Detection of mammographic microcalcifications using a refined region-growing approach

Microcalcification (MC) clusters in mammograms can be important early signs of breast cancer in women. Accurate detection of MC clusters is an important but challenging problem. In this paper, we propose the use of a recently developed machine learning technique -- relevance vector machine (RVM) -- for automatic detection of MCs in digitized mammograms. RVM is based on Bayesian estimation theory, and as a feature it can yield a decision function that depends on only a very small number of so-called <i>relevance vectors</i>. We formulate MC detection as a supervised-learning problem, and use RVM to classify if an MC object is present or not at each location in a mammogram image. MC clusters are then identified by grouping the detected MC objects. The proposed method is tested using a database of 141 clinical mammograms, and compared with a support vector machine (SVM) classifier which we developed previously. The detection performance is evaluated using the free-response receiver operating characteristic (FROC) curves. It is demonstrated that the RVM classifier matches closely with the SVM classifier in detection performance, and does so with a much sparser kernel representation than the SVM classifier. Consequently, the RVM classifier greatly reduces the computational complexity, making it more suitable for real-time processing of MC clusters in mammograms.

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.

Computer-aided diagnosis (CAD) systems in mammography have been developed and investigated for several years and recently they have left the research stage to enter the clinical stage. The purpose of this article is to review the present situation of mammography for breast cancer detection and the role played by CAD systems. Results from the recent literature show that CAD systems have the potential to improve the sensitivities of radiologists in the detection of malignant clustered microcalcifications and masses, while keeping specificities at acceptable levels. This leads to the conclusion that CAD systems can be incorporated into clinical practice as a double reading option to radiologists. However, some issues have yet to be tackled for CAD systems to gain better acceptance and more widespread use worldwide.

Breast cancer is one of the main causes of death in women and early diagnosis is an important means to reduce the mortality rate. The presence of microcalcification clusters are primary indicators of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This chapter presents a procedure for the classification of microcalcification clusters in mammograms using sequential difference of gaussian filters (DoG) and three evolutionary artificial neural networks (EANNs) compared against a feedforward artificial neural network (ANN) trained with backpropagation. It is shown that the use of genetic algorithms (GAs) for finding the optimal weight set for an ANN, finding an adequate initial weight set before starting a backpropagation training algorithm and designing its architecture and tuning its parameters, results mainly in improvements in over-all accuracy, sensitivity and specificity of an ANN, compared with other networks trained with simple backpropagation.

Breast cancer is the most common cancer diagnosed in women worldwide. Up to 50% of non-palpable breast cancers are detected solely through microcalcification clusters in mammograms. This article presents a novel and completely automated algorithm for the detection of microcalcification clusters in a mammogram. A multiscale 2D non-linear energy operator is proposed for enhancing the contrast between the microcalcifications and the background. Several texture, shape, intensity, and histogram of oriented gradients (HOG)-based features are used to distinguish microcalcifications from other brighter mammogram regions. A new majority class data reduction technique based on data distribution is proposed to counter data imbalance problem. The algorithm is able to achieve 100% sensitivity with 2.59, 1.78, and 0.68 average false positives per image on Digital Database for Screening Mammography (scanned film), INbreast (direct radiography) database, and PGIMER-IITKGP mammogram (direct radiography) database, respectively. Thus, it might be used as a second reader as well as a screening tool to reduce the burden on radiologists.

Microcalcification clusters in mammograms are one of the major signs of breast cancer. However, the detection of microcalcifications from mammograms is a challenging task for radiologists due to their tiny size and scattered location inside a denser breast composition. Automatic CAD systems need to predict breast cancer at the early stages to support clinical work. The intercluster gap, noise between individual MCs, and individual object’s location can affect the classification performance, which may reduce the true-positive rate. In this study, we propose a computer-vision-based FC-DSCNN CAD system for the detection of microcalcification clusters from mammograms and classification into malignant and benign classes. The computer vision method automatically controls the noise and background color contrast and directly detects the MC object from mammograms, which increases the classification performance of the neural network. The breast cancer classification framework has four steps: image preprocessing and augmentation, RGB to grayscale channel transformation, microcalcification region segmentation, and MC ROI classification using FC-DSCNN to predict malignant and benign cases. The proposed method was evaluated on 3568 DDSM and 2885 PINUM mammogram images with automatic feature extraction, obtaining a score of 0.97 with a 2.35 and 0.99 true-positive ratio with 2.45 false positives per image, respectively. Experimental results demonstrated that the performance of the proposed method remains higher than the traditional and previous approaches.

