Facial recognition techniques applied to the automated registration of patients in the emergency treatment of head injuries

This paper describes the development of a registration framework for image-guided solutions to the automation of certain routine neurosurgical procedures. The registration process aligns the pose of the patient in the preoperative space to that of the intra-operative space. CT images are used in the pre-operative (planning) stage, whilst white light (TV camera) images are used to capture the intra-operative pose. Craniofacial landmarks, rather than artificial markers, are used as the registration basis for the alignment. To further synergy between the user and the image-guided system, automated methods for extraction of these landmarks have been developed. The results obtained from the application of a Polynomial Neural Network (PNN) classifier based on Gabor features for the detection and localisation of the selected craniofacial landmarks, namely the ear tragus and eye corners in the white light modality are presented. The robustness of the classifier to variations in intensity and noise is analysed. The results show that such a classifier gives good performance for the extraction of craniofacial landmarks

A methodology for design and appraisal of surgical robotic systems

Surgical robotics is a growing discipline, continuously expanding with an influx of new ideas and research. However, it is important that the development of new devices take account of past mistakes and successes. A structured approach is necessary, as with proliferation of such research, there is a danger that these lessons will be obscured, resulting in the repetition of mistakes and wasted effort and energy. There are several research paths for surgical robotics, each with different risks and opportunities and different methodologies to reach a profitable outcome. The main emphasis of this paper is on a methodology for ‘applied research’ in surgical robotics. The methodology sets out a hierarchy of criteria consisting of three tiers, with the most important being the bottom tier and the least being the top tier. It is argued that a robotic system must adhere to these criteria in order to achieve acceptability. Recent commercial systems are reviewed against these criteria, and are found to conform up to at least the bottom and intermediate tiers, the most important first two tiers, and thus gain some acceptability. However, the lack of conformity to the criteria in the top tier, and the inability to conclusively prove increased clinical benefit, is shown to be hampering their potential in gaining wide establishment

Development of machine vision techniques for intraoperative registration and bleeding characterisation in robot-assisted neurosurgery

Development of machine vision techniques for intraoperative registration and bleeding characterisation in robot-assisted neurosurger

A machine learning approach to tracking and characterizing planar or near planar fluid flow from motion history images

This paper presents the design of a Machine Vision technique to segment planar or near-planar fluid flow, which uses artificial neural networks to characterize fluid flow in determining rate of flow and source of the fluid, which can be applied in various areas, e.g. characterizing fluid flow in surface irrigation from aerial pictures, in leakage detection and in surgical robotics for characterizing blood flow over an operative site. When applied to the latter, the outcome enables to assess bleeding severity and find the source of the bleeding. Based on its importance in assessing injuries in general and from a medical perspective in directing the course of surgery, fluid flow assessment is deemed to be a desirable addition to a surgical robot’s capabilities. The results from tests on simulated fluid flows obtained from a test rig show that the proposed methods can contribute to an automated characterization of fluid flow, which in the presence of several fluid flow sources can be achieved by tracking the flows, determining the locations of the sources and their relative severities, with execution times suitable for real-time operation

A machine learning approach to tracking and characterizing planar or near planar fluid flow

This paper presents a framework to segment planar or near-planar fluid flow and uses artificial neural networks to characterize fluid flow by determining the rate of flow and source of the fluid, which can be applied in various areas (e.g., characterizing fluid flow in surface irrigation from aerial pictures, in leakage detection, and in surgical robotics for characterizing blood flow over an operative site). For the latter, the outcome enables to assess bleeding severity and find the source of the bleeding. Based on its importance in assessing injuries and from a medical perspective in directing the course of surgery, fluid flow assessment is deemed to be a desirable addition to a surgical robot's capabilities. The results from tests on fluid flows generated from a test rig show that the proposed methods can contribute to an automated characterization of fluid flow, which in the presence of several fluid flow sources can be achieved by tracking the flows, determining the locations of the sources and their relative severities, with execution times suitable for real-time operation