Detection of microcalcifications in mammograms using statistical measures based region-growing

In this paper, we describe an algorithm for enhancement and detection of microcalcifications in digital mammograms. The microcalcifications are first enhanced by a undecimated wavelet transform and segmented by a classification based method. After spatial features are extracted from the segmented images, a neural network and a Bayes classifier are used to identify clustered microcalcifications. We have tested this algorithm on 40 regions-of-interest images extracted from the database provided by the University Hospital of Nijmegen.

The appearance of micro-calcifications in mammograms is a crucial early sign of breast cancer. Automatic micro-calcification detection techniques play an important role in cancer diagnosis and treatment. This, however, still remains a challenging task. This paper presents novel algorithms for the detection of micro-calcifications using stochastic resonance (SR) noise. In these algorithms, a suitable dose of noise is added to the abnormal mammograms such that the performance of a suboptimal lesion detector is improved without altering the detector's parameters. First, a SR noise-based detection approach is presented to improve some suboptimal detectors which suffer from model mismatch due to the Gaussian assumption. Furthermore, a SR noise-based detection enhancement framework is presented to deal with more general model mismatch cases. Our algorithms and the framework are tested on a set of 75 representative abnormal mammograms. They yield superior performance when compared with several classification and detection approaches developed in our work as well as those available in the literature.

A new method for detecting microcalcifications in mammograms is described. In this method, the mammogram image is first processed by a subband decomposition filterbank. The bandpass subimage is divided into overlapping square regions in which skewness and kurtosis as measures of the asymmetry and impulsiveness of the distribution are estimated. The detection method utilizes these two parameters. A region with high positive skewness and kurtosis is marked as a region of interest. Simulation results show that this method is successful in detecting regions with microcalcifications.

Mammography and finding of suspicious masses during self-examinations and clinical breast examinations form the primary screening tools for early detection of breast cancer. Mammography is essential for early detection of cancer, prior to any other means. Many abnormalities other than cancer are shown by screening mammography, leading to biopsy of the suspicious defect. Any enhancement that reduces the number of unnecessary biopsies is welcomed. Digital enhancement may aid in early detection of some patterns, such as microcalcification clusters, indicating the onset of DCIS (ductal carcinoma in situ) that accounts for 20% of all mammographically detected breast cancers, and early detection may enable a complete cure. The individual calcifications are hard to detect, due to their size and shape variability and the inhomogeneous background structure. Our study addresses only early detection of microcalcifications. We present an algorithm which locates microcalcifications based on local gray-scale variability, tissue structures and image statistics. The mammographs are digitally enhanced to accent textures and a previously developed threshold filter creates a binary mask of the calcification's spatial location.

Clustered microcalcifications are often the first sign of breast cancer in a mammogram. Nevertheless, all clustered microcalcifications are not found by an individual radiologist reading a mammogram. The use of a second reader may find those clusters of microcalcifications not found by the first reader, thereby improving the sensitivity of detecting clustered microcalcifications. Our laboratory has developed a computerized scheme for the detection of clustered microcalcifications, which can act like a second reader, that is undergoing clinical evaluation. This paper concerns the feature analysis stage of the computer scheme, which is designed to remove some of the false-computer detections. We have examined three methods of feature analysis, namely, rule based (the method currently used), an artificial neural network (ANN), and a combined method. In an independent database of 50 images, at a sensitivity of 83%, the average number of false positive (FP) detections per image was: 1.9 for rule-based, 1.6 for ANN, and 0.8 for the combined method. We demonstrate that the combined method performs best because each of the two stages eliminates different types of false positives.

