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Breast cancer is considered as one of a major health problem that constitutes the strongest cause behind mortality among women in the world. So, in this decade, breast cancer is the second most common type of cancer, in term of appearance frequency, and the fifth most common cause of cancer related death. In order to reduce the workload on radiologists, a variety of CAD systems; Computer-Aided Diagnosis (CADi) and Computer-Aided Detection (CADe) have been proposed. In this paper, we interested on CADe tool to help radiologist to detect cancer. The proposed CADe is based on a three-step work flow; namely, detection, analysis and classification. This paper deals with the problem of automatic detection of Region Of Interest (ROI) based on Level Set approach depended on edge and region criteria. This approach gives good visual information from the radiologist. After that, the features extraction using textures characteristics and the vector classification using Multilayer Perception (MLP) and k-Nearest Neighbours (KNN) are adopted to distinguish different ACR (American College of Radiology) classification. Moreover, we use the Digital Database for Screening Mammography (DDSM) for experiments and these results in term of accuracy varied between 60 % and 70% are acceptable and must be ameliorated to aid radiologist.

The Hardware (Ti W)/SofMare (SW) partitioning relies 017 hvo subtasks : the cost function and the real time (RT) analysis. Besides these two subtasks, the proposed generic &"work also called HT Design Trotter ( K T 0 7 processes the problem of the Quality of Service @OS) management. The aim of ihis paper is to add a new dimension to solution selection, namely the guarantee of QoSJi.om both application and RT points of view. The proposed ji-umework deJnes an iteration loop of ihree steps that solve the sub-problems. The cost hnction takes into account the System on Chip (SoC} area and the static and dynamic power dissipation. We show how our tool c m be used to rapid4 eVdUQle the impact of application qualily and RT constraints choices (QoSparamefers) over the final cost.

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