Read e-book online Developments in Medical Image Processing and Computational PDF

By João Manuel R. S. Tavares, Renato Natal Jorge (eds.)

ISBN-10: 331913406X

ISBN-13: 9783319134062

ISBN-10: 3319134078

ISBN-13: 9783319134079

This ebook provides novel and complex issues in clinical snapshot Processing and Computational imaginative and prescient so that it will solidify wisdom within the comparable fields and outline their key stakeholders. It comprises prolonged types of chosen papers awarded in VipIMAGE 2013 – IV overseas ECCOMAS Thematic convention on Computational imaginative and prescient and scientific photo, which happened in Funchal, Madeira, Portugal, 14-16 October 2013.

The twenty-two chapters have been written by way of invited specialists of foreign acceptance and deal with vital matters in clinical snapshot processing and computational imaginative and prescient, together with: 3D imaginative and prescient, 3D visualization, color quantisation, continuum mechanics, information fusion, facts mining, face attractiveness, GPU parallelisation, picture acquisition and reconstruction, photograph and video research, photo clustering, picture registration, picture restoring, photo segmentation, computer studying, modelling and simulation, item detection, item reputation, item monitoring, optical circulate, development acceptance, pose estimation, and texture analysis.

diversified functions are addressed and defined in the course of the booklet, comprising: biomechanical reports, bio-structure modelling and simulation, bone characterization, mobile monitoring, computer-aided prognosis, dental imaging, face attractiveness, hand gestures detection and popularity, human movement research, human-computer interplay, photograph and video knowing, picture processing, photo segmentation, item and scene reconstruction, item reputation and monitoring, distant robotic keep watch over, and surgical procedure planning.

This quantity is of use to researchers, scholars, practitioners and brands from numerous multidisciplinary fields, similar to man made intelligence, bioengineering, biology, biomechanics, computational mechanics, computational imaginative and prescient, special effects, machine technological know-how, desktop imaginative and prescient, human movement, imagiology, computing device studying, laptop imaginative and prescient, arithmetic, scientific snapshot, drugs, trend acceptance, and physics.

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Extra resources for Developments in Medical Image Processing and Computational Vision

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As presented in this contribution, we have utilized our previous methods [13, 15, 16] to RNFL texture analysis using commonly available high-resolution colour fundus images. We extended the potential of these methods in order to show usability of the proposed texture features and their combination. Our approach utilizes Gaussian Markov random field (GMRF) texture modeling and local binary patterns (LBP) to generate features useful for description of changes in the RNFL texture. Different regression models are tested as the predictors of the RNFL thickness using the proposed features.

IEEE Trans Med Imagin 8(4):297–230. 41482 16. Heckemann RA, Hajnal JV, Aljabar P, Rueckert D, Hammers A (2006) Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1):115–126. 061. com/ science/article/pii/S1053811906006458 Analysis of the Retinal Nerve Fiber Layer Texture Related to the Thickness Measured by Optical Coherence Tomography J. Odstrcilik, R. Kolar, R. P. Tornow, A. Budai, J. Jan, P. Mackova and M. Vodakova Abstract The retinal nerve fiber layer (RNFL) is one of the most affected retinal structures due to the glaucoma disease.

The curves in individual B-scans define segmentation of the RNFL of fundus images is corrected together with the increase of image contrast using CLAHE (Contrast Limited Adaptive Histogram Equalization) technique [20]. The RNFL texture is the most contrasted in the green (G) and the blue (B) channels of the input RGB image (Fig. 1). Therefore, an average of G and B channel (called GB image) is computed for each fundus image after CLAHE. Further, only the GB images are used for processing. In the first step, we manually selected small square-shaped image regions of interest (ROIs) with size of 61 × 61 pixels from all fundus images included in the group of normal subjects.

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Developments in Medical Image Processing and Computational Vision by João Manuel R. S. Tavares, Renato Natal Jorge (eds.)

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