R. Lojacono

Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing

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.

Type-2 Fuzzy Sets for Modeling Uncertainty in Measurement

A correct representation of uncertainty in measurement is crucial in many applications. Statistical approach sometimes is not the best choice, especially when the knowledge of the measurement process refers only to the support of the values and does not allow a correct assumption on the probability density function (pdf) of the measured variable. In this paper we present an approach that uses the concept of generalized fuzzy numbers, namely type-2 fuzzy sets, in order to handle the intrinsic dispersion of the possible pdfs associated to a variable. The relation between our representation and the so called random fuzzy variables (RFV) will be also investigated. The use of this representation allows to easily implement the uncertainty propagation, through a functional model, by working directly on the type-2 fuzzy numbers and by evaluating simultaneously the propagation results for the whole set of confidence levels. Anyway, when a statistical analysis can be performed, the results can be embedded in this generalized representation. Moreover, the new approach allows to assign to the final measurement value a reliable confidence level also in this case, by combining the expanded uncertainty evaluated following IEC-ISO guide recommendations with the type-2 fuzzy numbers associated to the output variable. An example of this representation was provided

Structurally passive digital filters in residue number systems

The possibility of realizing structural passive digital filters in terms of residue number systems (RNS) is considered. For this purpose, an algorithm is proposed for realizing the RNS rotator, which represents the basic element of these filters. The algorithm is discussed and illustrated in detail in the case of an 8-b dynamic range. The possibility of extending the dynamic range by the Chinese remainder theorem (CRT) is also considered and a simplified reconstruction formula is proposed for reducing the computational cost of the CRT. >

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.

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.

Wavelet based adaptive algorithm for mammographic images enhancement and denoising

Mammography is the most effective method for early detection of breast diseases. However, the typical diagnostic signs, such as masses and microcalcifications, 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 phantom and clinical images.

Short term local meteorological forecasting using type-2 fuzzy systems

Meteorological forecasting is an important issue in research. Typically, the forecasting is performed at “global level,” by gathering data in a large geographical region and by studying their evolution, thus foreseeing the meteorological situation in a certain place. In this paper a “local level” approach, based on time series forecasting using Type-2 Fuzzy Systems, is proposed. In particular temperature forecasting is inspected. The Fuzzy System is trained by means of historical local time series. The algorithm uses a detrend procedure in order to extract the chaotic component to be predicted.

On a generalized T-norm for the representation of uncertainty propagation in statistically correlated measurements by means of fuzzy variables

The problem of uncertainty representation and propagation in the context of statistically correlated variables is commonly addressed by means of Monte Carlo simulation as recommended in IEC-ISO Guide. Moreover, in a recent literature, fuzzy sets have proved to be a valid alternative in the case of independent variables. Unfortunately, the problem of modelling statistically correlated variables, by means of fuzzy sets, is still an open problem. Since it is well known that T-norms are the natural way of combining fuzzy variables into a nonfuzzy function f, in this paper, we investigate how to generalize the class of T-norms, making it dependent from correlation coefficient in order to emulate different statistical correlation degree among variables. In order to validate the model a comparison with central limit theorem will be accomplished in the case of zero correlation while a practical example will be provided in order to compare the proposed method with Montecarlo simulation and with that obtained by uncertainty propagation described in IEC-ISO Guide.