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Background Microcalcification clusters in mammograms can be considered as early signs of breast cancer. However, their detection is a very challenging task because of different factors: large variety of breast composition, highly textured breast anatomy, impalpable size of microcalcifications in some cases, as well as inherent low contrast of mammograms. Thus, the need to support the clinicians’ work with an automatic tool. Methods In this work a three-phases approach for clustered microcalcification detection is presented. Specifically, it is made up of a pre-processing step, aimed at highlighting potentially interesting breast structures, followed by a single microcalcification detection step, based on Hough transform, that is able to grasp the innate characteristic shape of the structures of interest. Finally, a cluster identification step to group microcalcifications is carried out by means of a clustering algorithm able to codify expert domain rules. Results The detection performance of the proposed method has been evaluated on 364 mammograms of 182 patients obtaining a true positive ratio of 91.78 % with 2.87 false positives per image. Conclusions Experimental results demonstrated that the proposed method is able to detect microcalcification clusters in digital mammograms showing performance comparable to different methodologies exploited in the state-of-art approaches, with the advantage that it does not require any training phase and a large set of data. The performance of the proposed approach remains high even for more difficult clinical cases of mammograms of young women having high-density breast tissue thus resulting in a reduced contrast between microcalcifications and surrounding dense tissues.

Breast cancer is the main cause of female malignancy worldwide. Effective early detection by imaging studies remains critical to decrease mortality rates, particularly in women at high risk for developing breast cancer. Breast Magnetic Resonance Imaging (MRI) is a common diagnostic tool in the management of breast diseases, especially for high-risk women. However, during this examination, both normal and abnormal breast tissues enhance after contrast material administration. Specifically, the normal breast tissue enhancement is known as background parenchymal enhancement: it may represent breast activity and depends on several factors, varying in degree and distribution in different patients as well as in the same patient over time. While a light degree of normal breast tissue enhancement generally causes no interpretative difficulties, a higher degree may cause difficulty to detect and classify breast lesions at Magnetic Resonance Imaging even for experienced radiologists. In this work, we intend to investigate the exploitation of some statistical measurements to automatically characterize the enhancement trend of the whole breast area in both normal and abnormal tissues independently from the presence of a background parenchymal enhancement thus to provide a diagnostic support tool for radiologists in the MRI analysis.

Purpose Mammography is the most effective and low-cost method for the early detection of breast cancers. The aim of this study was to develop an automated model in order to improve detection and characterization of clustered microcalcifications on full-field digital mammograms (FFDM). Methods We analyzed 98 digital mammograms extracted from the public database Breast Cancer Digital Repository with 276 microcalcification clusters; these images were previously evaluated in double blind by two radiologists which had identified the microcalcification clusters. We identified some regions of interest (ROI) on mammograms after pre-processing by using the Circular Hough Transform algorithm in order to recognize circles corresponding to the microcalcifications. Then, we selected morphological and morphometric features for each ROI, such as Speeded Up Robust Feature, as well as classic statistical features based on Haar Transform. These features were used for giving instructions to a Random Forest classifier in order to recognize – riga clustered microcalcifications normal and abnormal ROIs. Finally, we tested the performance of – riga this automated model in cross-validation and with statistical measurements. Results Statistical results ( Table 1 ) showed good performance of proposed model in automatic detection of breast clustered microcalcifications on FFDM (sensitivity over 90% with 3.54 false positives – riga per image), even in young patients with dense breast tissue (sensitivity over 88%) and according to published data [1] . Also classification performance of clustered microcalcifications (AUC 98,6 ± 0,1, Accuracy 97,6 ± 0,2%, Sensitivity 97,7 ± 0,4%, Specificity 96,2 ± 0,4%) was comparable with the state of the art [2] . Conclusions The proposed automated model enables to improve detection of clustered microcalcifications that could be characterized by difficult diagnostic interpretation especially when occurred in dense breast parenchyma. The proposed model results highly performing and comparable to the state-of-art approaches.

