Assesment of Stroke Risk Based on Morphological Ultrasound Image Analysis With Conformal Prediction

Non-invasive ultrasound imaging of carotid plaques allows for the development of plaque image analysis in order to assess the risk of stroke. In our work, we provide reliable confidence measures for the assessment of stroke risk, using the Conformal Prediction framework. This framework provides a way for assigning valid confidence measures to predictions of classical machine learning algorithms. We conduct experiments on a dataset which contains morphological features derived from ultrasound images of atherosclerotic carotid plaques, and we evaluate the results of four different Conformal Predictors (CPs). The four CPs are based on Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Naive Bayes classification (NBC), and k-Nearest Neighbours (k-NN). The results given by all CPs demonstrate the reliability and usefulness of the obtained confidence measures on the problem of stroke risk assessment

Measurement of Motion of Carotid Bifurcation Plaques

Video loops of B-mode ultrasound images of 35 carotid bifurcation plaques were obtained (4 symptomatic and 31 asymptomatic) from patients with carotid bifurcation atherosclerosis. Video loops were classified visually as showing concordant (n=22) or discordant motion (n=13). Concordant plaques were characterized by uniform orientation of motion throughout the cardiac cycle. Discordant plaques exhibited significant spread in motion orientation at different parts of the cardiac cycle, especially at systole. We developed a real-time motion analysis system that applies Farneback's method to estimate velocities between consecutive video frames. For our purposes, we allow a 100msec time interval between the video frames used in the analysis. This approach allows us to analyze significant motions associated with a larger time interval. Over each video frame, we measure the spread of the motion orientation around the dominant orientation. For each video, we look at the spreads of the motion orientations for different motion magnitudes. Using these motion-spread measurements, we can quantify discordant movement. The sum of maximum fan widths for the median pixel motions 5 to 3 (SMFW5to3) had a median value of 100 degrees and interquartile range (IQR) of (80, 110) degrees for the concordant plaques and 270, (230, 430) for the discordant plaques (P <; 0.001). Thus, we have a new tool to differentiate between concordant and discordant plaques

New models for region of interest reader classification analysis in chest radiographs

In several computer-aided diagnosis (CAD) applications of image processing, there is no sufficiently sensitive and specific method for determining what constitutes a normal versus an abnormal classification of a chest radiograph. In the case of lung nodule detection or in classifying the perfusion of pneumoconiosis, multiple radiograph readers (radiologists) are asked to examine and score specific regions of interest (ROIs). The readers provide size, shape and perfusion grades for the presence of opacities in each region and then use all the ROI grades to classify the lung as normal or abnormal. The combined grades from all readers are then used to arrive at a consensus normal or abnormal classification. In this paper, using area under the ROC curve, we evaluate new mathematical models that are based on mathematical statistics, logic functions, and several statistical classifiers to analyze reader performance in grading chest radiographs for pneumoconiosis as the first step toward applying this technique to early detection of nodules found in lung cancer. In pneumoconiosis, rounded opacities are on the order of 1-10 mm in size, while lung nodules are often not diagnosed until they reach a size on the order of 1 cm. © 2008 Elsevier Ltd. All rights reserved

Towards Diagnostically Robust Medical Ultrasound Video Streaming using H.264

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Guest Editorial Introduction to the Special Section on Computational Intelligence in Medical Systems

