-images (92.7%). The overview OC-image revealed 72% of those calcifications (p < 0.01). CONCLUSION: According to our results, this technology is not appropriate for diagnosis of breast microcalcifications, but may be a promising communication digital medium for transmitting an image/report unit to referring physicians.     

A novel approach to microcalcification detection using fuzzy logic technique

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.     

Image compression in digital mammography: effects on computerized detection of subtle microcalcifications

Our previous receiver operating characteristic (ROC) study indicated that the detection accuracy of microcalcifications by radiologists is significantly reduced if mammograms are digitized at 0.1 mm x 0.1 mm. Our recent study also showed that detection accuracy by computer decreases as the pixel size increases from 0.035 mm x 0.035 mm. It is evident that very large matrix sizes have to be used for digitizing mammograms in order to preserve the information in the image. Efficient compression techniques will be needed to facilitate communication and archiving of digital mammograms. In this study, we evaluated two compression techniques: full frame discrete cosine transform (DCT) with entropy coding and Laplacian pyramid hierarchical coding (LPHC). The dependence of their efficiency on the compression parameters was investigated. The techniques were compared in terms of the trade-off between the bit rate and the detection accuracy of subtle microcalcifications by an automated detection algorithm. The mean-square errors in the reconstructed images were determined and the visual quality of the error images was examined. It was found that with the LPHC method, the highest compression ratio achieved without a significant degradation in the detectability was 3.6:1. The full frame DCT method with entropy coding provided a higher compression efficiency of 9.6:1 at comparable detection accuracy. The mean-square errors did not correlate with the detection accuracy of the microcalcifications. This study demonstrated the importance of determining the quality of the decompressed images by the specific requirements of the task for which the decompressed images are to be used. Further investigation is needed for selection of optimal compression technique for digital mammograms.     

Digital mammography: M-channel quadrature mirror filters (QMFs) for microcalcification extraction

Multiresolution methods are reported for feature extraction in breast cancer screening using digital mammography. The initial application is directed at the detection of microcalcification clusters (MCCs). Quadrature mirror filter (QMF) banks, using both two and three channel are proposed for the first time for both multiresolution decomposition and reconstruction. These filters are specifically tailored for automatic extraction of MCCs. The QMF multiresolution methods are compared to two channel tree structured wavelet transforms (TSWTs) methods previously reported. The QMF filters are preceded by an advanced tree structured nonlinear filter for noise suppression, prior to feature extraction, in order to minimize the false positive (FP) detection rate in digital mammography. The relative performance of these methods were evaluated using both simulated images and fifteen representative digitized mammograms containing biopsy proven microcalcification clusters. Similar high sensitivity (true positive (TP) detection rate (100%) and high specificity (0.6 average false positive (FP) MCC's/image) were observed, substantially better than more traditional approaches using single scale filters. The three channel QMF method, however, demonstrated better detail preservation of MCC's extracted compared to the two channel method. Detail preservation is important for the characterization of MCC's or individual microcalcifications in cancer screening.     

Development of an improved CAD scheme for automated detection of lung nodules in digital chest images

Lung cancer is the leading cause of cancer deaths in men and women in the United States, with a 5-year survival rate of only about 13%. However, this survival rate can be improved to 47% if the disease is diagnosed and treated at an early stage. In this study, we developed an improved computer-aided diagnosis (CAD) scheme for the automated detection of lung nodules in digital chest images to assist radiologists, who could miss up to 30% of the actually positive cases in their daily practice. Two hundred PA chest radiographs, 100 normals and 100 abnormals, were used as the database for our study. The presence of nodules in the 100 abnormal cases was confirmed by two experienced radiologists on the basis of CT scans or radiographic follow-up. In our CAD scheme, nodule candidates were selected initially by multiple gray-level thresholding of the difference image (which corresponds to the subtraction of a signal-enhanced image and a signal-suppressed image) and then classified into six groups. A large number of false positives were eliminated by adaptive rule-based tests and an artificial neural network (ANN). The CAD scheme achieved, on average, a sensitivity of 70% with 1.7 false positives per chest image, a performance which was substantially better as compared with other studies. The CPU time for the processing of one chest image was about 20 seconds on an IBM RISC/6000 Powerstation 590. We believe that the CAD scheme with the current performance is ready for initial clinical evaluation.     

