Auto-shape lossless compression of pharynx and esophagus fluoroscopic images.

The massive number of medical images produced by fluoroscopic and other conventional diagnostic imaging devices demand a considerable amount of space for data storage. This paper proposes an effective method for lossless compression of fluoroscopic images. The main contribution in this paper is the extraction of the regions of interest (ROI) in fluoroscopic images using appropriate shapes. The extracted ROI is then effectively compressed using customized correlation and the combination of Run Length and Huffman coding, to increase compression ratio. The experimental results achieved show that the proposed method is able to improve the compression ratio by 400 % as compared to that of traditional methods.

Prediction models for early risk detection of cardiovascular event.

Cardiovascular disease (CVD) is the major cause of death globally. More people die of CVDs each year than from any other disease. Over 80% of CVD deaths occur in low and middle income countries and occur almost equally in male and female. In this paper, different computational models based on Bayesian Networks, Multilayer Perceptron,Radial Basis Function and Logistic Regression methods are presented to predict early risk detection of the cardiovascular event. A total of 929 (626 male and 303 female) heart attack data are used to construct the models.The models are tested using combined as well as separate male and female data. Among the models used, it is found that the Multilayer Perceptron model yields the best accuracy result.

Improved biliary detection and diagnosis through intelligent machine analysis.

This paper reports on work undertaken to improve automated detection of bile ducts in magnetic resonance cholangiopancreatography (MRCP) images, with the objective of conducting preliminary classification of the images for diagnosis. The proposed I-BDeDIMA (Improved Biliary Detection and Diagnosis through Intelligent Machine Analysis) scheme is a multi-stage framework consisting of successive phases of image normalization, denoising, structure identification, object labeling, feature selection and disease classification. A combination of multiresolution wavelet, dynamic intensity thresholding, segment-based region growing, region elimination, statistical analysis and neural networks, is used in this framework to achieve good structure detection and preliminary diagnosis. Tests conducted on over 200 clinical images with known diagnosis have shown promising results of over 90% accuracy. The scheme outperforms related work in the literature, making it a viable framework for computer-aided diagnosis of biliary diseases.

A novel strategy for load balancing of distributed medical applications.

Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.

Graph-cut energy minimization for object extraction in MRCP medical images.

Bile duct identification and extraction in magnetic resonance cholangiopancreatography (MRCP) images, is a necessary step in the development of computer-aided diagnosis systems using such images. MRCP is becoming the de facto modality in the diagnosis of biliary diseases and even in the pre-surgical workup for liver transplants. The energy minimization graph-cut method is a proven technique in the extraction of objects in natural images, and even used in 3D reconstruction. This paper proposes several versions of the graph-cut approach for the extraction of the biliary structures in MRCP images. The schemes include a fully interactive lazy snapping method, a manual point selection method for minimal user interaction and an automated phase unwrapping via max flows (PUMA) implementation. The performance of the algorithms vary, but the results support that the scheme is a promising semi-automated object extraction scheme for the significant biliary structures in medical MRCP images.

Magnetic resonance cholangiopancreatography image enhancement for automatic disease detection.

AIM: To sufficiently improve magnetic resonance cholangiopancreatography (MRCP) quality to enable reliable computer-aided diagnosis (CAD). METHODS: A set of image enhancement strategies that included filters (i.e. Gaussian, median, Wiener and Perona-Malik), wavelets (i.e. contourlet, ridgelet and a non-orthogonal noise compensation implementation), graph-cut approaches using lazy-snapping and Phase Unwrapping MAxflow, and binary thresholding using a fixed threshold and dynamic thresholding via histogram analysis were implemented to overcome the adverse characteristics of MRCP images such as acquisition noise, artifacts, partial volume effect and large inter- and intra-patient image intensity variations, all of which pose problems in application development. Subjective evaluation of several popular pre-processing techniques was undertaken to improve the quality of the 2D MRCP images and enhance the detection of the significant biliary structures within them, with the purpose of biliary disease detection. RESULTS: The results varied as expected since each algorithm capitalized on different characteristics of the images. For denoising, the Perona-Malik and contourlet approaches were found to be the most suitable. In terms of extraction of the significant biliary structures and removal of background, the thresholding approaches performed well. The interactive scheme performed the best, especially by using the strengths of the graph-cut algorithm enhanced by user-friendly lazy-snapping for foreground and background marker selection. CONCLUSION: Tests show promising results for some techniques, but not others, as viable image enhancement modules for automatic CAD systems for biliary and liver diseases.