RECENT technological advances in medicine have facil- itated the development of complex biomedical systems including sophisticated biomedical signal devices and instru- ments, medical imaging equipment, and computer-aided diag- nosis (CAD) tools enabling the better delivery of healthcare services. In parallel, computational intelligence, incorporat- ing neural computing, fuzzy systems, evolutionary computing, and more recently, rough sets, and autoimmune systems have emerged as promising tools for the development, application, and implementation of intelligent systems. In the last ten years, there has been a significant effort in the application of computational intelligent techniques in nu- merous biomedical systems. These cover applications in med- ical decision-making [1], biosignal analysis, and biomedical engineering at large [2], medical imaging [3]–[5], bioinformat- ics [6], [7], and others. All these systems underlie the impact of these technologies in the biomedical domain. The aim of this special issue is to focus on the most recent applications of computational intelligent systems in medicine. Papersinthisspecialissuecoverinnovativeapplicationsofcom- putational intelligence in the following physiological systems: skin, skeletal, muscular, central nervous, peripheral nervous, systems of special senses (eye), cardiovascular, respiratory, and reproductive. A total of 45 papers were submitted for this special issue that were reviewed by at least three reviewers. Following the recommendations of the guest editors and the Editor-in-Chief, 19 papers were accepted for publication. The accepted papers were organized under the topics: General, Computational Biol- ogy, Biosignal Analysis, and Medical Imaging, with two, three, seven, and seven papers in each topic, respectively. Some of these papers (10 in total) have been published earlier by mistake, unfortunately in previous issues of the IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN B OMEDICINE . All the accepted papers are briefly summarized in the following section. It is generally accepted that, nowadays, health services are facing a number of complex interacting and multifactorial challenges [8]. To address these issues from the information and communication technologies (ICTs) perspective, the World Health Assembly (WHA) adopted an eHealth Strategy for the World Health Organization (WHO) [9]. The resolution docu- mented that the use of ICT for health is one of the most rapidly growing application areas in health today. Moreover, it was pro- posed that automated or semiautomated systems that support decision-making in a clinical environment would be very useful for the better support of healthcare services. In parallel with the WHO activities, the European Commis- sion (EC) in 2004 adopted the eHealth action plan [10], as well as subsequent directives [11], that cover a wide spectrum of eHealth services, ranging from cross-border interoperability of electronic health record systems, to electronic prescriptions and health cards, to new information systems that are targeting to reduce waiting times and errors to facilitate a more harmonious and complementary European approach to eHealth. These initiatives, as stated in the Prague Declaration of the EU Member States, stress the need to keep the momentum so that the potential advantages of gradual deployment of ICT in the health sector are not compromised by barriers of legal, technical, economic, or any other nature. At the same time, it is considered crucial that the benefits of eHealth applications and services are further enhanced and properly distributed among all the relevant stakeholders, patients and healthcare professionals, society, and the economy. To facilitate the provision of better, and more efficient and effective eHealth services as documented before, sophisticated and advanced medical systems based on computational intel- ligence have to be developed. Although significant steps have been carried out in this direction in the last two decades, the need still exists that intelligent medical systems be developed, covering a wider spectrum of services, and most importantly, be thoroughly evaluated before their deployment in clinical prac- tice. The papers in this special issue cover a wide range of applications, demonstrating the promising potential of compu- tational intelligence in medical systems

Ultrasound Imaging in the Analysis of Carotid Plaque Morphology for the Assessment of Stroke

The aim of this chapter is to summarise the recent advances in ultrasonic plaque characterisation and to evaluate the efficacy of computer aided diagnosis based on neural and statistical classifiers using as input texture and morphological features. Several classifiers like the K-Nearest Neighbour (KNN) the Probabilistic Neural Network (PNN) and the Support Vecton Machine (SVM) are evaluated resulting to a diagnostic accuracy up to 71.2%

Multi-scale AM-FM for lesion phenotyping

Age-related macular degeneration (AMD) is the most common cause of visual loss in the United States and is a growing public health problem. The presence and severity of AMD in current epidemiological studies is detected by the grading of color stereoscopic fundus photographs. The purpose of this study was to show that a mathematical technique, amplitude-modulation frequency modulation (AM-FM) can be used to generate multi-scale features for classifying pathological structures, such as drusen, on a retinal image. AM-FM features were calculated for N=120 40times40 regions from 5 retinal images presenting with age-related macular degeneration. The results show that with this technique, drusen can be differenced from normal retinal structures by more than three standard deviations using the AM-FM histograms. In addition, by using different color spaces highly accurate classification of structures of the retina is achieved. These results are the first step in the development of an automated AMD grading system