Computerized detection of masses in digitized mammograms using single-image segmentation and a multilayer topographic feature analysis

RATIONALE AND OBJECTIVES: We developed and evaluated a computer-aided detection (CAD) scheme for masses in digitized mammograms. METHODS: A multistep CAD scheme was developed and tested. The method uses a technique of single-image segmentation with Gaussian bandpass filtering to yield a high sensitivity for mass detection. A rule-based multilayer topographic feature analysis method is then used to classify suspected regions. A set of 260 cases, including 162 verified masses, was divided into two subsets; one set was used to set the rule-based classification and one was used to test the performance of the scheme. RESULTS: In a preliminary clinical study, the implemented detection scheme yielded 98% sensitivity with a false-positive detection rate of less than one false-positive region per image. CONCLUSION: Single-image segmentation methods seem to have high sensitivity in selecting true-positive mass regions in the first stage of a CAD scheme. A multilayer topographic image feature analysis method in the second stage of a CAD scheme has the potential to significantly reduce the false-positive detection rate.     

Storage phosphor direct magnification mammography in comparison with conventional screen-film mammography--a phantom study

Contact mammography with current photostimulable storage phosphors is hampered by its low spatial resolution. Detail visualization can be improved by geometric magnification radiography which enlarges small details to exceed inherent image noise. This study compares storage phosphor mammography using a dedicated direct magnification system with state-of-the-art conventional screen-film mammography. Storage phosphor direct magnification survey views (1.7x) and spot views (4x) were obtained with a prototype mammography unit providing focal spot sizes of 120-40 microns. Conventional technique screen-film survey views (1.1x) and spot views (1.8x) served as comparison. A contrast detail study and a receiver operating characteristic (ROC) analysis using an anthropomorphic breast phantom with superimposed microcalcifications was performed. Contrast detail resolution in the digital and conventional survey views were equivalent. For the spot views, contrast detail resolution was significantly higher with the digital technique (p < 0.001). ROC analysis of 400 observations demonstrated a significantly higher performance (p < 0.001) with digital images versus conventional screen-film mammograms. The area under the ROC curve (Az) in the digital survey views was 0.76 +/- 0.07 versus 0.59 +/- 0.02 in the conventional technique. In digital spot views, Az was 0.82 +/- 0.07 as compared with 0.66 +/- 0.04 in the conventional spot views. These results suggest that storage phosphor digital mammography in conjunction with direct geometric magnification technique may be superior to conventional screen-film mammography in the detection of microcalcifications.     

Calculation of exposure chamber leak rate with thermal correction: a measure of chamber integrity

RATIONALE AND OBJECTIVES: The authors prospectively tested the performance of a single numeric classifier constructed from a discriminative analysis classification system based on automatic computer-extracted quantitative features of clustered microcalcifications. MATERIALS AND METHODS: Mammographically detected clustered microcalcifications in patients who had been referred for biopsy were digitized at 600 dpi with an 8-bit gray scale. A software program was developed to extract features automatically from digitized images to describe the clustered microcalcifications quantitatively. The significance of these features was evaluated by using the Wilcoxon test, the Welch modified two-sample t test, and the two-sample Kolmogorov-Smirnov test. A discriminant analysis pattern recognition system was constructed to generate a single numeric classifier for each case, based on the extracted features. This system was trained on 137 archival known reference cases and its performance tested on 24 unknown prospective cases. The results were evaluated by using receiver operating characteristic analysis. RESULTS: Thirty-seven extracted parameters demonstrated a statistically significant difference between the values for the benign and for the malignant lesions. Seven independent factors were selected to construct the classifier and to evaluate the unknown prospective cases. The area under the receiver operating characteristic curve for the prospective cases was 0.88. CONCLUSION: A pattern recognition classifier based on quantitative features for clustered microcalcifications at screen-film mammography was found to perform satisfactorily. The software may be of value in the interpretation of mammographically detected microcalcifications.     

Radiosurgical correlations of 61 breast microcalcification foci

OBJECTIVE. Diagnostic value of breast clustered microcalcifications discovered by mammography. DESIGN. A retrospective study. SETTING. Oncology Center of Rennes. SUBJECTS. 58 women (study group) with breast clustered microcalcifications without palpable tumour were operated. SURGERY. Prior to surgical removal of microcalcification, needle localization was performed. Histological results. We observed, 36 benign lesions (59%), 25 carcinomas (45%), 10 of them in situ and 15 infiltrative. RESULTS. Different radiological parameters were studied in relation to histological results, the vermicular morphology of microcalcification, an increased number, their triangular aspect, provide clue to the presence of breast carcinoma. The cluster of stippled calcification is not, in our series suggestive of a carcinoma, but also requires histopathological study, owig to the fact that in such cases, we have as many carcinomas as benign lesions. FINDING. Careful analysis of microcalcifications, within the clinical context, ensures a safe attitude, and enables one to operate only carcinomas.     