Purpose Dual-energy contrast-enhanced spectral mammography (CESM) has emerged as a potential new option for breast cancer staging. This imaging technique allows acquisition of multiple views of both breasts after a single injection of iodinated Contrast Medium (CM). It based on the fact that the CM injected will have different uptake path between hypervascularized breast lesions and background parenchyma. The aim of this paper is to evaluate the performance of identifying and characterizing the breast lesions, costs and comfort of patients of a supervision CESM single-step path with respect to a traditional double-step path mammography and MRI. Methods We collected data on 46 patients who performed CESM: we have selected a subsample that also performed mammography in tomosynthesis and MRI. A total of 33 lesions have been identified by histological examination (28 malignant and 5 benign). The findings of all the techniques have been submitted to double blind check by two radiologists. The test was performed on 2D synthetic images of tomosynthesis and confirmed in low-energy images of CESM as reported in literature [1] . We have performed a statistical analysis of the diagnostic performances of each method in order to estimate the efficiency of the proposed diagnostic path with the only CESM technique with respect to the traditional path. Results This study have shown consistent results in terms of sensitivity (over 90%) and between paths. The total costs of CESM alone were lower than the combination of digital mammography and MRI with CM, and between the different methods, while the greater discomfort was reported by patients undergoing MRI. Conclusions The comparison between the methods has underlined the validity of the CESM supervision model both as morphological examination with respect to traditional mammography, and as dynamic examination with respect to MRI, according to the literature. In addition, CESM is more tolerated by patients than MRI and less expensive than the traditional paths.

Purpose Contrast-Enhanced Spectral Mammography (CESM) allows the acquisition of low- and high-energy digital mammograms with Dual-Energy technique after a single injection of iodinated contrast medium (CM) obtaining the production of recombined mammographic images [1] , [2] . The aim of this paper is to characterize ROI and measure the gray-level parenchymal background in order to improve the lesion detection in CESM. Methods We have selected 42 patients positive to the methodic for the presence of at least one findings after histological examination.We have evaluated 65 lesions (52 malignant and 13 benign) from 0.5 to 13.5 cm. Primary and secondary (if present) lesions have been identified and their diameters have been measured in axial projection. Regions including the parenchymal background and the non-contrasted background were also marked in both low-energy and recombined images.For each region the statistical parameters of gray levels were recorded by CESM instrumentation and have been elaborated by multivariate statistics and machine learning techniques in order to characterize each region and define a background intensity indicator, not yet defined objectively. Results The analysis on our available dataset seems to underline significant differences between ROIs with lesions and regions marked on the parenchymal background with respect to the coefficient of variation, i.e. standard deviation of the gray levels of the marked area compared to the mean value of the same) in recombined images.We have found an increase of the coefficient of ROIs greater than 20% with respect to the coefficient of the regions marked on parenchymal background in 57 lesions. Conclusions As preliminary results, an indicator to detect the parenchymal background can be identified as the coefficient of variation. Data collection is currently ongoing because a more massive data sample will be useful to increase the sensitivity in the lesion detection by CESM device, even in patients with a medium-high value of background.

Introduction An important area in which an improvement of the imaging techniques would be extremely important, is the diagnosis of breast cancer. For this purpose, mammography is the principal diagnostic tool used. Although it is effective in the early detection of breast cancer, exists a real need for new automatic approaches that can improve the accuracy of detection of breast cancer in mammogram Images. In fact, a computerized system as a second reader can support the radiologist in the interpretation of these exams by reducing the number of false positives and thus, the biopsy procedures not necessary. Purpose In this paper, we propose a Computer Aided Detection System (CAD) for the microcalcification in mammogram images as a diagnostic support tool for radiologists in the analysis. Materials and methods We develop a fully automated tool for (1) pre-processing images using the edge detection process described by Canny which was designed to be an optimal edge detector according to particular criteria; (2) region of Interest extraction; (3) Adapted Hough Transform to identify the microcalcification cluster. The proposed method was evaluated using cases from publicly available mammography dataset such as Breast Cancer Digital Repository (BCDR) database. Results We present the results obtained in terms of accuracy, sensitivity, false positive for image. The proposed system shows results comparable state of the art. Conclusion The proposed method was advantageous in the identification of microcalcifications.