At present, mammography is the only not invasive diagnostic technique allowing the diagnosis of a breast cancer at a very early stage. A visual clue of such disease particularly significant is the presence of clusters of microcalcifications. Reliable methods for an automatic detection of such clusters are very difficult to accomplish because of the small size of the microcalcifications and of the poor quality of the digital mammograms. A method designed for this task is described. The mammograms are firstly segmented by means of the tree structured Markov random field algorithm which extracts the elementary homogeneous regions of interest on the image. Such regions are then submitted to a further analysis (based both on heuristic rules and support vector classification) in order to reduce the false positives. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.

Database characteristics can affect significantly the performance of a mammography CAD scheme. Hence adequate performance comparison among different CAD schemes is not suitable since a single scheme could present different results depending on the set of chosen cases. Images in database shou ld follow a set of quality criteria, since the imaging process up to digital file. CAD schemes can not be developed without a database used to test their efficacy, but each database with particular characteristics may in fluence on the processing scheme performance. A possible solution could be using information on the imaging equipment characteristics. This work describes a preprocessing in order to compensateŽ the image degradation during the acquisition steps, assuring a better uniformityŽ relative to the images quality. Thus, poor quality images would be restored, providing therefore some independen ce on the images source to CAD schemes and allowing to reach the better possible performance. Tests performed with mammography images sets reported a 14% increase in sensitivity for microcalcifications detection. Although this result was followed by a little increase in false positive rates, simple changes in techniques pa rameters can provide the same improvement but with a reduction of the false positive detections. Keywords: Computer-aided diagnosis in Mammography, mi crocalcifications detection, mammography images database

Breast cancer is a leading cause of deaths in women. Mammography is considered as the most effective technology presently available for breast cancer screening, being very effective in the detection of clustered microcalcifications which are considered as one of the most important findings associated to the existence of breast cancer. A computer aided diagnosis (CAD) system named "Hippocrates-mst" has been already developed in the lab based on detailed analysis and evaluation of related features of microcalcifications (individually and in clusters). Preliminary evaluation results have shown that the system achieves high levels of sensitivity, while suffering from low specificity. For this reason, our current studies aim to a methodology refinement which will lead to optimized classification results. In this paper, we focus on obscure diagnostic cases classified by the radiologists as BI-RADS 3. In such cases, although short-term re-examination is normally advised, radiologists and physicians usually have strong doubts about their recommendations. We tested the performance of two classifiers embedded in the proposed CAD system using a dataset of 63 (57 benign and 6 malignant) mammograms, all classified as BI-RADS 3 and biopsy proven. The sensitivity achieved by the first one (the default Hippocrates-mst classifier) is as high as 100%, classifying correctly all the malignant cases. As far as the benign cases are concerned, system's specificity is 35.09%. Using the second classifier (a rule based and SVM hybrid classifier) the specificity increases to 63.16% with a cost of sensitivity decrease to 66.67%. ©2008 IEEE.

To investigate the value of computer-aided diagnosis (CAD) system in assessing thyroid nodules concurrent with Hashimoto's thyroiditis (HT). Totally 148 patients with 193 thyroid nodules were enrolled. A radiologist assessed the nodules using a thyroid ultrasound CAD system. Additionally, the nodules were evaluated by one experienced radiologist alone, and one training radiologist without and with CAD assistance. The diagnostic performance was compared between the CAD system and the experienced radiologist, and the training radiologist without and with CAD assistance. The CAD system demonstrated a similar sensitivity to that of the experienced radiologist in diagnosing thyroid cancers (89.8% versus 92.4%, P > .05). The specificity and accuracy of the CAD system were lower than that of the experienced radiologist in assessing the nodules with diffusedly altered glands (specificity, 60.0% versus 81.7%, P=.007; accuracy, 77.5% versus 88.1%, P=.011). With CAD assistance, the training radiologist had improved sensitivity and accuracy that increased to 87.9% and 86.8% in classifying nodules with sonographically evident HT (both P=.012). The CAD system has comparable sensitivity, but lower specificity compared with the experienced radiologist in diagnosing thyroid malignancies concurrent with HT. For a radiologist with less experience, the CAD system can help improve the diagnostic performance by increasing sensitivity and accuracy in assessing thyroid nodules with diffusely altered parenchyma.

