Giulia Rabottino

Metrological Characterization of a CADx System for the Classification of Breast Masses in Mammograms

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.

Performance evaluation of a region growing procedure for mammographic breast lesion identification

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.

DENOISING AND ENHANCEMENT OF MAMMOGRAPHIC IMAGES UNDER THE ASSUMPTION OF HETEROSCEDASTIC ADDITIVE NOISE BY AN OPTIMAL SUBBAND THRESHOLDING

Mammographic images suffer from low contrast and signal dependent noise, and a very small size of tumoral signs is not easily detected, especially for an early diagnosis of breast cancer. In this context, many methods proposed in literature fail for lack of generality. In particular, too weak assumptions on the noise model, e.g., stationary normal additive noise, and an inaccurate choice of the wavelet family that is applied, can lead to an information loss, noise emphasizing, unacceptable enhancement results, or in turn an unwanted distortion of the original image aspect. In this paper, we consider an optimal wavelet thresholding, in the context of Discrete Dyadic Wavelet Transforms, by directly relating all the parameters involved in both denoising and contrast enhancement to signal dependent noise variance (estimated by a robust algorithm) and to the size of cancer signs. Moreover, by performing a reconstruction from a zero-approximation in conjunction with a Gaussian smoothing filter, we are able to extract the background and the foreground of the image separately, as to compute suitable contrast improvement indexes. The whole procedure will be tested on high resolution X-ray mammographic images and compared with other techniques. Anyway, the visual assessment of the results by an expert radiologist will be also considered as a subjective evaluation.

Assisted Breast Cancer Diagnosis Environment: A Tool for DICOM mammographic images analysis

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.

Uncertainty evaluation in a fuzzy classifier for microcalcifications in digital mammography

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.

Uncertainty Modeling and Propagation Through RFVs for the Assessment of CADx Systems in Digital Mammography

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.

An Iris detector for tumoral masses identification in mammograms

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.

Uncertainty handling and propagation in X-ray images analysis systems by means of Random-Fuzzy Variables

In this paper we consider uncertainty handling and propagation by means of RFV through a Computer Aided Detection (CAD) system for denoising and contrast enhancement of mammographic images. In this context, we assume that uncertainty associated to each pixel of the image has both a random and a not negligible systematic contribution. So, after a noise variance estimation performed on the original image, using recent RFV mathematics, we propagate uncertainty through the whole system. Finally, we compare our results with those obtained by a Monte Carlo simulation.

Assessment of a Breast Mass Identification Procedure Using an Iris Detector

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.

Uncertainty propagation for the assessment of tumoral masses segmentation

In this paper, we perform the assessment of a tumoral mass segmentation and characterization algorithm by implementing the uncertainty propagation through the blocks. We use a Monte Carlo method owing to the iterative and very complex structure of the algorithms used. The validation of the results is based on confidence intervals for given coverage probabilities and ad hoc performance metrics.