The visual fidelity of realistic renderings in Computer Graphics depends fundamentally upon how we model the appearance of objects resulting from the interaction between light and matter reaching the eye. In this paper, we survey the research addressing appearance modelling of living human tissue. Among the many classes of natural materials already researched in Computer Graphics, living human tissues such as blood and skin have recently seen an increase in attention from graphics research. There is already an incipient but substantial body of literature on this topic, but we also lack a structured review as presented here. We introduce a classification for the approaches using the four types of human tissues as classifiers. We show a growing trend of solutions that use first principles from Physics and Biology as fundamental knowledge upon which the models are built. The organic quality of visual results provided by these Biophysical approaches is mainly determined by the optical properties of biophysical components interacting with light. Beyond just picture making, these models can be used in predictive simulations, with the potential for impact in many other areas.

Magnetic resonance imaging (MRI) is a very effective medical imaging technique for the clinical diagnosis and monitoring of neurological disorders. Because of intensity similarities between brain lesions and normal tissues, multispectral MRI modalities are usually applied for brain lesion detection. However, the time and cost restrictions for collecting multi-spectral MRI, and the issue of possible errors from registering multiple MR images necessitate developing an automatic lesion detection approach that can detect lesions using a single anatomical MRI modality. In this paper, an automatic algorithm for brain stroke and tumor lesion detection and segmentation using single-spectral MRI is presented. The proposed algorithm, called histogram-based gravitational optimization algorithm (HGOA), is a novel intensity-based segmentation technique, which applies enhanced gravitational optimization algorithm on histogram analysis results. The mathematical descriptions as well as the convergence criteria of the developed optimization algorithm are presented in detail. Using this algorithm, brain is segmented into different number of regions, which will be labeled as lesion or healthy. Here, the ischemic stroke lesions and tumor lesions are segmented with 91.5% and 88.1% accuracy, respectively.

Background Medical mentoring is becoming increasingly complex with the evolving needs of trainees and the complexities of their personal and social lives. The Internet is an enabling technology, which increasingly facilitates interaction with multiple people at a distance. Web 2.0 and 3.0 technology shows promise in furthering this facilitation. Objective The objective of our study was to establish opinions among doctors in postgraduate surgical training regarding mentoring and whether these doctors would readily accept virtual mentoring following a brief experience. Methods On the 12th of February 2012, an introductory teaching class was arranged by The London Postgraduate School of Surgery for doctors in training. Participants were introduced to a novel virtual mentoring system and asked to complete a questionnaire regarding their opinions before and after the demonstration. Results A total of 57 junior doctors attended. Among them, 35 completed questionnaires pre- and postdemonstration. Regarding usefulness of a 3D virtual environment for mentoring, 6/35 (17%) agreed or strongly agreed and 20/35 (57%) were unsure prior to the session. Following 20 minutes using MentorSL, this significantly increased to 14/35 (40%) agreeing or strongly agreeing with 11/35 (31%) unsure (P<.001). Prior to using MentorSL, regarding usefulness of voice communication for virtual mentoring, 11/35 (31%) agreed or strongly agreed and 18/35 (51%) were unsure. Following 20 minutes using MentorSL, 19/35 (54%) agreed or strongly agreed and 10/35 (29%) were unsure of usefulness. Regarding ease of use of navigation, search mentor, meeting scheduling, and voice communication features, 17/35 (49%), 13/35 (37%), 15/35 (43%), and 16/35 (46%) participants agreed or strongly agreed, respectively. Regarding usefulness of telementoring, 24/35 (69%) agreed or strongly agreed, increasing to 28/35 (80%) following the introduction. For usefulness of multiple mentors, initially 24/35 (69%) agreed or strongly agreed increasing to 29/35 (83%). For overall satisfaction, 30/35 (86%) reported good or adequate and 19/35 (54%) agreed or strongly agreed with using the system again. Conclusions These data suggest that a short introduction on how to use virtual systems may result in significant participation and use of virtual mentoring systems.

