Augmented reality based real-time subcutaneous vein imaging system

A novel 3D reconstruction and fast imaging system for subcutaneous veins by augmented reality is presented. The study was performed to reduce the failure rate and time required in intravenous injection by providing augmented vein structures that back-project superimposed veins on the skin surface of the hand. Images of the subcutaneous vein are captured by two industrial cameras with extra reflective near-infrared lights. The veins are then segmented by a multiple-feature clustering method. Vein structures captured by the two cameras are matched and reconstructed based on the epipolar constraint and homographic property. The skin surface is reconstructed by active structured light with spatial encoding values and fusion displayed with the reconstructed vein. The vein and skin surface are both reconstructed in the 3D space. Results show that the structures can be precisely back-projected to the back of the hand for further augmented display and visualization. The overall system performance is evaluated in terms of vein segmentation, accuracy of vein matching, feature points distance error, duration times, accuracy of skin reconstruction, and augmented display. All experiments are validated with sets of real vein data. The imaging and augmented system produces good imaging and augmented reality results with high speed

Roadmap on holography

From its inception holography has proven an extremely productive and attractive area of research. While specific technical applications give rise to 'hot topics', and three-dimensional (3D) visualisation comes in and out of fashion, the core principals involved continue to lead to exciting innovations in a wide range of areas. We humbly submit that it is impossible, in any journal document of this type, to fully reflect current and potential activity; however, our valiant contributors have produced a series of documents that go no small way to neatly capture progress across a wide range of core activities. As editors we have attempted to spread our net wide in order to illustrate the breadth of international activity. In relation to this we believe we have been at least partially successful.This work was supported by Ministerio de Economía, Industria y Competitividad (Spain) under projects FIS2017-82919-R (MINECO/AEI/FEDER, UE) and FIS2015-66570-P (MINECO/FEDER), and by Generalitat Valenciana (Spain) under project PROMETEO II/2015/015

Adaptive Ridge Point Refinement for Seeds Detection in X-Ray Coronary Angiogram

Seed point is prerequired condition for tracking based method for extracting centerline or vascular structures from the angiogram. In this paper, a novel seed point detection method for coronary artery segmentation is proposed. Vessels on the image are first enhanced according to the distribution of Hessian eigenvalue in multiscale space; consequently, centerlines of tubular vessels are also enhanced. Ridge point is extracted as candidate seed point, which is then refined according to its mathematical definition. The theoretical feasibility of this method is also proven. Finally, all the detected ridge points are checked using a self-adaptive threshold to improve the robustness of results. Clinical angiograms are used to evaluate the performance of the proposed algorithm, and the results show that the proposed algorithm can detect a large set of true seed points located on most branches of vessels. Compared with traditional seed point detection algorithms, the proposed method can detect a larger number of seed points with higher precision. Considering that the proposed method can achieve accurate seed detection without any human interaction, it can be utilized for several clinical applications, such as vessel segmentation, centerline extraction, and topological identification

Diagrammatic sketch for hunting the adaptive searching window.

<p>Diagrammatic sketch for hunting the adaptive searching window.</p

Denoising results for a 61-year-old female patient with liver tumor.

<p>The first column from (a) to (e) shows the SDCT image, LDCT image, and images denoised with AT-PCA, NLM and KSVD, respectively. Red circles and numbers denote positions of tumors (numbers 1, 2, 3, 4, 5) and the backgrounds (numbers 6 and 7). The second column from (a’) to (e’) shows the zoomed regions corresponding to (a) to (e).</p

STDs of three ROIs for LDCT, SDCT, and processed LDCT images of a 53-year-old female patient with liver tumor.

<p>STDs of three ROIs for LDCT, SDCT, and processed LDCT images of a 53-year-old female patient with liver tumor.</p

Denoised results of the “House” image at different stages.

<p>(a) Original House image; (b) Noisy image; (c) Denoised result in the first stage; (d) Denoised result in the second stage; (e) Denoised result in the third stage; (f) Denoised result in the fourth stage.</p

Illustration of regions of interest for STD calculation.

<p>Illustration of regions of interest for STD calculation.</p