An Improved Olympic Hole-Filling Method for Ultrasound Volume Reconstruction of Human Spine

Hole-filling in ultrasound volume reconstruction using freehand three-dimensional ultrasound estimates the values for empty voxels from the unallocated voxels in the Bin-filling process due to inadequate sampling in the acquisition process. Olympic operator, as a neighbourhood averaging filter, can be used to estimate the empty voxel. However, this method needs improvement to generate a closer estimation of the empty voxels. In this paper, the authors propose an improved Olympic operator for the Hole-filling algorithm, and apply it to generate the volume in a 3D ultrasound reconstruction of the spine. The conventional Olympic operator defines the empty voxels by sorting the neighbouring voxels, removing the n% of the upper and lower values, and averaging them to attain the value to fill the empty voxels. The empty voxel estimation can be improved by thresholding the range width of its neighbouring voxels and adjusting it to the average values. The method is tested on a hole-manipulated volume derived from a cropped 3D ultrasound volume of a part of the spine. The MAE calculation on the proposed technique shows improved result compared to all tested existing methods.

‘Pragmatic Evaluation’

E-Health continues to be implemented despite continued demonstration that it lacks value. Specific guidance regarding research approaches and methodologies would be beneficial due to the value in identifying and adopting a single model or framework for any one ‘entity’ (healthcare organisation, sub-national region, country, etc.) so that the evidence-base accumulates more rapidly and interventions can be more meaningfully compared. This paper describes a simple and systematic approach to e-health evaluation in a real-world setting, which can be applied by an evaluation team and raises the quality of e-health evaluations. The framework guides and advises users on evaluation approaches at different stages of e-health development and implementation. Termed ‘Pragmatic Evaluation,’ the approach has five principles that unfold in a staged approach that respects the collective need for timely, policy relevant, yet meticulous research.

The mHealth Stack

Applications accessible through mobile devices, such as mobile phones, are playing an increasingly important part in the delivery of high quality and personalized healthcare services. In this paper, we examine current usage of mobile devices and networks by mobile healthcare applications, and present our views on how mobile devices and networks could be used for creating patient-centered healthcare applications. The patient-centered healthcare paradigm allows for increased quality of care and quality of life for patients while increasing personal freedom to move about and be always connected to care-givers and healthcare services. The structure of our discussion is analogous to layered protocol stack in communications, progressing from the network and radio technologies, servicing middleware, cloud services, health sensors, mobile smartphones, and applications. All these layers come into play to support future mobile healthcare services.

A Semantic-Based Dynamic Search Engine Design and Implementation for Electronic Medical Records

In the current technology driven world, information retrieval activities are in almost every aspect of daily, as society uses popular web search engines like Google, Yahoo!, Live Search, Ask, and so forth to obtain helpful information. Often, these popular search engines look for and obtain key information; however, not all of the retrieved items are relevant in context to the search target a. Thus, it is left for the user to filter out unwanted information, using only a few information items left from the search results. These popular web search engines use a first generation search service based on “static keywords”, which require the users to know exactly what they want to search and enter the right keywords. This approach puts the user at a disadvantage. In this paper, the authors investigate and design a dynamic, question-answer search engine that enables searching by attributes for more precise and relevant information in Electronic Medical Record (EMR) field.

Active Noise Control for Hearing Screening Test

A hearing screening test is a method to determine human ear disorders and conventional audiometers and audiologists are required to perform the test. However, this procedure is difficult to implement, especially in a remote site such as a factory or a school due to the ambient noise that may cause test inaccuracy. In this work, the application of active noise control (ANC) is proposed to reduce the ambient noise using a personal computer in a hearing screening test. The ANC algorithm was simulated in MATLAB software and implemented using a computer with data acquisition modules and LabVIEW software. Results show that anti-noise was successfully generated in the electrical domain but no reduction was observed in the acoustic domain. ANC is a deterministic application that requires a real-time operating system to respond to the input with precisely timed output. To have an effective ANC system, the processing time has to be less than 0.125 ms at 8 KHz sampling rate.

