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

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

This publication offers novel and complex subject matters in clinical snapshot Processing and Computational imaginative and prescient on the way to solidify wisdom within the comparable fields and outline their key stakeholders. It comprises prolonged models of chosen papers offered in VipIMAGE 2013 – IV overseas ECCOMAS Thematic convention on Computational imaginative and prescient and clinical photograph, which came about in Funchal, Madeira, Portugal, 14-16 October 2013.

The twenty-two chapters have been written through invited specialists of foreign attractiveness and tackle very important concerns in clinical snapshot processing and computational imaginative and prescient, together with: 3D imaginative and prescient, 3D visualization, color quantisation, continuum mechanics, info fusion, information mining, face attractiveness, GPU parallelisation, photograph acquisition and reconstruction, photo and video research, photograph clustering, photo registration, photograph restoring, photograph segmentation, desktop studying, modelling and simulation, item detection, item attractiveness, item monitoring, optical movement, development popularity, pose estimation, and texture analysis.

assorted purposes are addressed and defined in the course of the booklet, comprising: biomechanical stories, bio-structure modelling and simulation, bone characterization, cellphone monitoring, computer-aided analysis, dental imaging, face reputation, hand gestures detection and popularity, human movement research, human-computer interplay, snapshot 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, comparable to man made intelligence, bioengineering, biology, biomechanics, computational mechanics, computational imaginative and prescient, special effects, machine technology, machine imaginative and prescient, human movement, imagiology, desktop studying, desktop imaginative and prescient, arithmetic, clinical snapshot, medication, development popularity, and physics.

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Infrared reflection images (scanning laser ophthalmoscope—SLO) and OCT B–scan (cross-sectional) images were acquired simultaneously. Acquisition of the OCT image volume (Fig. 2a) was performed within the peripapillary area. Circular scan pattern (Fig. 2b) is usually used for glaucoma diagnosis via OCT. 4 mm is placed in the center of the optic disc (OD) and one B-scan is measured along this circle [18]. 22 J. Odstrcilik et al. Fig. 1 An example of an original RGB fundus image of the healthy eye and individual colour channels of the image.

10 represent the ranks of particular observations i = 1, . . ,n of the respective variables. Spearman’s rank correlation coefficient was chosen because of two main properties: (i) it can measure a general monotonic relationship between two variables, even when the relationship is not necessarily linear, and (ii) it is robust to outliers due to ranking of values. Even when the correlation between y and c can be strong, the predicted values can still differ from the target values with some error.

Analysis of the Retinal Nerve Fiber Layer Texture . . 25 Fig. 4 At the top: section of the GB image after CLAHE processing with depiction of ROI positions; at the bottom: few examples of magnified ROIs with the RNFL texture taken in different positions in the peripapillary area (around the OD) 26 J. Odstrcilik et al. Fig. 5 The RNFL thickness map mapped on the SLO image of a normal subject. The colour spectral scale represents the changes of RNFL thickness approx. 2 Feature Extraction The advance texture analysis methods, namely Gaussian Markov random field (GMRF) [24] and local binary patterns (LBP) [25] were used for the description of RNFL texture.

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