Segmentation of Microcalcifications in Mammograms

One of the signs of early breast cancer is the presence of microcalcification clusters in mammograms. Automated diagnostic aids for detection of breast cancer in mammograms must determine the presence of microcalcification clusters with high enough accuracy to serve as a clinical tool. To do so, the automated system has to process the digitized mammogram with at least three different types of algorithms. The first is segmentation of individual small candidate structures that are separated from the background tissue. The second is extraction of quantitative features from each candidate structure, to represent size, shape, texture, contrast, etc. The third is classification of candidate structures as microcalcification or other tissue, using the feature values, based on automated training against ground truth provided by clinical experts. An optional fourth type of algorithm, enhancement, may also be used as a preprocessing step, depending on the image quality, to reduce the noise level of the image or to increase the contrast of candidate objects before segmentation is applied. In this multistage process, the role of segmentation is essential because the delineation of each candidate object dictates the extracted features, and subsequently their classification. This chapter focuses on segmentation algorithms that are especially suited for segmenting small, low-contrast, arbitrarily shaped objects such as microcalcifications in mammograms. Segmentation algorithms used to separate objects of interest from the image background are primarily based on three different approaches. Some algorithms determine the pixels of the object by comparing their brightness values to either a global or local threshold. The second type of algorithm first determines the edge pixels of the object using a gradient operator, then an edge-linking technique forms a closed contour and labels all pixels within the edge contour. The third approach is to grow a region of contiguous pixels starting from a seed pixel selected inside the object. The segmentation of microcalcifications in mammograms is especially demanding because each microcalcification is a very small, low-contrast object embedded in a nonuniform background that may have significant spatial high-frequency components. An edge detector applied to a mammogram finds edges throughout the image and may also overlook the edges of some microcalcifications due to their low contrast. Segmentation of small, low-contrast objects such as microcalcifications cannot effectively be based on conventional edge detection, especially because linking edge pixels obtained on a mammogram is not practical. Most algorithms reported for segmentation of microcalcifications are based either on thresholding or region growing, with or without enhancement. The segmentation algorithm suggested in Refs. 1 and 2 compares each pixel to a local threshold computed within a square neighborhood around the pixel. The size of the neighborhood is selected by the user, and the threshold is set as the mean value of pixels in the neighborhood plus their rms value multiplied by a selected coefficient. This threshold represents the highest value that the background pixels in that neighborhood are expected to have. Pixels that have values greater than the threshold are connected to form the segmented region.

A novel technique to detect the microcalcifications in digital mammograms presented in this paper uses the statistical measures, namely mean and variance, as the criterion to classify the pixels representing microcalcifications. This method has proved its credentials by accurately segmenting the microcalcifications in the mammogram image. This approach fixes the boundary of the microcalcifications accurately, which confirms, its qualitative performance.

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.

Microcalcification (MC) clusters in mammograms are one of the primary signs of breast cancer. In the literature, most MC detection methods follow a two-step paradigm: segmenting each MC and analyzing their spatial distributions to form MC clusters. However, segmentation of MCs cannot avoid low sensitivity or high false positive rate due to their variability in size (sometimes <0.1 mm), brightness, and shape (with diverse surroundings). In this paper, we propose a novel self-adversarial learning framework to differentiate and delineate the MC clusters in an end-to-end manner. The class activation mapping (CAM) mechanism is employed to directly generate the contours of MC clusters with the guidance of MC cluster classification and box annotations. We also propose the self-adversarial learning strategy to equip CAM with better detection capability of MC clusters by using the backbone network itself as a discriminator. Experimental results suggest that our method can achieve better performance for MC cluster detection with the contouring of MC clusters and classification of MC types.

The authors are developing a computer-aided diagnostic method to assist radiologists in differentiating between malignant and benign clustered microcalcifications in mammograms. In earlier studies we investigated shape and contrast features of microcalcifications for classification. It was found that segmentation strongly influences classification results. For this reason a phantom study has been carried out. The CDMAM phantom, consisting of a pattern of dots with known size and object contrast is used for evaluation of contrast measurement and segmentation. Dots in the range of 0.2-0.8 mm are taken as a model for microcalcifications. In this article performances of different methods for segmentation of microcalcifications are compared. An iterative method based on a Markov random field and a signal dependent criterion give satisfying results. The segmentation performances of both methods are comparable. Also the influence of the modulation transfer function on contrast estimates is determined and effect of exposure level on segmentation is analyzed.

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.

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.

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%.

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.

Clustered microcalcifications (MCCs) on mammograms are an important early sign of breast cancer. An intelligent computer-aided diagnosis system can be very helpful for radiologist in detecting and diagnosing MCCs earlier than typical screening programs. In this paper, the detection algorithm is able to extract high-frequency signal and remove low-frequency background by exploiting a difference-image technique in which a signal-suppressed image is subtracted from a signal-enhanced image to remove the structured background in a mammogram. The difference image is thresholded to detect these MCCs in mammograms. The algorithm is tested with a series of clinical mammograms. A true positive rate ofmore than 75.5% is obtained at a false-positive (FP) detection of 2.18 per image

Microcalcification cluster detection using multiscale products based Hessian matrix via the Tsallis thresholding scheme

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.

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.

An Evaluation of Conventional Culture, invA PCR, and the Real-Time PCR iQ-Check Kit as Detection Tools for Salmonella in Naturally Contaminated Premarket and Retail Turkey

Breast cancer is one of the most commonly occurring cancers among women globally. The accurate detection and classification of the abnormalities such as masses and microcalcifications in mammograms is a challenging task for the radiologist without which the survival rate of the breast cancer patients may increase worldwide. This paper presents a novel Computer Aided Diagnosis (CAD) system which uses Cellular Neural Network (CNN) technique, which is optimized using Particle Swarm Optimization (PSO) for detection and Particle Swarm Optimised Probabilistic Neural Network (PSOPNN) for the classification of breast masses as benign or malignant. The breast mass texture feature extraction is carried out using Gray Level Co-occurrence Matrix (GLCM) and the optimal texture features are selected using a particle swarm optimized feature selection. The performance of the proposed system can be evaluated using the True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN) values.