Non-Compactness Attribute Filtering to Extract Retinal Blood Vessels in Fundus Images

Retinal blood vessels can give information about abnormalities or disease by examining its pathological changes. One abnormality is diabetic retinopathy, characterized by a disorder of retinal blood vessels resulting from diabetes mellitus. Currently, diabetic retinopathy is one of the major causes of human vision abnormalities and blindness. Hence, early detection can lead to proper treatment, and segmentation of the abnormality provides a map of retinal vessels that can facilitate the assessment of the characteristics of these vessels. In this paper, the authors propose a new method, consisting of a sequence of procedures, to segment blood vessels in a retinal image. In the method, attribute filtering with a so-called Max-Tree is used to represent the image based on its gray value. The filtering process is done using the branches filtering approach in which the tree branches are selected based on the non-compactness of the nodes. The selection is started from the leaves. This experiment was performed on 40 retinal images, and utilized the manual segmentation created by an observer to validate the results. The proposed method can deliver an average accuracy of 94.21%.

Privacy Challenges in the Use of eHealth Systems for Public Health Management

Privacy is a major issue in information management for public health needs. For example, the surveillance of infectious diseases, such as HIV, is an important function of public health and it presents major privacy concerns for affected people. While their privacy must be protected, privacy concerns should not come in the way of effective data collection and surveillance. In this paper, the authors present a survey of published work covering privacy challenges in the use of eHealth systems, especially in the context of public health management. The authors identify and present the major privacy challenges, their effects on personal patient privacy and public health management based on the review of research in electronic data privacy and eHealth privacy. The authors also present a survey of privacy-preserving technologies and solutions that address these challenges.

Phase Unwrapping Using Energy Minimization Methods for MRI Phase Image

Phase Unwrapping (PU) is reconstruction of absolute phase data from its wrapped phase. The absolute phase cannot be extracted from the wrapped phase data directly. Without phase noise, singularity, and aliasing problems, the phase information can be unwrapped easily. However, the phase data are always contaminated by noise and discontinuities, making the PU process more complicated. Therefore, a suitable PU algorithm is required to address the problems properly. In this method, the energy difference between neighborhood pixels in level 3 is counted, followed by getting the probability value to obtain its total fringes. The capability of the proposed method to unwrap simulated and actual MRI phase images is also demonstrated. In actual MRI phase image, PU can be implemented for water and fat separation.

An Automated Method for Differential Blood Counting Using Microscope Color Image of Isolated WBC

Healthcare Delivery Systems are becoming overloaded in developed and developing countries. It is imperative that more efficient and cost effective processes be employed by innovative applications of technology in the delivery system. One such process in Haematology that needs attention is “Generation of report on the Differential Count of Blood”. Most rural centers in India still employ traditional, manual processes to identify and count White Blood Cells under a microscope. This traditional method of manually counting the white blood cells is prone to human error and time consuming. Medical Imaging with innovative application of algorithms can be used for recognizing and analyzing the images from blood smears to provide an efficient alternative for differential counting and reporting. In this regard, the objective of this paper is to provide a simple and pragmatic software system built on innovative yet simple imaging algorithms for achieving better efficiency and accuracy of results. The resulting work-flow process has enabled truly practical tele-pathology by enabling e-collaboration between lesser skilled technicians and more skilled experts, which cuts down the total turnaround time for differential count reporting from days to minutes. The system can be extended to detect malarial parasites in blood and also cancerous cells.

Blood Vessel Segmentation in Complex-Valued Magnetic Resonance Images with Snake Active Contour Model

Accurate blood vessel segmentation plays a crucial role in non-invasive blood flow velocity measurement based on complex-valued magnetic resonance images. We propose a specific snake active contour model-based blood vessel segmentation framework for complex-valued magnetic resonance images. The proposed framework combines both magnitude and phase information from a complex-valued image representation to obtain an optimum segmentation result. Magnitude information of the complex-valued image provides a structural localization of the target object, while phase information identifies the existence of flowing matters within the object. Snake active contour model, which models the segmentation procedure as a force-balancing physical system, is being adopted as a framework for this work due to its interactive, dynamic, and customizable characteristics. Two snake-based segmentation models are developed to produce a more accurate segmentation result, namely the Model-constrained Gradient Vector Flow-snake (MC GVF-snake) and Stochastic-snake. MC GVF-snake elaborates a prior knowledge on common physical structure of the target object to restrict and guide the segmentation mechanism, while Stochastic-snake implements the simulated annealing stochastic procedure to produce improved segmentation accuracy. The developed segmentation framework has been evaluated on actual complex-valued MRI images, both in noise-free and noisy simulated conditions. Evaluation results indicate that both of the developed algorithms give an improved segmentation performance as well as increased robustness, in comparison to the conventional snake algorithm.