Customizable wireless remote control for collaborative medical diagnosis and teaching.

Effective collaborative diagnosis, in a presentation environment, requires the presenter's ability to control equipment at a distance. Conventional remote control multimedia presenters suffice for basic functionality of presentation applications. However, such factory-made remote controls are not particularly useful for a wider variety of applications, especially in the medical field where effective use of the systems and applications require the use of different keys. This paper reports the development of a USB (universal serial bus) remote control with customizable buttons, using RF (radio frequency) technology and allowing for effective control for a large range of manipulations on a workstation. Description of the implementation ideas and results achieved in the development of the firmware and hardware for such a technological tool for use in improving collaborative medical diagnosis and teaching are presented in this article.

Cost effective patient location monitoring system using webcams.

This paper details the development of a simple webcam joystick, a wireless, or rather cableless, and contactless pointing device by using a webcam and a simple flexible non-electronic joystick. Such a system requires no power source on the joystick, allows for light, robust and very mobile joysticks, and can be extended into a large array of applications. This paper proposes the use of small webcam joysticks as sensors for recording movement, the way wireless sensors are used. Specifically, it could be used as a simple navigation and monitoring system for patient movement in medical wards, where knowledge of patient location and movement could provide instant assistance, pre-emptive action and also hinder untoward patient mix-ups. Experiments and discussions in this paper highlight how a successful implementation is possible, and emphasize the flexibility of such an implementation in a low cost medical environment.

Cholangiocarcinoma--an automated preliminary detection system using MLP.

Cholangiocarcinoma, cancer of the bile ducts, is often diagnosed via magnetic resonance cholangiopancreatography (MRCP). Due to low resolution, noise and difficulty is actually seeing the tumor in the images, especially by examining only a single image, there has been very little development of automated systems for cholangiocarcinoma diagnosis. This paper presents a computer-aided diagnosis (CAD) system for the automated preliminary detection of the tumor using a single MRCP image. The multi-stage system employs algorithms and techniques that correspond to the radiological diagnosis characteristics employed by doctors. A popular artificial neural network, the multi-layer perceptron (MLP), is used for decision making to differentiate images with cholangiocarcinoma from those without. The test results achieved was 94% when differentiating only healthy and tumor images, and 88% in a robust multi-disease test where the system had to identify the tumor images from a large set of images containing common biliary diseases.

Segment-growing hierarchical model for bile duct detection in MRCP.

Magnetic resonance cholangiopancreatography (MRCP) is the popular diagnostic imaging sequence for the diagnosis and surgery workup for the pancreatobiliary system and liver. The technique is relatively noisy and suffers from imaging characteristics such as the partial volume effect and varying acquisition orientation, making automatic analysis of the images difficult. This paper explores some of the popular image processing techniques with the goal of selecting suitable features in MRCP images, as a basis for preliminary computer-aided diagnosis systems in biliary structure image reconstruction and disease detection. Visual results and observations are given and analyzed. The findings support that many popular techniques such as texture analysis fail to highlight the structures of interest in MRCP images, whereas multi-scale, multi-resolution and dynamic thresholding achieve better success. The proposed multi-scale combination technique known as the Segment-Growing Hierarchical Model produced good visual results for detection of the bile ducts.

Node status algorithm for load balancing in distributed service architectures at paperless medical institutions.

