Abstract Masses are important elements in the diagnosis of breast cancer. Many studies discussed the problem of detection and/or diagnosis of masses and most of these researches were based on shape descriptors to make decision. Textural descriptors contribute in indicating the presence of masses. Morphological descriptors determine their malignancy degree. Thus, we decided in our work to make a combination of morphological and textural descriptors. In fact, this method allowed us to extract different features in order to help make a decision concerning the malignancy of masses. The shape descriptor “Zernike moments” has the advantages to be invariant to the rotation and to be orthogonal. In addition, the texture descriptor “local binary attributes” provides information about the local variations of gray levels in the image. A multi-layer perceptron is used in the classification stage. The results were validated by using 160 regions of interest which are extracted from the database of mammographic images DDSM (Digital Database for Screening Mammography). We obtained an area under the ROC (Receiver Operating Characteristics) curve which is equal to 0,96. The results were confirmed by a radiologist. Read More Read Less

Abstract Iris segmentation step is an essential process in iris recognition system. This step becomes much more difficult due to the presence of eyelids and eyelashes. This paper contributes to fast and robust eyelids and eyelash detection algorithm. The main contributions are: (1) Proposition of robust eyelids detection algorithm based on the Radon transform and polynomial curve fitting, using Least Squares Fitting method. (2) Eyelashes detection using diagonal gradient and thresholding process. The proposed method was evaluated on the CASIA V3 database and compared to the previous work. As the experimental results show that the proposed algorithm provides an encouraging performance in terms of accuracy and computational complexity. Moreover our method is very useful for iris recognition system which requires excluding the bits generated from eyelashes regions during iris matching stage. Read More Read Less

Abstract The increase in CPU power and screen quality of today’s smartphones as well as the availability of high bandwidth wireless networks has enabled high quality mobile videoconferencing never seen before. However, adapting to the variety of devices and network conditions that come as a result is still not a trivial issue. In this paper, we present a multiple participant videoconferencing service that adapts to different kind of devices and access networks while providing a stable communication. By combining network quality detection and the use of a multipoint control unit for video mixing and transcoding, desktop, tablet and mobile clients can participate seamlessly. We also describe the cost in terms of bandwidth and CPU usage of this approach in a variety of scenarios. Read More Read Less