We present an integrated system for training ultrasound guided needle puncture. Our aim is to provide a cost effective and validated training tool that uses actual patient data to enable interventional radiology trainees to learn how to carry out image-guided needle puncture. The input data required is a computed tomography scan of the patient that is used to create the patient specific models. Force measurements have been made on real tissue and the resulting data is incorporated into the simulator. Respiration and soft tissue deformations are also carried out to further improve the fidelity of the simulator.

Interventional Radiology (IR) procedures are minimally invasive, targeted treatments performed using imaging for guidance. Needle puncture using ultrasound, x-ray, or computed tomography (CT) images is a core task in the radiology curriculum, and we are currently developing a training simulator for this. One requirement is to include support for physically-based simulation of x-ray images from CT data sets. In this paper, we demonstrate how to exploit the capability of today's graphics cards to efficiently achieve this on the Graphics Processing Unit (GPU) and compare performance with an efficient software only implementation using the Central Processing Unit (CPU).

The ever improving price-performance ratio of personal computers is making a significant contribution to widening the accessibility of training simulators for a wide variety of medical procedures. High fidelity solutions are becoming more and more sought after. However, the problem of providing realistic soft tissue deformation in real time remains, particularly if haptic interaction is also required. This paper presents a new approach for efficient soft tissue deformation using particle systems to model both structure and haptic properties of anatomy. We are applying this technique to a simulator for interventional radiology procedures, but it can easily be adapted for other medical domains.

Objective: The Op3D visualization system allows, for the first time, a surgeon in the operating theatre to interrogate patient-specific medical data sets rendered in three dimensions using high-performance computing. The hypothesis of this research is that the success rate of hepato-pancreatic surgical resections can be improved by replacing the light box with an interactive 3D representation of the medical data in the operating theatre. Materials and Methods: A laptop serves as the client computer and an easy-to-use interface has been developed for the surgeon to interact with and interrogate the patient data. To date, 16 patients have had 3D reconstructions of their DICOM data sets, including preoperative interrogation and planning of surgery. Results: Interrogation of the 3D images live in theatre and comparison with the surgeons' operative findings (including intraoperative ultrasound) led to the operation being abandoned in 25% of cases, adoption of an alternative surgical approach in 25% of cases, and helpful image guidance for successful resection in 50% of cases. Conclusions: The clinical value of the latest generation of scanners and digital imaging techniques cannot be realized unless appropriate dissemination of the images takes place. This project has succeeded in translating the image technology into a user-friendly form and delivers 3D reconstructions of patient-specific data to the “sharp end”—the surgeon undertaking the tumor resection in theatre, in a manner that allows interaction and interpretation. More time interrogating the 3D data sets preoperatively would help reduce the incidence of abandoned operations—this is part of the surgeons' learning curve. We have developed one of the first practical applications to benefit from remote visualization, and certainly the first medical visualization application of this kind.

Overview There is a significant opportunity for Web3D technology to make a real difference to medical practitioners. The use of the WWW for medical education and training is growing exponentially. It offers inexpensive training opportunities at virtually any location and at any time. This flexibility is ideal for the high pressured staff of a typical hospital. Few of the current training packages available for this medium are making use of 3D graphics and virtual environments, however.

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probability-based multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).

The use of stereoscopy to enhance the immersive experience obtained from a virtual environment is well known, and many medical applications can benefit from this technology. The stereoscopic projection sessions at the last two MMVR conferences contain many excellent examples. Stereoscopy need not be expensive to implement, and can easily be provided on a desktop PC. This paper looks at the different options available today, from active stereo, to passive stereo, to autostereoscopic displays, and their deployment on both high-end and low-end systems. The potential benefits of these different approaches are illustrated with examples from medical visualisation projects currently being carried out at the Manchester Visualization Centre (MVC).