Computer-Aided Diagnosis (CAD) systems provide a second opinion to health professionals about the possible existence of an anomaly. Evaluation of CAD systems is a challenge and most of the traditional metrics requires the constant participation of experts. This paper presents an approach for evaluating CAD systems using concepts of Content-Based Image Retrieval and graphical oracles. After implementing feature descriptors and selecting three similarity functions, two metrics are proposed to measure the efficiency of CAD systems. A case study was conducted considering three simulated CAD systems to detect masses and calcifications in mammographic images. The results indicated that the our approach is as robust as traditional metrics with respect to performance evaluation. However, our approach is more flexible than traditional metrics because evaluators can choose the more adequate features to assess a particular CAD system.

In this chapter we discuss rationale and methods for computer-aided diagnosis in mammography. Computer-aided diagnosis (CAD) is a diagnosis made by a clinician who uses the output from a computerized analysis of medical images as a €œsecond opinion in detecting lesions and in making diagnostic decisions. The final diagnosis is rendered by the clinician, e.g., the radiologist. Computer vision and artificial intelligence techniques are developed and customized to accommodate lesions such as cancer and their radiographic presentations on the normal parenchymal background of the breast. Mammography, x-ray imaging of the breast, is currently the best method for the early detection of breast cancer. Between 10 and 30% of women who have breast cancer and undergo mammography have negative mammograms, however. In approximately two-thirds of these false-negative mammograms, the radiologist failed to detect a cancer that was evident retrospectively. The missed detections may be due to the subtle nature of the radiographic findings (i.e., low conspicuity of the lesion), poor image quality, eye fatigue, or oversight by the radiologists. It has been suggested that double reading (by two radiologists) may increase sensitivity. Thus, one aim of CAD is to increase the efficiency and effectiveness of screening procedures by using a computer system, as a second reader (like a €œspell checker€), to indicate locations of suspicious abnormalities in mammograms as an aid to the radiologist leaving the final decision regarding the likelihood of the presence of a cancer and patient management to the radiologist. The interpretation of screening mammograms lends itself to CAD since it is a repetitive task involving mostly normal images. Figure 15.1 shows a schematic diagram of a computerized detection method for use in screening mammography. If a suspicious region is detected, the radiologist then decides if the abnormality is likely to be malignant or benign, and what course of action should be recommended (i.e., return to screening, return for short term follow-up, or return for biopsy). Many patients are referred for surgical biopsy on the basis of a radiographically detected mass lesion or cluster of microcalcifications. Although general rules for the differentiation between benign and malignant breast lesions exist, considerable variability occurs in the interpretation of findings by radiologists with current radiographic techniques. On average, only 10-€“20% of masses referred for surgical breast biopsy are actually malignant. Thus, another aim of CAD is to extract and analyze the characteristics of benign and malignant lesions seen at mammography in an objective manner in order to aid the radiologist. This has the potential for increasing diagnostic accuracy and reducing the numbers of false-positive diagnoses of malignancies, thereby decreasing patient morbidity by reducing the number of surgical biopsies performed and their associated complications. Figure 15.2 shows a schematic diagram of a computerized diagnosis method for use in the mammographie workup of a suspect lesion.

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.

Segmentation or abnormality detection is an essential step in mammographic computer-aided diagnosis (CAD) systems. This paper presents a novel computerized method to automatically detect mass lesions (i.e. detect suspicious locations containing abnormalities inside the breast area) on digitized mammogram images. In particular, we implement an enhanced version of the region growing algorithm for segmentation of mass lesions that can be implemented in a complete CAD system. The proposed algorithm uses region growing technique with a novel automatic threshold estimation method to detect and segment mass lesions. The proposed algorithm detects masses by analyzing a single view of the breast (i. e. Medio-Lateral oblique (MLO) view or Cranio-Caudal (CC) view). The performance of the proposed algorithm was evaluated using two mammogram databases from two different hospitals. The matching percentage of the segmented regions obtained by the proposed algorithm is 83% with respect to the ground truth (i.e. reference determined by an expert radiologist). The proposed algorithm showed promising performance when compared with other commonly used segmentation techniques.

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.