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Automatic detection of the nipple in mammograms is an important step in computerized systems that combine multiview information for accurate detection and diagnosis of breast cancer. Locating the nipple is a difficult task owing to variations in image quality, presence of noise, and distortion and displacement of the breast tissue due to compression. In this work, we propose a novel Hessian-based method to locate automatically the nipple in screen-film and full-field digital mammograms (FFDMs). The method includes detection of a plausible nipple/retroareolar area in a mammogram using geometrical constraints, analysis of the gradient vector field by mean and Gaussian curvature measurements, and local shape-based conditions. The proposed procedure was tested on 566 mammographic images consisting of 372 randomly selected scanned films from two public databases (mini-MIAS and DDSM), and 194 digital mammograms acquired with a GE Senographe 2000D FFDM system. A radiologist independently marked the centers of the nipples for evaluation of the results. The average error obtained was 6.7 mm (22 pixels) with reference to the center of the nipple as identified by the radiologist. Only two out of the 566 detected nipples (0.35 %) had an error larger than 50 mm. The method was also directly compared with two other techniques for the detection of the nipple. The results indicate that the proposed method outperforms other algorithms presented in the literature and can be used to identify accurately the nipple on various types of mammographic images.

The interpretation of diagnostic images is often conditioned by the specific properties of the instrument that generated the image. This makes particularly complicated to develop universal recognition algorithms that can facilitate the diagnosis in case of massive population screenings. Mammography is a typical example where such an algorithm is required. Although the technological advances in medical imaging increases the accuracy of interpretation of images, the improved resolution may not facilitate the identification of breast cancer at a very early stage due to the many confounding factors related, for instance, to differences in instrument settings or breast positioning by the operator. Being impossible an exact standardization, the problem of reducing the effects of different instruments and operators can be faced with a proper algorithm that can extract from each image the relevant information in an unsupervised manner, thus limiting the influence of instrumental and positioning issues. For this scope, in this paper we investigated the properties of a classifier based on an ensemble of Adaptive Artificial Immune Networks (A2INET) applied to original mammography image indicators aimed at diagnosing bilateral asymmetry that is known to be correlated with increased breast cancer risk. Classification models were trained using a set of descriptors measuring the degree of similarity of paired regions of the left and right breasts. Noteworthy, the ensemble of A2INET models achieved very high classification rates even when training and testing were made on two completely independent and heterogeneous datasets. The obtained results are promising (maximum accuracy level of 0.90, sensitivity of 0.93 and specificity of 0.87) and they prefigure to apply automatic diagnostic tools in clinical practice exploiting a network of different instrument databases.

At present, mammography is the most effective examination for an early diagnosis of breast cancer. Nevertheless, the detection of cancer signs in mammograms is a difficult procedure owing to the great number of non-pathological structures which are also present in the image. Recent statistics show that in current breast cancer screenings 10%-25% of the tumors are missed by the radiologists. For this reason, a lot of research is currently being done to develop systems for Computer Aided Detection (CADe). Probably, some causes of the false-negative screening examinations are that tumoral masses have varying dimension and irregular shape, their borders are often ill-defined and their contrast is very low, thus making difficult the discrimination from parenchymal structures. Therefore, in a CADe system a preliminary segmentation procedure has to be implemented in order to separate the mass from the background tissue. In this way, various characteristics of the segmented mass can be evaluated and used in a classification step to discriminate benign and malignant cases. In this paper, we describe an effective algorithm for massive lesions segmentation based on a region-growing technique and we provide full details the performance evaluation procedure used in this specific context.

In this paper, we perform the assessment of a CAD for the tumoral masses classification in mammograms by the uncertainty propagation through the system. Carrying on the work of the authors concerning the metrological characterization of the developed CAD, we validate the features extraction, features selection, and classification steps in this paper. In particular, suitable metrics such as the Receiving Operating Curve (ROC) and the Area Under ROC (AUC) are widely used in order to provide a quantitative evaluation of the performance. Finally, we implement a Monte Carlo simulation in order to provide the confidence interval for some coverage probabilities for all involved parameters. The procedure is tested on mammographic images containing both malignant and benign breast masses.

Breast cancer is the main cause of cancer deaths among women in the world. About one million new cases appear every year, and about 25% of them lead to the death of the patient. The best solution is the early detection of suspicious tumoral signs through an effective mammographic screening program. Unfortunately, this kind of images is very difficult to interpret by the radiologists because of its very low contrast, so proper image-processing procedures could help them to achieve better diagnoses. This paper improves and assesses an algorithm, already proposed by the authors, that suggests to doctors the suspicious regions that could contain tumoral masses. The procedure succeeds also in the case of very low contrast because it depends only on the orientation of the gradient vectors in the image but not on their amplitude.