Computer-assisted evaluation of mammography images. Initial clinical experiences

Preceding studies have shown that a second independent reviewer of conventional mammographies increases the detection rate of features typical for malignancy by up to 15%. METHODS: In order to test a computer-aided diagnostic (CAD) system (ImageChecker, R2 Technology, USA) for the detection of pathologic criteria in conventional mammography, 96 mammographies were retrospectively evaluated using ImageChecker. Thirty-five of these mammographies had been diagnosed as not showing pathologies, and 61 had depicted histologically confirmed malignancy. RESULTS: Detecting 41 of 61 breast malignancies, ImageChecker showed a diagnostic sensitivity of 70.5%. All malignancies accompanied by microcalcifications were identified by ImageChecker, whereas 18 cases characterized by parenchymal opacity without microcalcifications were not marked. On the average, 1.95 markers per image were set, giving a total of 187 markers in this study. 63% of all markers showed normal tissue and were thus false positive. CONCLUSIONS: Pathologic parenchymal opacities in mammography are a well-known problem for all CAD systems in use. Despite this major drawback, even now ImageChecker can provide tremendous support in routine interpretation of conventional mammographies.     

Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms

The objective of this research is to model the mammographic parenchymal, ductal patterns and enhance the microcalcifications using deterministic fractal approach. According to the theory of deterministic fractal geometry, images can be modeled by deterministic fractal objects which are attractors of sets of two-dimensional (2-D) affine transformations. The iterated functions systems and the collage theorem are the mathematical foundations of fractal image modeling. In this paper, a methodology based on fractal image modeling is developed to analyze and model breast background structures. We show that general mammographic parenchymal and ductal patterns can be well modeled by a set of parameters of affine transformations. Therefore, microcalcifications can be enhanced by taking the difference between the original image and the modeled image. Our results are compared with those of the partial wavelet reconstruction and morphological operation approaches. The results demonstrate that the fractal modeling method is an effective way to enhance microcalcifications. It may also be able to improve the detection and classification of microcalcifications in a computer-aided diagnosis system.     

Application of artificial neural networks for quantitative analysis of image data in chest radiographs for detection of interstitial lung disease

The authors have developed an automated computeraided diagnostic (CAD) scheme by using artificial neural networks (ANNs) on quantitative analysis of image data. Three separate ANNs were applied for detection of interstitial disease on digitized chest images. The first ANN was trained with horizontal profiles in regions of interest (ROIs) selected from normal and abnormal chest radiographs for distinguishing between normal and abnormal patterns. For training and testing of the second ANN, the vertical output patterns obtained from the 1st ANN were used for each ROI. The output value of the second ANN was used to distinguish between normal and abnormal ROIs with interstitial infiltrates. If the ratio of the number of abnormal ROIs to the total number of all ROIs in a chest image was greater than a specified threshold level, the image was classified as abnormal. In addition, the third ANN was applied to distinguish between normal and abnormal chest images. The combination of the rule-based method and the third ANN also was applied to the classification between normal and abnormal chest images. The performance of the ANNs was evaluated by means of receiver operating characteristic (ROC) analysis. The average Az value (area under the ROC curve) for distinguishing between normal and abnormal cases was 0.976 +/- 0.012 for 100 chest radiographs that were not used in training of ANNs. The results indicate that the ANN trained with image data can learn some statistical properties associated with interstitial infiltrates in chest radiographs.     

Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces

We are developing computerized feature extraction and classification methods to analyze malignant and benign microcalcifications on digitized mammograms. Morphological features that described the size, contrast, and shape of microcalcifications and their variations within a cluster were designed to characterize microcalcifications segmented from the mammographic background. Texture features were derived from the spatial gray-level dependence (SGLD) matrices constructed at multiple distances and directions from tissue regions containing microcalcifications. A genetic algorithm (GA) based feature selection technique was used to select the best feature subset from the multi-dimensional feature spaces. The GA-based method was compared to the commonly used feature selection method based on the stepwise linear discriminant analysis (LDA) procedure. Linear discriminant classifiers using the selected features as input predictor variables were formulated for the classification task. The discriminant scores output from the classifiers were analyzed by receiver operating characteristic (ROC) methodology and the classification accuracy was quantified by the area, Az, under the ROC curve. We analyzed a data set of 145 mammographic microcalcification clusters in this study. It was found that the feature subsets selected by the GA-based method are comparable to or slightly better than those selected by the stepwise LDA method. The texture features (Az = 0.84) were more effective than morphological features (Az = 0.79) in distinguishing malignant and benign microcalcifications. The highest classification accuracy (Az = 0.89) was obtained in the combined texture and morphological feature space. The improvement was statistically significant in comparison to classification in either the morphological (p = 0.002) or the texture (p = 0.04) feature space alone. The classifier using the best feature subset from the combined feature space and an appropriate decision threshold could correctly identify 35% of the benign clusters without missing a malignant cluster. When the average discriminant score from all views of the same cluster was used for classification, the Az value increased to 0.93 and the classifier could identify 50% of the benign clusters at 100% sensitivity for malignancy. Alternatively, if the minimum discriminant score from all views of the same cluster was used, the Az value would be 0.90 and a specificity of 32% would be obtained at 100% sensitivity. The results of this study indicate the potential of using combined morphological and texture features for computer-aided classification of microcalcifications.     

