Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network.

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.

This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

The presence of microcalcification clusters in digital mammograms is a primary indicator of early stages of malignant types of breast cancer and its detection is important to prevent the disease. This paper uses a procedure for the classification of microcalcification clusters in mammograms using sequential Difference of Gaussian filters (DoG) and feedforward Neural Networks (NN). Three methods using class separability, forward sequential search and genetic algorithms for feature selection are compared. We found that the use of Genetic Algorithms (GAs) for selecting the features from microcalcifications and microcalcification clusters that will be the inputs of a feedforward Neural Network (NN) results mainly in improvements in overall accuracy, sensitivity and specificity of the classification.

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.

In this paper, we propose a novel combinational SVM algorithm via a set of decision rules to achieve better performances in microcalcification detection inside digital mammograms towards computer aided breast cancer diagnosis. Based on the discovery that the polynomial SVM is sensitive to MC (microcalcification) pixels and the linear SVM is sensitive to non-MC pixels, we designed an adaptive threshold mechanism via establishment of their correspondences to exploit the complementary nature between the polynomial SVM and the linear SVM. Experiments show that the proposed algorithm successfully reduced false positive detection rate while keeping the true positive detection rate competitive.

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.

GravityNet for end-to-end small lesion detection

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.

We aimed to evaluate the influence of image reduction using a bi-cubic interpolation method on the accuracy of detection of clustered microcalcifications (MCLs) and masses on digital mammograms. Digital mammograms (n=194) of 97 subjects were selected retrospectively, comprising 47 patients with clustered MCLs or masses and 52 controls. Images were acquired in the craniocaudal view by phase-contrast mammography (PCM). Original PCM images comprised 25-&mu;m pixels. The reduced images converted from the originals by bi-cubic interpolation were of 50-&mu;m pixel size. Five observers independently interpreted all images, and rated their confidence concerning the presence of lesions on a continuous 0-100 scale. Receiver-operating characteristic (ROC) analysis was performed using the jackknife method and LABMRMC program. Differences in areas under the curve (AUC) values based on 95% confidence intervals were evaluated. The average AUC values for detection of masses were 0.8435 and 0.8646 for the original and reduced images, respectively. The difference between the average AUC values was not statistically significant (<i>p</i>=0.5855). Average AUC values for clustered MCLs detection were 0.9273 and 0.9574 for the original and reduced images, respectively. This difference was not statistically significant (<i>p</i>=0.1949). Detection of masses and clustered MCLs on digital mammograms was unaffected by bi-cubic interpolation image reduction.

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 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 the only technique currently used for detecting microcalcification (MC) clusters, an early indicator of breast cancer. However, mammographic images superimpose a three-dimensional compressed breast image onto two-dimensional projection views, resulting in overlapped anatomical breast structures that may obscure the detection and visualization of MCs. One possible solution to this problem is the use of cone beam computed tomography (CBCT) with a flat-panel (FP) digital detector. Although feasibility studies of CBCT techniques for breast imaging have yielded promising results, they have not shown how radiation dose and x-ray tube voltage affect the accuracy with which MCs are detected by CBCT experimentally. We therefore conducted a phantom study using a FP-based CBCT system with various mean glandular doses and kVp values. An experimental CBCT scanner was constructed with a data acquisition rate of 7.5 frames/s. 10.5 and 14.5 cm diameter breast phantoms made of gelatin were used to simulate uncompressed breasts consisting of 100% glandular tissue. Eight different MC sizes of calcium carbonate grains, ranging from 180-200 microm to 355-425 microm, were used to simulate MCs. MCs of the same size were arranged to form a 5 x 5 MC cluster and embedded in the breast phantoms. These MC clusters were positioned at 2.8 cm away from the center of the breast phantoms. The phantoms were imaged at 60, 80, and 100 kVp. With a single scan (360 degrees), 300 projection images were acquired with 0.5 x, 1x, and 2x mean glandular dose limit for 10.5 cm phantom and with 1x, 2x, and 4x for 14.5 cm phantom. A Feldkamp algorithm with a pure ramp filter was used for image reconstruction. The normalized noise level was calculated for each x-ray tube voltage and dose level. The image quality of the CBCT images was evaluated by counting the number of visible MCs for each MC cluster for various conditions. The average percentage of the visible MCs was computed and plotted as a function of the MGD, the kVp, and the average MC size. The results showed that the MC visibility increased with the MGD significantly but decreased with the breast size. The results also showed that the x-ray tube voltage affects the detection of MCs under different circumstances. With a 50% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 348(+/-2), 288(+/-7), 257(+/-2) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 355 (+/-1), 307 (+/-7), 275 (+/-5) microm at 6, 12, and 24 mGy, respectively. With a 75% threshold, the minimum detectable MC sizes for the 10.5 cm phantom were 367 (+/-1), 316 (+/-7), 265 (+/-3) microm at 3, 6, and 12 mGy, respectively. Those for the 14.5 cm phantom were 377 (+/-3), 334 (+/-5), 300 (+/-2) microm at 6, 12, and 24 mGy, respectively.

Using for-presentation and for-processing digital mammograms, the presence of microcalcifications has been shown to be associated with short-term risk of breast cancer. In a previous article we developed an algorithm for microcalcification cluster detection from for-presentation digital mammograms. Here, we focus on digitised mammograms and use a three-step algorithm. In total, 253 incident invasive breast cancer cases (with a negative mammogram between three months and two years before diagnosis, from which we measured microcalcifications) and 728 controls (also with prior mammograms) were included in a short-term risk study. After adjusting for potential confounding variables, we found evidence of an association between the number of microcalcification clusters and short-term (within 3–24 months) invasive breast cancer risk (per cluster OR = 1.30, 95% CI = (1.11, 1.53)). Using the 728 postmenopausal healthy controls, we also examined association of microcalcification clusters with reproductive factors and other established breast cancer risk factors. Age was positively associated with the presence of microcalcification clusters (p = 4 × 10−04). Of ten other risk factors that we studied, life time breastfeeding duration had the strongest evidence of association with the presence of microcalcifications (positively associated, unadjusted p = 0.001). Developing algorithms, such as ours, which can be applied on both digitised and digital mammograms (in particular for presentation images), is important because large epidemiological studies, for deriving markers of (clinical) risk prediction of breast cancer and prognosis, can be based on images from these different formats.

Early detection of breast cancer, a significant public health problem in the world, is the key for improving breast cancer early prognosis. Mammography is considered the most reliable and widely used diagnostic technique for early detection of breast cancer. However, it is difficult for radiologists to perform both accurate and uniform evaluation for the enormous mammograms with widespread screening. Microcalcification clusters is one of the most important clue of the breast cancer, and their automated detection is very helpful for early breast cancer diagnosis. Because of the poor quality of the mammographic images and the small size of the microcalcifications, it is a very difficult task to perform detecting the early breast cancer. In this paper, we propose a novel approach based on hybrid subspace fusion for detection microcalcification clusters, and successfully apply it to detection task in digital mammograms. In such a system, subspace learning algorithms will be selectively fused according to the ability of preserving the classification information. Experimental results show that the proposed method improved the performance and stability of microcalcification cluster detection and could be adapt to the noise environments better. The proposed methods could get satisfactory results on sensitivity and reduce false positive rate, which provide some new ideas and methods for the research and development of computer-aided detection system in the breast cancer detection community.

Computer aided detection system for micro calcifications in digital mammograms