Mammography is the most effective method for the early detection of breast diseases. However, the typical diagnostic signs such as microcalcifications and masses are difficult to detect because mammograms are low-contrast and noisy images. In this paper, a novel algorithm for image denoising and enhancement based on dyadic wavelet processing is proposed. The denoising phase is based on a local iterative noise variance estimation. Moreover, in the case of microcalcifications, we propose an adaptive tuning of enhancement degree at different wavelet scales, whereas in the case of mass detection, we developed a new segmentation method combining dyadic wavelet information with mathematical morphology. The innovative approach consists of using the same algorithmic core for processing images to detect both microcalcifications and masses. The proposed algorithm has been tested on a large number of clinical images, comparing the results with those obtained by several other algorithms proposed in the literature through both analytical indexes and the opinions of radiologists. Through preliminary tests, the method seems to meaningfully improve the diagnosis in the early breast cancer detection with respect to other approaches.

We present a novel method to detect asymmetry in mammograms based upon bilateral analysis of the spatial distribution of density within paired mammographic strips. Various differential measures of spatial correlation of gray-scale values were computed with reference to the position of the nipple for a set of 128 pairs of mammograms from the Digital Database for Screening Mammography (DDSM). Features were selected by stepwise logistic regression and the leave-one-patient-out method was used for cross-validation of results. An area under the receiver operating characteristic curve of 0.87 (SE = 0.08) was achieved by using an artificial neural network classifier with radial basis functions.

We present new feature descriptors specifically designed to quantify angular nonstationarity and angular dependence of pixel values in sectors of mammographic lesions. A key novelty of this work is that the proposed measures characterize the texture of masses without relying on accurate determination of their contours. An artificial neural network based on radial basis functions was used to predict the diagnosis of 120 benign masses and 26 malignant tumors in a database of full-field digital mammograms. Features were selected using stepwise logistic regression and the leave-one-patient-out method was used for cross-validation of results. An area under the receiver operating characteristic curve of 0.9890 ± 0.0114 was obtained using randomly selected centroids and an expected size of the masses. Results indicate that the use of the proposed contour-independent features can be an effective approach for computer-aided classification of mammographic lesions.

Based on recent statistics from the International Agency for Research on Cancer (Ferlay et al., 2010), breast cancer account for 10.9% of all cancers diagnosed and ranks as the fifth cause of cancer death in the world. Although incidence rates are increasing, mortality rates are stable, representing an improved survival rate. This improvement can be attributed to effective means for the early detection as well as to significant improvement in treatment options, exposure, etc. Mammography is, at present, the only viable method for detecting most of tumors early enough for effective treatment, without unnecessary biopsies, or other invasive procedures. Therefore, screening mammography in women aged 40 to 70 years is currently the effective strategy to reduce breast cancer mortality. Early detection of invasive breast cancers is associated with better prognosis than waiting for women to become symptomatic. However, detecting the early signs of breast cancer is challenging because the cancerous structures have many features in common with normal breast tissue. Moreover, the accuracy of interpretation of screening mammograms is affected by several factors, such as image quality, the radiologist’s level of expertise, and the high volume of cases. Recent statistics show that in current breast cancer screenings 10% − 25% of the tumors are missed by the radiologists (Burrell et al., 2001; Cheng, 2003; Nishikawa, 2007). Missed cancers are due to many reasons: low disease prevalence, breast structure complexity, finding subtleties, and radiologist fatigue. To accomplish with these difficulties, different methods have been analyzed: first of all, double reading, which provides either double perception or double interpretation of lesions. It has been demonstrated that a single radiologist is more accurate when reading mammograms methodically than quickly and that two observers achieve an improvement in detection rate of 5% − 15% (Mazzarella & Bazzocchi, 2007). Obviously, this procedure is too expensive, complex, and time consuming especially in screening programs where a huge number of mammographic images have to be read. The development of computerized systems as second readers represents an alternative. Researchers have been developing algorithms to detect mammographic abnormalities for more than 30 years with the aim of either automating mammographic interpretation or providing a tool that could enhance human film-reading accuracy. Computer-aided detection (CADe) and diagnosis (CADx) systems are widely used in mammography, where signs of breast cancer are often very subtle. Both systems involve the use of computer algorithms to detect patterns in images associated with signs of disease and to assign them a malignancy index. This result 15

In this work we propose a joint two-side masking procedure for automatic analysis of mammographic images. The primary objective is the improvement of computerized systems capability in revealing additional findings, as the asymmetrical changes of the breast parenchyma. The method allows the proper comparison of the left and right breast by progressive selection of paired small areas on the mammogram, primarily the so-called “forbidden areas”, zones that need special attention in mammographic interpretation. The masking of specific areas of the mammogram requires the identification of two anatomical structures of the breast: the pectoral muscle and the nipple used, together with the breast skin line, to find paired matching points on the images for comparison. With this purpose, specific algorithms have been developed. In particular, a new method for nipple extraction will be presented and validated by expert radiologists, by the use of a proprietary program developed by the authors. Finally, an application example of the automatic Tabar masking procedure will be shown, in order to point out the potential of this method in detection of suspicious areas in mammograms.