基于深度学习的宫颈癌异常细胞快速检测方法

宫颈癌是严重危害妇女健康的恶性肿瘤，威胁着女性的生命，而通过基于图像处理的细胞学筛查是癌前筛查的最为广泛的检测方法。近年来，随着以深度学习为代表的机器学习理论的发展，卷积神经网络以其强有效的特征提取能力取得了图像识别领域的革命性突破，被广泛应用于宫颈异常细胞检测等医疗影像分析领域。但由于病理细胞图像具有分辨率高和尺寸大的特点，且其大多数局部区域内都不含有细胞簇，深度学习模型采用穷举候选框的方法进行异常细胞的定位和识别时，经过穷举候选框获得的子图大部分都不含有细胞簇。当子图数量逐渐增加时，大量不含细胞簇的图像作为目标检测网络输入会使图像分析过程存在冗余时长，严重减缓了超大尺寸病理图像分析时的检测速度。本文提出一种新的宫颈癌异常细胞检测策略，针对使用膜式法获得的病理细胞图像，通过基于深度学习的图像分类网络首先判断局部区域是否出现异常细胞，若出现则进一步使用单阶段的目标检测方法进行分析，从而快速对异常细胞进行精确定位和识别。实验表明，本文提出的方法可提高一倍的宫颈癌异常细胞检测速度。      

Cyclins: relevance of subcellular localization in cell cycle control

OBJECTIVE: To develop a filter utilizing mathematical theory to extract the skeletal patterns of trabecular bone. METHODS: Studies of morphology in the extraction of patterns of calcification in mammograms provided the theoretical framework. Using these studies as a basis, a morphological filter was applied to extract skeletal patterns from digital images of trabecular bone. Sequential images (subset) were combined in a structured fashion to create an aggregate (sumset) which compared with the original images, skeleton and line skeleton images. RESULTS: Binary images of the skeletal patterns in continuous, round and mesh-like forms were obtained from the original images by processing with the skeleton operation using a disc-shaped single structuring element. The line skeleton operation using line structuring elements with constant directions allowed the extraction of linear and discontinuous patterns. Both the skeleton and line skeleton operations extracted binary subset images which depicted skeletal patterns correlating with the operation sequence. CONCLUSIONS: Modification of the morphological filter enhanced the extraction of skeletal characteristics of trabecular bone. A morphological filter may be a useful adjunct in computer-aided structural analysis of bone.     

The uroguanylin gene (Guca1b) is linked to guanylin (Guca2) on mouse chromosome 4

PURPOSE: To evaluate how anode-filter combinations influence image quality in and mean glandular dose to breasts of different thicknesses and compositions. MATERIALS AND METHODS: Mammograms were obtained with a molybdenum (Mo) anode and a Mo filter at 26 kVp, a Mo anode and a rhodium (Rh) filter at 27 kVp, or a tungsten (W) anode and a Rh filter at 26 kVp in 965 women. One anode-filter-tube voltage combination was used in the right breast and another in the left. The mean glandular dose to each breast was calculated. RESULTS: Image contrast was highest in the Mo-Mo mammograms. However, depiction of the glandular tissue, pectoral muscle, and skin and subcutis was significantly (P < .001) better with the Mo-Rh and the W-Rh than with the Mo-Mo combination. The average mean absorbed doses to the glandular tissue for W-Rh and Mo-Rh were 50% and 75%, respectively, of that for Mo-Mo. CONCLUSION: Breast thickness is the most important parameter in selection of an anode-filter-tube voltage combination. Compared with Mo-Mo, both Mo-Rh and W-Rh gave good image quality of the mammary gland and a considerably lower absorbed dose. Mo-Rh-27 kVp is recommended for breast thicknesses of 60 mm or less; W-Rh-26 kVp, for breast thicknesses of greater than 60 mm.