Efficient Representation of Geometric Tree Models with Level-of-Detail Using Compressed 3D Chain Code

In the paper, we present a method for space-efficient representation of geometric tree models, which are provided as skeletons with radii attached to individual branch segments. The proposed approach uses a new differential 3D chain code to encode orientation changes of consecutive branch segments, which allows optimizing chain code generation for increased compressibility while maintaining control over the model reconstruction error. The presented method is the first to encode the complete branching geometry including the branch radii and provides level-of-detail construction directly from the chain code. It is demonstrated that by using interpolative encoding of the resulting tree descriptors and radii sequences, the storage requirements for geometric description of a mixed all-aged forest can be reduced to less than 15 percent of its raw size while preserving the structural fidelity of tree models.

An innovative approach to near-infrared spectroscopy using a standard mobile device and its clinical application in the real-time visualization of peripheral veins

Background

Excessive venipunctures are a significant problem both in emergency rooms and during hospital stays. Near-infrared (NIR) illumination devices improve venipuncture success rate but their usage is limited by their availability and economic cost. The objectives of this study were to develop a low-cost NIR spectroscopy prototype from a standard mobile device, to evaluate its efficacy and acceptance as an educational tool, and in a clinical setting.

Methods

Through a user-centric design process a prototype device was developed. Its educational efficacy was evaluated through a non-invasive, observational study (20 student clinicians, 25 subjects) and its acceptance was assessed using quantitative and qualitative analysis. A smaller clinical trial was performed by a group of 4 medical professionals over a period of 6 weeks that involved 64 patients.

Results

The prototype enables real-time visualization of peripheral veins on a variety of Android-based devices. The prototype was 35.2% more successful in visualizing and locating veins (n = 500 attempts) than the nursing students. The acceptance assessment revealed high perception of usefulness, satisfaction, and ease of use. In the clinical trial, 1.6 (SD 1.3) additional veins per patient were identified compared with the traditional visualization methods.

Conclusions

To the best of our knowledge this is the first study that describes the design, feasibility and application of an NIR spectroscopy prototype developed on a standard mobile device.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-014-0100-z) contains supplementary material, which is available to authorized users.

Towards a low-cost mobile subcutaneous vein detection solution using near-infrared spectroscopy

Excessive venipunctures are both time- and resource-consuming events, which cause anxiety, pain, and distress in patients, or can lead to severe harmful injuries. We propose a low-cost mobile health solution for subcutaneous vein detection using near-infrared spectroscopy, along with an assessment of the current state of the art in this field. The first objective of this study was to get a deeper overview of the research topic, through the initial team discussions and a detailed literature review (using both academic and grey literature). The second objective, that is, identifying the commercial systems employing near-infrared spectroscopy, was conducted using the PubMed database. The goal of the third objective was to identify and evaluate (using the IEEE Xplore database) the research efforts in the field of low-cost near-infrared imaging in general, as a basis for the conceptual model of the upcoming prototype. Although the reviewed commercial devices have demonstrated usefulness and value for peripheral veins visualization, other evaluated clinical outcomes are less conclusive. Previous studies regarding low-cost near-infrared systems demonstrated the general feasibility of developing cost-effective vein detection systems; however, their limitations are restricting their applicability to clinical practice. Finally, based on the current findings, we outline the future research direction.

Progressive lossless compression of volumetric data using small memory load

Nowadays, applications dealing with volumetric datasets, Medical applications being a typical representative, have become possible even on low cost computers due to a rapid increase of computer memory and processing power. However, even today, dealing with volumetric datasets creates two considerable problems: slow visualization and large file sizes. While recently, due to significant progress in graphics hardware, real-time or near real-time volume visualization has become possible, volume compression still remains a problematic issue. This paper introduces a new method for lossless compression of volumetric datasets. It is based on quadtree encoding. The method consists of three steps: during initialization, so-called division quadtree is built. The smallest unit of the division quadtree is called basic macro-block. During the processing phase, Boolean intersection is built on pairs of quadtrees, and the differences are stored. In the last phase, the variable length encoding is applied to reduce the entropy among the differences. Proposed method supports progressive visualization, what is especially important when a transfer trough the internet is needed. To test the efficiency of this method it was compared to popular octree encoding scheme. The results proved that data coherence is exploited more sufficiently using proposed quadtree approach. Additional advantage of this approach is that the algorithm does not need a lot of memory space. Only two quadtrees of two consecutive slices need be loaded in the memory at the same time. This feature makes this algorithm extremely attractive for possible hardware implementation. This paper introduces a new method for the compression of volumetric datasets. It is based on quadtree encoding. This method consists of three steps: during initialization, a so-called division quadtree is built. The smallest, unit of the division quadtree is called a basic macro-block. A Boolean intersection is built on pairs of quadtrees during the processing phase and the differences are stored. In the last phase, variable length encoding is applied to reduce entropy among the differences. This method has been compared with the popular octree-based method and gives, in general, better compression results. In addition, this method can be realized using small on-board memory.