Service architectures are necessary for providing value-added services in telecommunications networks, including those in medical institutions. Separation of service logic and control from the actual call switching is the main idea of these service architectures, examples include Intelligent Network (IN), Telecommunications Information Network Architectures (TINA), and Open Service Access (OSA). In the Distributed Service Architectures (DSA), instances of the same object type can be placed on different physical nodes. Hence, the network performance can be enhanced by introducing load balancing algorithms to efficiently distribute the traffic between object instances, such that the overall throughput and network performance can be optimised. In this paper, we propose a new load balancing algorithm called "Node Status Algorithm" for DSA infrastructure applicable to electronic-based medical institutions. The simulation results illustrate that this proposed algorithm is able to outperform the benchmark load balancing algorithms-Random Algorithm and Shortest Queue Algorithm, especially under medium and heavily loaded network conditions, which are typical of the increasing bandwidth utilization and processing requirements at paperless hospitals and in the telemedicine environment.

Reconstruction of biliary structure in 2D MRCP images using multi-scale analysis.

Magnetic resonance cholangio pancreatography (MRCP) has become a reference technique for biliary tree analysis. Typical MRCP images, however, suffer from difficulty in distinguishing the structure of the biliary tree in order to identify abnormalities, for clinical diagnosis. For efficiency in analysing MRCP image series, the need arises for the use of semi-automated image processing techniques. A segment-based multi-scale approach is described, incorporated with image selection, enhancement and watershed segmentation, to identify and reconstruct the hierarchical biliary tree structure in 2D MRCP images. The results achieved may be further extended to higher dimensional images.

A computer-aided multidisease diagnostic system using MRCP.

Automated computer analysis of magnetic resonance cholangiopancreatography (MRCP) (a focused magnetic resonance imaging sequence for the pancreatobiliary region of the abdomen) images for biliary diseases is a difficult problem because of the large inter- and intrapatient variations in the images, varying acquisition settings, and characteristics of the images, defeating most attempts to produce computer-aided diagnosis systems. This paper proposes a system capable of automated preliminary diagnosis of several diseases affecting the bile ducts in the liver, namely, dilation, stones, tumor, and cyst. The system first identifies the biliary ductal structure present in the MRCP images, and then proceeds to determine the presence or absence of the diseases. Tested on a database of 593 clinical images, the system, which uses visual-based features, has shown to be successful in delivering good performance of 70-90% even in the presence of multiple diseases, and may be useful in aiding medical practitioners in routine MRCP examinations.

Implementing cost-effective 3-dimensional ultrasound using visual probe localization.

This article proposes a set-up for a 3-dimensional ultrasound system using visual probe localization on the conventional 2-dimensional ultrasound machines readily available in most hospitals. A calibrated digital camera is used for probe-tracking (localization) purposes, whereas ultrasound probe calibration is implemented using a purpose-built phantom. The calibration steps and results are detailed here. The overall system is proven effective in clinical trials through scanning of human organs. Results obtained show successful, accurate 3-dimensional representations using this simple cost-effective set-up.

Cost-effective handling of digital medical images in the telemedicine environment.

BACKGROUND: This paper concentrates on strategies for less costly handling of medical images. Aspects of digitization using conventional digital cameras, lossy compression with good diagnostic quality, and visualization through less costly monitors are discussed. METHOD: For digitization of film-based media, subjective evaluation of the suitability of digital cameras as an alternative to the digitizer was undertaken. To save on storage, bandwidth and transmission time, the acceptable degree of compression with diagnostically no loss of important data was studied through randomized double-blind tests of the subjective image quality when compression noise was kept lower than the inherent noise. A diagnostic experiment was undertaken to evaluate normal low cost computer monitors as viable viewing displays for clinicians. RESULTS: The results show that conventional digital camera images of X-ray images were diagnostically similar to the expensive digitizer. Lossy compression, when used moderately with the imaging noise to compression noise ratio (ICR) greater than four, can bring about image improvement with better diagnostic quality than the original image. Statistical analysis shows that there is no diagnostic difference between expensive high quality monitors and conventional computer monitors. CONCLUSION: The results presented show good potential in implementing the proposed strategies to promote widespread cost-effective telemedicine and digital medical environments.

A visual probe localization and calibration system for cost-effective computer-aided 3D ultrasound.

