Apprentissage numérique pour la recherche d'informations en imagerie médicale : Modélisation des filtres de Gabor

ISBN : 978-1-4799-3391-4 / Part Number : CFP1316X-CDRInternational audienceWe propose in this paper , a method of indexing and searching images by exploiting the digital image content . Our method is based on the representation of digital image content by a specific vector to index image characteristics. This vector will be called : digital signature of the image. To do this, we exploited the texture of the image using Gabor wavelets . In this work , each image of the training set is represented by its indexed and characteristics ( texture ) . This representation takes place offline , is characterized by the backing , in a database , all the signatures of indexed images . This allows us, online, to search digital similarity to a query picture. This same query image is indexed online with the same algorithm used offline . To evaluate the performance we tested our application on a learning image database containing 320 mammograms. The results show that the representation of the digital image content is important in information retrieval imaging .Nous proposons, dans cet article, une méthode d'indexation et de recherche d'images en exploitant le contenu numérique des images. Notre méthode est fondée sur la représentation du contenu numérique de l'image par un vecteur de caractéristiques propres à l'image indexée. Ce vecteur sera appelé : signature numérique de l'image. Pour ce faire, nous avons exploité la texture de l'image en utilisant les ondelettes de Gabor. Dans ce travail, chaque image de la base d'apprentissage est indexée et représentée par ses caractéristiques (texture). Cette représentation, qui s'effectue en offline, est caractérisée par la sauvegarde, dans une base de données, de toutes les signatures des images indexées. Ce qui nous permet, en online, d'effectuer une recherche de similarité numérique par rapport à une image requête. Cette même image requête sera indexée en online avec le même algorithme utilisé en offline. Afin d'évaluer les performances nous avons testé notre application sur une base d'images d'apprentissage contenant 320 mammographies. Les résultats obtenus montrent bien que la représentation du contenu numérique des images s'avère important en matière de recherche d'information en imagerie

Étude Comparative en Indexation Appliquée à l'Image Médicale

International audienceNous avons vu dans un travail antérieur 1 une méthode d'indexation d'image et de recherche d'information sur un fond d'images mammographiques. Les images sont représentées par leur contenu numérique en plus des attributs sémantiques. La première technique se base sur l'extraction des paramètres de texture en utilisant les ondelettes de Gabor. La seconde se base sur une indexation textuelle : représentation par concepts sémantiques. Les résultats obtenus étaient relativement satisfaisants mais restreints à un type spécifique d'images: les mammographies. Nous avons donc pensé à tester la robustesse de notre algorithme dans à un éventail plus élargie d'images. Nous l'avons alors adapté à d'autres types d'image autre que mammographiques: cérébrales et autres images non médicales utilisées comme "parasites" afin de mieux évaluer les performances. Nous exposerons dans ce papier l'approche utilisée ainsi que les différents résultats obtenus. Une comparaison des résultats sera alors réalisée à la fin de ce papier avant de terminer par une conclusion. Mots clés: Recherche d'images par le contenu, Signature numérique d'image, Paramètres de texture, Filtre de Gabor, Concepts sémantiques, Indexation textuelle. Abstract : We saw in a former work a method of images indexing and information research on mammographic image-scanner databases. The images are represented by their digital component in addition to the semantic attributes. The first technique is based on the extraction of the texture parameters by using the Gabor's Wivelets. The second is based on a textual indexing: representation by semantic concepts. The obtained results were relatively satisfactory but restricted with a specific type of images: mammographies. We thus thought of testing the robustness of our algorithm in to a range more widened of images. We then adapted it to other types of image other than mammographic: cerebral and other nonmedical images used like " parasites " for better evaluating the performances. We will expose in this paper the approach used as well as the various results obtained. A comparison of the results will then be carried out at the end of this paper before finishing by a conclusion

Optimization of Energy Consumption of Rain Gauge Network Using MSG Infrared Image

This paper details the development of an easy-to-use, ultra-low power wireless data logger incorporating a scalable, intelligent data collection and transmission topology for rain measurement. The design of gauges sensor node of Wireless Sensor Network (WSN) is accomplished using low consumption Microcontroller and electronics components operating with Zigbee wireless network technology. To optimize the rate consumption of the energy for each sensor, we propose a novel technique based on using, in real time, the Meteosat Second Generation (MSG) infrared images to control the activation of the gauge sensors in the precipitation area. To reach this aim, we developed routing strategies for the Zigbee WSN. Some results of simulations are shown to illustrate the performance of our optimization strategies

Images indexing and matched assessment of semantics and visuals similarities applied to a medical learning X-ray image base