Due to the fact that digital mammography is not yet available, conventional films have to be digitized for computer aided diagnosis (CAD) applications. The nascency of the digital image — from exposure, development to digitization — has crucial influence on the performance of the CAD system [1].KeywordsCongestive Heart FailureMean Arterial PressureDigital MammographyVentricular Filling PressureDiuretic TreatmentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Breast cancer is one of the most common cancer among women and the survival rate tends to be low when its stage found high when treated. To improve breast cancer survival, early detection is critical. There are two ways of detection for breast cancer: early diagnosis and screening. To make an accurate diagnosis in the early stage of breast cancer, the appearance of masses and micro-calcifications on the mammography image are two important indicators. It is time-consuming and challenging to identify micro-calcification from mammogram images by the human eye because of its size and appearance. Several Computer-Aided Detection (CADe) have been developed to support radiologists because the automatic detection of micro-calcification is important for diagnosis and the next recommended treatment. Most of the current CADe systems at this time started using Convolutional Neural Network (CNN) to implement the micro-calcification detection in mammograms and their quantitative results are very satisfying, the average level of accuracy is more than 90%. However, most of the methods used are image fragments from a complete image which are then included in the program. This research conducts an automated approach to detect the location of any micro-calcification in the mammogram images with the complete image and in a simple way. At first, the image preprocessing algorithms were applied to enhance the image quality. After that, the micro-calcification region was labeled using image segmentation based on the Radiologist's expertise. The positive label which contains micro-calcification pixels was taken to train with segmentation network. A total of 354 images from INbreast dataset were used in this research study. Finally, the trained network was utilized to detect the micro-calcification area automatically from the mammogram images. This process can help as an assistant to the radiologist for early diagnosis and increase the detection accuracy of the micro-calcification regions. The proposed system performance is measured according to the error values of Mean Squared Logarithmic Error (MSLE) as the technique to find out the difference between the values predicted by the proposed model and the actual values. The best MSLE loss value obtained was achieved in 0.05 with accuracy 0.95.

A systematic method for the detection and segmentation of microcalcifications in mammograms is presented. It is important to preserve size and shape of the individual calcifications as exactly as possible. A reliable diagnosis requires both rates of false positives as well as false negatives to be extremely low. The proposed approach uses a two-stage algorithm for spot detection and shape extraction. The first stage applies a weighted difference of Gaussians filter for the noise-invariant and size-specific detection of spots. A morphological filter reproduces the shape of the spots. The results of both filters are combined with a conditional thickening operation. The topology and the number of the spots are determined with the first filter, and the shape by means of the second. The algorithm is tested with a series of real mammograms, using identical parameter values for all images. The results are compared with the judgement of radiological experts, and they are very encouraging. The described approach opens up the possibility of a reproducible segmentation of microcalcifications, which is a necessary precondition for an efficient screening program. >

The presence of clusters of microcalcifications in mammograms is particularly significant for early detection of breast cancer. In this paper a Computer Aided Detection system designed for this task is described. The detection of microcalcifications is performed by means of a segmentation based on a watershed transform and a further analysis based both on heuristic rules and AdaBoost classification. Finally a clustering algorithm is applied to detect those clusters of medical interest. The approach has been successfully tested on a Full Field Digital Mammographic database that has been developed through a strong cooperation between radiologists and computer scientists.

Mammography is one of the most efficient imaging techniques for the early detection and diagnosis of breast cancer in woman. The appearance of microcalcifications (MCs) in X-ray mammograms can be regarded as important early diagnostic feature of breast cancer. However, the detection of MCs in X-ray mammograms only depend on radiologists by visual inspection is a difficult task. In this paper, a novel algorithm for the automated detection of MCs in digital mammograms is described. The aim of approach is to locate suspicious MCs by analyzing the distribution of brightness over the digital mammograms. The algorithm for the detection of MCs is performed in two steps. First, the digital X-ray mammogram intensity surface is well-fitted by the least squares support vector machines (LS-SVM), and then the maximum extremum points are detected on the fitted intensity surface by convolving the image with the second order directional derivative operators deduced from the mapped LS-SVM with mixture of kernels. The proposed approach is developed and evaluated by using MIAS database. The experimental results demonstrate the possible MCs are detected accurately and efficiently.

In computer-aided detection of microcalcifications (MCs), the detection accuracy is often compromised by frequent occurrence of false positives (FPs), which can be attributed to a number of factors, including imaging noise, inhomogeneity in tissue background, linear structures, and artifacts in mammograms. In this study, the authors investigated a unified classification approach for combating the adverse effects of these heterogeneous factors for accurate MC detection. To accommodate FPs caused by different factors in a mammogram image, the authors developed a classification model to which the input features were adapted according to the image context at a detection location. For this purpose, the input features were defined in two groups, of which one group was derived from the image intensity pattern in a local neighborhood of a detection location, and the other group was used to characterize how a MC is different from its structural background. Owing to the distinctive effect of linear structures in the detector response, the authors introduced a dummy variable into the unified classifier model, which allowed the input features to be adapted according to the image context at a detection location (i.e., presence or absence of linear structures). To suppress the effect of inhomogeneity in tissue background, the input features were extracted from different domains aimed for enhancing MCs in a mammogram image. To demonstrate the flexibility of the proposed approach, the authors implemented the unified classifier model by two widely used machine learning algorithms, namely, a support vector machine (SVM) classifier and an Adaboost classifier. In the experiment, the proposed approach was tested for two representative MC detectors in the literature [difference-of-Gaussians (DoG) detector and SVM detector]. The detection performance was assessed using free-response receiver operating characteristic (FROC) analysis on a set of 141 screen-film mammogram (SFM) images (66 cases) and a set of 188 full-field digital mammogram (FFDM) images (95 cases). The FROC analysis results show that the proposed unified classification approach can significantly improve the detection accuracy of two MC detectors on both SFM and FFDM images. Despite the difference in performance between the two detectors, the unified classifiers can reduce their FP rate to a similar level in the output of the two detectors. In particular, with true-positive rate at 85%, the FP rate on SFM images for the DoG detector was reduced from 1.16 to 0.33 clusters/image (unified SVM) and 0.36 clusters/image (unified Adaboost), respectively; similarly, for the SVM detector, the FP rate was reduced from 0.45 clusters/image to 0.30 clusters/image (unified SVM) and 0.25 clusters/image (unified Adaboost), respectively. Similar FP reduction results were also achieved on FFDM images for the two MC detectors. The proposed unified classification approach can be effective for discriminating MCs from FPs caused by different factors (such as MC-like noise patterns and linear structures) in MC detection. The framework is general and can be applicable for further improving the detection accuracy of existing MC detectors.