The 3D ultrasound systems produce much better reproductions than 2D ultrasound, but their prohibitively high cost deprives many less affluent organization this benefit. This paper proposes using the conventional 2D ultrasound equipment readily available in most hospitals, along with a single conventional digital camera, to construct 3D ultrasound images. The proposed system applies computer vision to extract position information of the ultrasound probe while the scanning takes place. The probe, calibrated in order to calculate the offset of the ultrasound scan from the position of the marker attached to it, is used to scan a number of geometrical objects. Using the proposed system, the 3D volumes of the objects were successfully reconstructed. The system was tested in clinical situations where human body parts were scanned. The results presented, and confirmed by medical staff, are very encouraging for cost-effective implementation of computer-aided 3D ultrasound using a simple setup with 2D ultrasound equipment and a conventional digital camera.

Stone detection in MRCP images using controlled region growing.

Stones in the biliary tract are routinely identified using MRCP (magnetic resonance cholangiopancreatography). The noisy nature of the images, as well as varying intensity, size and location of the stones, defeat most automatic detection algorithms, making computer-aided diagnosis difficult. This paper proposes a multi-stage segment-based scheme for semi-automated detection of choledocholithiasis and cholelithiasis in the MRCP images, producing good performance in tests, differentiating them from "normal" MRCP images. With the high success rate of over 90%, refinement of the scheme could be applicable in the clinical environment as a tool in aiding diagnosis, with possible applications in telemedicine.

Discontinuous region growing scheme for preliminary detection of tumor in MRCP images.

Tumors are generally difficult to detect in Magnetic Resonance (MR) images as they can be of varying intensities and do not appear as clear structures on these images. This difficulty is more prominent in MR Cholangiopancreatography (MRCP), which is the MR technology using a special sequence of T2-weighted imaging to identify the biliary tract, pancreatic duct, and gallbladder in the liver region, as MRCP images are more noisy in nature and are acquired for a more focused area with too much flexibility in position orientation for convenient computer-aided diagnosis. Based on the principle that the tumor mass manifests itself as blockage of the biliary tree structure, this paper introduces a technique that uses a region growing algorithm to identify discontinuities in the biliary tree as a means to preliminary detection of a possible tumor, in a fashion similar to the visual observation used by most radiologists in making their preliminary diagnosis. Through the use of appropriate image normalization, watershed segmentation, thresholding, rule-based region growing, and region analysis, the proposed technique is shown in this paper to be successful in identifying MRCP images with liver carcinoma from those with normal liver. Acquisition standardization, interactive image selection, and optimum image orientation will further enhance the accuracy of this proposed scheme for use in aiding clinical diagnosis at medical institutions.

Neural networks aided stone detection in thick slab MRCP images.

This paper proposes a detection scheme for identifying stones in the biliary tract of the body, which is examined using magnetic resonance cholangiopancreatography (MRCP), a sequence of magnetic resonance imaging targeted at the pancreatobiliary region of the abdomen. The scheme enhances the raw 2D thick slab MRCP images and extracts the biliary structure in the images using a segment-based region-growing approach. Detection of stones is scoped within this extracted structure, by highlighting possible stones. A trained feedforward artificial neural network uses selected features of size and average segment intensity as its input to detect possible stones in MRCP images and eliminate false stone-like objects. The proposed scheme achieved satisfactory results in tests of clinical MRCP thick slab images, indicating potential for implementation in computer-aided diagnosis systems for the liver.

Improving diagnostic quality of MR images through controlled lossy compression using SPIHT.

This paper attempts to improve the diagnostic quality of magnetic resonance (MR) images through application of lossy compression as a noise-reducing filter. The amount of imaging noise present in MR images is compared with the amount of noise introduced by the compression, with particular attention given to the situation where the compression noise is a fraction of the imaging noise. A popular wavelet-based algorithm with good performance, Set Partitioning in Hierarchical Trees (SPIHT), was employed for the lossy compression. Tests were conducted with a number of MR patient images and corresponding phantom images. Different plausible ratios between imaging noise and compression noise (ICR) were considered, and the achievable compression gain through the controlled lossy compression was evaluated. Preliminary results show that at certain ICR's, it becomes virtually impossible to distinguish between the original and compressed-decompressed image. Radiologists presented with a blind test, in certain cases, showed preference to the compressed image rather than the original uncompressed ones, indicating that under controlled circumstances, lossy image compression can be used to improve the diagnostic quality of the MR images.