In this paper, we propose a novel approach for the automatic breast boundary segmentation using spatial fuzzy c-means clustering and active contours models. We will evaluate the performance of the approach on screen film mammographic images digitized by specific scanner devices and full-field digital mammographic images at different spatial and pixel resolutions. Expert radiologists have supplied the reference boundary for the massive lesions along with the biopsy proven pathology assessment. A performance assessment procedure will be developed considering metrics such as precision, recall, F-measure, and accuracy of the segmentation results. A Montecarlo simulation will be also implemented to evaluate the sensitivity of the boundary extracted on the initial settings and on the image noise.

Breast cancer is the second most common cancer overall and the leading cause of cancer deaths in women. Mammography is, at present, the only viable method for detecting most of tumors early enough for effective treatment. The secret of setting up the accurate diagnosis is to detect and understand the most subtle signs of breast lesions. Analysis of asymmetry between the left and right mammograms can provide clues about the presence of early signs of tumors. In this work we present an automated procedure for bilateral asymmetry detection composed of the following steps: (1) mammography density analysis and fibro-glandular disc detection through adaptive clustering techniques, (2) analysis and implementation of bilateral asymmetries detection algorithms based on Gabor filters analysis, (3) use of a linear Bayes classifier with the leave-one-out method to asses the asymmetry degree of the two breasts, (4) metrological evaluation of the whole system through random and systematic measurement uncertainty contributions modeling.

Microcalcifications clusters are subtle signs of breast cancer and are very difficult to diagnose in the mammographic images because of their small size and low contrast with respect to the normal breast tissue. In this paper, we propose an automatic procedure for the identification of microcalcifications clusters to aid the radiologist in the detection of this kind of breast cancer signs.

Breast cancer is the most common cancer in women. Fortunately, in the last years, the percentage of recovery has become higher and higher. The reasons are multiple: prevention, better surgery techniques, better diagnoses. In order to improve diagnostic performance, many Computer Aided Diagnosis systems have been developed in the latest years, which help the radiologists to detect and classify lump-masses. These systems, however, do not yet consider the uncertainty associated to the measurements (the mammography), even if, from a metrological point of view, they should. In this paper, measurement uncertainty is considered through a fuzzy inference system for the classification of microcalcifications in digital mammography.

Radiologists that analyze screening mammographic images miss the 10–20% of the diagnosis since this kind of images are very difficult to interpret. In this paper, we present the first step of a CADx (Computer Aided Diagnosis) system that, from the original mammogram, extracts suspicious regions on which the radiologists have to focus their attention. The procedure often successes also in case of very low contrast, because it depends only on the orientation of the gradient vectors in the image but not on their amplitude.

The paper presents a first implementation of a novel tool to assist radiologists in analyzing screening mammographic images. The software named Assisted Breast Cancer Diagnosis Environment (ABCDE) is able to acquire DICOM images (also in presence of Grayscale Softcopy Presentation State). It is designed to assist the doctor, as a second reader, during the various phases of the diagnosis. The program uses some public libraries and implements many new algorithms developed in the last years by the research group.

In this paper, we consider uncertainty handling and propagation by means of random fuzzy variables (RFVs) through a computer-aided-diagnosis (CADx) system for the early diagnosis of breast cancer. In particular, the denoising and the contrast enhancement of microcalcifications is specifically addressed, providing a novel methodology for separating the foreground and the background in the image to selectively process them. The whole system is then assessed by metrological aspects. In this context, we assume that the uncertainty associated to each pixel of the image has both a random and a non-negligible systematic contribution. Consequently, a preliminary noise variance estimation is performed on the original image, and then, using suitable operators working on RFVs, the uncertainty propagation is evaluated through the whole system. Finally, we compare our results with those obtained by a Monte Carlo method.

Breast cancer is the most common cause of death among women and the most effective method for its diagnosis is mammography. However, this kind of analysis is very difficult to interpret and for this reason radiologists miss 20-30% of tumors. We propose a module for the segmentation of masses that can be implemented in a complete CADx (Computer Aided Diagnosis) system. In particular, we implement a new version of the region growing algorithm specific for this kind of images and for the constraints on computation time of this application.

Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and macrocalcfications, are difficult to be detected because mammograms are low contrast and noisy images. In this paper, we present an algorithm for mammographic images enhancement and denoising based on the wavelet transform. In particular, we develop an adaptive procedure to perform an optimal denoising using a local iterative fuzzy noise variance estimation. Moreover, the degree of enhancement is adaptively tuned at each scale. The proposed algorithm has been tested on clinical images.