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Computer assisted detection (CAD) are able to detect and characterize suspicious mammographic images, micro calcifications, masses or more difficult, architectural distortion. With the exploitation of these different characteristics, the system can specify and predict the severity of the tumor to assess the risk in terms of Malignity/Benignness. Our work involves the development of a new method for screening breast cancer, this is achieved by developing a whole strategy of knowledge extraction through deep learning and medical ontology appropriate for the classification of regions selected from digital mammograms, for each radiological sign considered, namely, masses and micro calcifications. First, we extracted the parameters characterizing the images used as input to a deep convolutional neuron network CNN. The learning is supervised because the images used are images from the MARATHON database of the University of Florida; they are already diagnosed by experts. The second phase aims to add a semantic level to our classification through a specialized ontology developed for this purpose based on the BIRADS characterization system. Based on the evaluation performed, the proposed approach provides better classification results than the usual methods for assisting in the computer aided detection of breast cancer.

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In remote sensing, texture is commonly used to support spectral information particularly when spectral signatures of class of interest are similar. It is usually extracted using panchromatic band instead of multispectral bands. This is because panchromatic band has rich texture content due to its fine spatial resolution. Recent space-borne and pansharpening techniques can deliver multispectral images with a submetric resolution which are also good candidates for texture analysis. The difficulty in extracting texture in multispectral images is the fact that existing and widely used methods are limited to analyzing spatial relationship between pixels in a single band at a time. When multispectral images are used texture characterization is usually performed by analyzing spatial relationships in each spectral band independently. This ignores inter-band spatial relationships which can be a source of valuable source of information. This paper evaluates the capability of a recently proposed method named multiband compact texture unit. This method extracts texture by characterizing simultaneously spatial relationship in the same band and across the different bands. This evaluation is performed in the context of object-based classification paradigm using WorldView-2 image of a forest area. For that image-objects were generated through superpixel segmentation. Classification in the object-feature space is performed suing K nearest neighbor algorithm. The proposed approach is compared to two groups of methods. The first group includes texture methods that use only spatial relationships in the same band: Gabor features wavelets and Granulometry. The second group includes methods that use intra-band and inter-band spatial relationships: integrative gray-level co-occurrence matrix, opponent Gabor features and opponent local binary patterns. Experimental results show that texture extracted using both intra-band and inter-band spatial relationship improves the classification accuracy compared to when it is extracted in each spectral band independently. Among the methods of the second group that use both intra-band and inter-band spatial relationships, the multiband compact texture unit method produces the best results.