International audienceBACKGROUND: Medical diagnostic support systems are important tools in the field of radiology. However, the precision obtained, during the exploitation of high homogeneity image datasets, needs to be improved. OBJECTIVE: To develop a new learning system dedicated to public health practitioners. This study presents an upgraded version dedicated to radiology experts for better clinical decision-making when diagnosing and treating the patient (CAD approach). METHODS: Our system is a hybrid approach based on a matching of semantic and visual attributes of images. It is a combination of two complementary subsystems to form the intermodal system. The first one named α based on semantic attributes. Indexing and image retrieval based on specific key words. The second system named β based on low-level attributes. Vectors characterizing the digital content of the image (color, texture and shape) represent images. Our image database consists of 930 X-ray images including 320 mammograms acquired from the mini-MIAS database of mammograms and 610 X-rays acquired from the Public Hospital Establishment (EPH-Rouiba Algeria). The combination of two subsystems gives rise to the intermodal system: α-subsystem offers an overall result (based on visual descriptors), then β-subsystem (low level descriptors) refines the result and increases relevance. RESULTS: Our system can perform a specific image search (in a database of images with very high homogeneity) with an accuracy of around 90% for a recall of 25% . The average (overall) accuracy of the system exceeds 70% . CONCLUSION: The results obtained are very encouraging, and demonstrate efficiency of our approach, particularly for the intermodal system

Wind Measurement Based on MEMS Micro-Anemometer With High Accuracy Using ANN Technique

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Numerical training for the information research in medical imagery : Modeling of the Gabor filters

International audienceWe propose, in this paper, a method of image indexing and research by exploiting the digital component of the images. Our method is founded on the representation of the image digital component by a vector of characteristics its own for the indexed image. This vector will be called: numerical signature of the image. With this intention, we exploited the texture of the image by using the Gabor’s wavelets. In this work, each image of the training base is indexed and represented by its characteristics (texture). This representation, which is carried out in offline, is characterized by the saving, in a data base, of all the signatures of the indexed images. What enables us, in online, to carry out a numerical search for similarity compared to a request image. This same request image will be indexed in online with the same algorithm used in offline. In order to evaluate the performances we tested our application on a training images basis containing 320 mammography. The results obtained show well that the representation of the digital component of the images proves to be significant as regards search for information in imagery

Multimodal Indexing and Information Retrieval in Medical Image Mammographies: Digital Learning Based on Gabor Filters Model

International audienceIn this chapter, we propose a new indexing approach on medical “image scanner” databases combiningthe analysis process of the texture characteristics with the information contents. The proposed modelis based on the digital image components using the vector of characteristics. This vector represent themorphological processing result on image texture. It is linked to semantic attributes of the image usingthe annotations of medical professionals. Our context of study is based on “Mammographic ImageAnalysis” (MIAS) in databases. The first aspect concerning the morphology processing on images calledthe “numerical signature” vector. In our approach, the image analysis of the texture is based on theGabor Wavelets (or Filters) Theory. In offline processing for each image in MIAS databases, the GaborWavelets determine all numerical signatures: vectors of image characteristics as multi-index. In online,the query by image is in real-time processing to define the query signature (or image-query vectors)and to determine similarities by matching of multi-index with all images in databases. The similaritiesare built between the image-query and images in MIAS databases using the same Gabors’ algorithmsimplemented. In order to evaluate the robustness of our system (based on multi-index, semantic attributes,query and information retrieval by image), we experiment with a controlled database of 320mammographies. The performance results show a set of successful criteria in image representationsbased on the Gabor’s Wavelets, semantic attributes and combining with significant ratios in the systemrecall and precision

Wall shear stress and pressure sensors development for active flow control

International audienceIn this paper we present a technology for wall shear stress and pressure integrated sensor fabrication. Thanks to the use of SOI wafers and wafer bonding technique, we came up with an innovative technology that provides high on-chip density of sensors required for arrays utilized in numerous microfluidic applications like active control of flow. At the end some wall shear stress results are presented

A microcontroller based wall shear stress measurement

International audienceA new type of hot wire anemometer was developed by using surface micro machining techniques. The reduction of the microprobes section and the development of a thermal isolation cavity below the hot polycrystalline silicon wire reduced considerably indirect thermal exchanges between the hot wire and its substrate. As a consequence, the response time of the sensor is considerably improved allowing the detection of fast fluctuations and turbulent phenomena in fluid mechanics. In this paper we briefly describe the realisation technology of the developed sensors, its operation principles and the data acquisition circuit especially conceived and realised to perform turbulent flow detection in a low speed wind tunnel