Breast carcinoma is the main cause of deaths in women suffering from cancer and its early detection is vital in order to improve its prognosis [1]. Screen film mammography represents the method of choice and the only accepted screening modality. Clustered microcalcifications (mc) are one of the mammographic hallmarks of early breast cancer [2]. The detection and differentiation of cancer related mc’s from those occurring in benign processes are highly dependent on image quality as well as the skills and the vigilance of the reporting radiologist [3],[4],[5]. In clinical routine additional investigations (eccentric resp. magnified ray views, ultrasound, MRI) have to be performed in patients with inclusive findings on standard mammographic projections. Patients with findings suspicious have to undergo biopsy followed by histologic examination of the specimen.KeywordsChronic Renal FailureAtrial Natriuretic PeptideSodium ExcretionAtrial TissueSodium LoadingThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work aims to explore whether we can improve the accuracy of an SVM classifier for microcalcification (MC) detection by incorporating prior knowledge of MCs in mammograms. Based on the fact that MCs are inherently invariant to their spatial orientation in a mammogram, we consider two different techniques for incorporating rotation invariance into SVM, of which one is virtual support vector SVM (VSVM) and the other is tangent vector SVM (TV-SVM). The experiment results show that both techniques can improve the performance in discriminating MCs from the image background, and TV-SVM achieved the best performance. In particular, the sensitivity was 96.3% for TV-SVM, compared to 94.5% for SVM, when the false positive rate was at 0.5%.

This paper presents a computer-aided diagnosis (CAD) system for automatic detection of clustered microcalcifications (MCs) in digitized mammograms. The proposed system consists of two main steps. First, potential MC pixels in the mammograms are segmented out by using four mixed features consisting of two wavelet features and two gray level statistical features and then the potential MC pixels are labeled into potential individual MC objects by their spatial connectivity. Second, MCs are detected by extracting a set of 17 features from the potential individual MC objects. The classifier which is used in the first step is a multilayer feedforward neural network classifier but for the second step we have used Adaboost with SVM-based component classifier. Component classifiers which we used in our combining method are SVM classifiers with RBF kernel. The method was applied to a database of 40 mammograms (Nijmegen database) containing 105 clusters of MCs. A free-response operating characteristics (FROC) curve is used to evaluate the performance of CAD system. Results show that the proposed system gives quite satisfactory performance. In particular, 89.55% mean true positive detection rate is achieved at the cost of 0.921 false positive per image.

The aim of this study is to evaluate the performance of a wavelet-based image compression algorithm in mammography. As microcalcifications detection is selected as diagnostic task, the algorithm is evaluated with respect to its visually lossless threshold at low compression ratios and not its supra-threshold performance. The threshold is determined by means of observer performance using a set of digitized mammograms from the Digital Database for Screening Mammography and by means of an image quality study, related to transfer characteristics of the compression algorithm, using computer-generated test images. The image quality parameters used are input/output response, noise, high contrast response and low contrast-detail response. The computer-generated test images mimic mammographic image and microcalcification characteristics. Receiver operating characteristics analysis of pooled data for microcalcification detection in mammograms indicated a threshold at compression ratio 40:1. Image quality parameters assessment, with respect to low contrast-detail patterns, indicated a threshold at compression ratio 35:1. The two approaches provided comparable thresholds, indicating the potential use of image quality parameters.