To evaluate the global solar irradiation flux received at the ground surface for Algeria, an analytical model is implemented by processing satellite images and solving the equation of radiative transfer. This model is derived from that initially worked out by C. Gautier et al. in 1980 using high-resolution Goes images. We get that it is well adapted to the processing of lesser resolution images such as those collected by Meteosat 2 following the B2 format. The data base under study mainly consists of clear-sky B2 Meteosat images recorded every three hours in the visible channel (i.e., [0.4 -1.1 µm]) during the 1986/87 period and representing North Africa and Southern Europe. Hourly and daily global solar irradiation fluxes received at the ground on a horizontal surface have therefore been evaluated by applying the analytical model to the Meteosat images. The obtained results were compared to the hourly ground solar measurements recorded in the radiometric stations of Bouzareah (Algiers) and Oran during the 1986/87 period. Since the deviations between both types of solar data do not exceed 10%, the radiances estimated by modelling are found to be close to the related ground measurements.

In this work, we build a computer aided diagnosis (CAD) system of breast cancer for high risk patients considering the breast imaging reporting and data system (BIRADS), mapping main expert concepts and rules. Therefore, a bag of words is built based on the ontology of breast cancer analysis. For a more reliable characterization of the lesion, a feature selection based on Choquet integral is applied aiming at discarding the irrelevant descriptors. Then, a set of well-known machine learning tools are used for semantic annotation to fill the gap between low level knowledge and expert concepts involved in the BIRADS classification. Indeed, expert rules are implicitly modeled using a set of classifiers for severity diagnosis. As a result, the feature selection gives a a better assessment of the lesion and the semantic analysis context offers an attractive frame to include external factors and meta-knowledge, as well as exploiting more than one modality. Accordingly, our CAD system is intended for diagnosis of breast cancer for high risk patients. It has been then validated based on two complementary modalities, MRI and dual energy contrast enhancement mammography (DECEDM), the proposed system leads a correct classification rate of 99%.

No abstract

International audienceThis paper focuses on breast cancer of the mammary gland. Both basic segmentation steps and usual features are recalled. Then textural and morphological information are combined to improve the overall performance of breast MRI in a computer-aided system. A model of selection based on Choquet integral is provided. Such model is suitable when handling with a weak amount of data even ambiguous in some extent. Achieved results compared to well-known classification methods show the interest of our approach