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Elawady M.,CNRS Hubert Curien Laboratory | Sadek I.,Image and Pervasive Access Laboratory | Shabayek A.E.R.,Suez Canal University | Pons G.,University of Girona | Ganau S.,Center Diagnostic
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2016

Breast cancer is one of the leading causes of cancer death among women worldwide. The proposed approach comprises three steps as follows. Firstly, the image is preprocessed to remove speckle noise while preserving important features of the image. Three methods are investigated, i.e., Frost Filter, Detail Preserving Anisotropic Diffusion, and Probabilistic Patch-Based Filter. Secondly, Normalized Cut or Quick Shift is used to provide an initial segmentation map for breast lesions. Thirdly, a postprocessing step is proposed to select the correct region from a set of candidate regions. This approach is implemented on a dataset containing 20 B-mode ultrasound images, acquired from UDIAT Diagnostic Center of Sabadell, Spain. The overall system performance is determined against the ground truth images. The best system performance is achieved through the following combinations: Frost Filter with Quick Shift, Detail Preserving Anisotropic Diffusion with Normalized Cut and Probabilistic Patch-Based with Normalized Cut. © Springer International Publishing Switzerland 2016. Source

Dao-Duc C.,National University of Singapore | Xiaohui H.,National University of Singapore | Xiaohui H.,Shanghai JiaoTong University | Morere O.,Institute for Infocomm Research | And 2 more authors.
ACM International Conference Proceeding Series | Year: 2015

The ability to identify maritime vessels and their type is an important component of modern maritime safety and security. In this work, we present the application of deep convolutional neural networks to the classification of maritime vessel images. We use the AlexNet deep convolutional neural network as our base model and propose a new model that is twice smaller then the AlexNet. We conduct experiments on different configurations of the model on commodity hardware. We comparatively evaluate and analyse the performance of different configurations the model. We measure the top-1 and top-5 accuracy rates. The contribution of this work is the implementation, tuning and evaluation of automatic image classifier for the specific domain of maritime vessels with deep convolutional neural networks under the constraints imposed by commodity hardware and size of the image collection. © 2015 ACM. Source

Aloulou H.,CNRS Laboratory for Informatics | Aloulou H.,Orange S.A. | Mokhtari M.,CNRS Laboratory for Informatics | Mokhtari M.,Orange S.A. | And 5 more authors.
IEEE Journal of Biomedical and Health Informatics | Year: 2014

On account of chronic neurocognitive disorders, many people progressively lose their autonomy and become more dependent on others, finally reaching the stage when they need round-the-clock care from caregivers. Over time, as patients' needs increase with the evolution of their diseases, caregivers experience increasing levels of stress and burden. Therefore, an assistive solution that is able to adapt to the changing needs of the end-users is needed. This need was considered as a major requirement that emerged from our field work and deployment experience in Singapore. In this paper, we focus on the technical aspects of our deployment, where we were interested in solving the technical requirement of adaptability and extendibility of the framework that has emerged from our predeployment analysis and discussions with professional caregivers. We expose our approach for dynamic integration of assistive services with their related sensing technologies and interaction devices and provide the technical results of the deployment of this solution. We also provide guidelines for real-world deployment of assistive solutions. © 2013 IEEE. Source

Aloulou H.,Image and Pervasive Access Laboratory | Aloulou H.,Orange S.A. | Mokhtari M.,Image and Pervasive Access Laboratory | Mokhtari M.,Orange S.A. | And 7 more authors.
BMC Medical Informatics and Decision Making | Year: 2013

Background: With an ever-growing ageing population, dementia is fast becoming the chronic disease of the 21st century. Elderly people affected with dementia progressively lose their autonomy as they encounter problems in their Activities of Daily Living (ADLs). Hence, they need supervision and assistance from their family members or professional caregivers, which can often lead to underestimated psychological and financial stress for all parties. The use of Ambient Assistive Living (AAL) technologies aims to empower people with dementia and relieve the burden of their caregivers.The aim of this paper is to present the approach we have adopted to develop and deploy a system for ambient assistive living in an operating nursing home, and evaluate its performance and usability in real conditions. Based on this approach, we emphasise on the importance of deployments in real world settings as opposed to prototype testing in laboratories. Methods. We chose to conduct this work in close partnership with end-users (dementia patients) and specialists in dementia care (professional caregivers). Our trial was conducted during a period of 14 months within three rooms in a nursing home in Singapore, and with the participation of eight dementia patients and two caregivers. A technical ambient assistive living solution, consisting of a set of sensors and devices controlled by a software platform, was deployed in the collaborating nursing home. The trial was preceded by a pre-deployment period to organise several observation sessions with dementia patients and focus group discussions with professional caregivers. A process of ground truth and system's log data gathering was also planned prior to the trial and a system performance evaluation was realised during the deployment period with the help of caregivers. An ethical approval was obtained prior to real life deployment of our solution. Results: Patients' observations and discussions allowed us to gather a set of requirements that a system for elders with mild-dementia should fulfil. In fact, our deployment has exposed more concrete requirements and problems that need to be addressed, and which cannot be identified in laboratory testing. Issues that were neither forecasted during the design phase nor during the laboratory testing surfaced during deployment, thus affecting the effectiveness of the proposed solution. Results of the system performance evaluation show the evolution of system precision and uptime over the deployment phases, while data analysis demonstrates the ability to provide early detection of the degradation of patients' conditions. A qualitative feedback was collected from caregivers and doctors and a set of lessons learned emerged from this deployment experience. (Continued on next page) (Continued from previous page). Conclusion: Lessons learned from this study were very useful for our research work and can serve as inspiration for developers and providers of assistive living services. They confirmed the importance of real deployment to evaluate assistive solutions especially with the involvement of professional caregivers. They also asserted the need for larger deployments. Larger deployments will allow to conduct surveys on assistive solutions social and health impact, even though they are time and manpower consuming during their first phases. © 2013 Aloulou et al.; licensee BioMed Central Ltd. Source

Goh H.,Institute for Infocomm Research | Goh H.,University Pierre and Marie Curie | Goh H.,Image and Pervasive Access Laboratory | Goh H.,French National Center for Scientific Research | And 7 more authors.
Proceedings - International Conference on Image Processing, ICIP | Year: 2011

Our objective is to learn invariant color features directly from data via unsupervised learning. In this paper, we introduce a method to regularize restricted Boltzmann machines during training to obtain features that are sparse and topographically organized. Upon analysis, the features learned are Gabor-like and demonstrate a coding of orientation, spatial position, frequency and color that vary smoothly with the topography of the feature map. There is also differentiation between monochrome and color filters, with some exhibiting color-opponent properties. We also found that the learned representation is more invariant to affine image transformations and changes in illumination color. © 2011 IEEE. Source

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