{"title":"Integrating digital photographs with medical imaging examinations","authors":"S. Tridandapani, Eugene Berkowitz, P. Bhatti","doi":"10.1109/IECBES.2010.5742225","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742225","url":null,"abstract":"The integration of patient digital photographic images with medical imaging studies can serve as a powerful identification tool and prevent medical errors. Photographs can also provide supplemental clinical information that can improve diagnosis. Digital cameras can easily and inexpensively be incorporated in all medical imaging devices such that point-of-care photographs can be obtained along with medical imaging tests.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134334385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Development of cellular neural network algorithm for detecting lung cancer symptoms","authors":"A. Abdullah, Hasdiana Mohamaddiah","doi":"10.1109/IECBES.2010.5742216","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742216","url":null,"abstract":"Lung cancer is the most common of lethal types of cancer. One of the most important and difficult tasks a doctor has to carry out is the detection and diagnosis of cancerous lung nodules from x-ray image's result. Some of these lesions may not be detected because of camouflaged by the underlying anatomical structure, the low-quality of the images or the subjective and variable decision criteria used by doctors. Hence, a detection system using cellular neural network (CNN) is developed in order to help the doctors to recognize the doubtful lung cancer regions in x-ray films. In this study, a CNN algorithm for detecting the boundary and area of lung cancer in x-ray image has been proposed. Computer simulation result shows that our CNN algorithm is verified to detect some key lung cancer symptoms successfully and has been proved by radiologist.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134376399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Hani, Hurriyatul Fitriyah, E. Prakasa, V. Asirvadam, S. Hussein, M. A. Azura
{"title":"In vivo 3D thickness measurement of skin lesion","authors":"A. Hani, Hurriyatul Fitriyah, E. Prakasa, V. Asirvadam, S. Hussein, M. A. Azura","doi":"10.1109/IECBES.2010.5742219","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742219","url":null,"abstract":"Thickness is one of the morphological characteristic of skin lesion that represents severity condition. Dermatologists use tactile inspection to subjectively assess the thickness by feeling the alteration of the lesion from its surrounding normal skin. In this paper, a method to objectively measure the abnormal elevation occurs in skin lesions is presented. A 3D fringe projection scanner is used to obtain 3D surface profile of the lesion. Thickness of a lesion is defined as the elevations of lesion surface from its lesion base. The lesion base is determined from the neighboring normal skin using a 3D surface interpolation technique. The lesion elevations are determined in a 3D space grid by subtracting the elevation of the lesion surface profile from the interpolated lesion base profile at all corresponding locations thus giving lesion thickness as the average value of the elevations. The algorithm has been validated using 3D surface samples with an error of 0.031 mm ± SD 0.014 mm (95% Confidence Interval: ±0.0011 mm). The validated algorithm has been successfully applied to measure thicknesses of 450 psoriasis plaque lesions with severity level ranging from mild to severe and thickness ranging from 0.021 mm to 0.883 mm. From the measured thicknesses, Psoriasis Area and Severity Index (PASI) thickness scores 0 to 4 are then determined using unsupervised K-means Clustering.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"129 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133617008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Frequency domain analysis of radial pulse in abnormal health conditions","authors":"Bhaskar Thakker, A. Vyas","doi":"10.1109/IECBES.2010.5742233","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742233","url":null,"abstract":"Radial pulse has been utilized to identify health status in Indian and Chinese complementary medicine approaches. The radial pulse signal in abnormal health conditions show variations in its morphology in comparison to that of the healthy subjects, resulting in changes in the pulse power spectrum. Comparison between the power spectrum of pulse signal for the subjects suffering from gastrointestinal disorders and healthy subjects has been carried out in this work. A frequency domain feature “Band Energy Ratio (BER)” has been defined to identify the band of frequencies carrying significant difference between these two groups. Abnormal bands showing significant elevation of energies has been identified as 4Hz to 10Hz band. Based on Receiver Operating Characteristics (ROC) analysis, 8 Hz to 10 Hz band has been identified carrying further significance giving 89.7% sensitivity, 90.5% specificity and 90% accuracy as optimum parameters to segregate the two groups.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"35 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130017557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. R. Ram, K. V. Madhav, E. Krishna, K. N. Reddy, K. Reddy
{"title":"Adaptive reduction of motion artifacts from PPG signals using a synthetic noise reference signal","authors":"M. R. Ram, K. V. Madhav, E. Krishna, K. N. Reddy, K. Reddy","doi":"10.1109/IECBES.2010.5742252","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742252","url":null,"abstract":"Pulse oximeters estimate both the heart rate and oxygen saturation accurately and are widely used in clinical applications for monitoring the patients at risk of hypoxia. The raw pulse oximeter signal namely Photoplethysmogram (PPG) usually suffers from motion artifacts (MA) corruption, due to the voluntary or involuntary movements of patient while recording the data from PPG sensor. The identification and elimination of these erroneous signal features has received much attention in the scientific literature over recent years. In this paper, we present a simple and efficient adaptive filtering technique for MA reduction using a synthetic noise reference signal without any extra hardware for noise reference signal generation. A thorough experimental analysis is carried out on real MA corrupted PPG data (for horizontal, vertical and bending motions of finger) to demonstrate the efficacy of the proposed method. Simulation results and statistical analysis reveal that the proposed method has shown better performance in MA reduction, making it suitable for pulse oximetry applications.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"2021 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114027018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Sim, M. K. Ong, S. S. Chong, J. Ng, C. Tso, S. Choo, A. H. Rozalina
{"title":"Auto detection of brain ventricles using Hausdorff distance","authors":"K. Sim, M. K. Ong, S. S. Chong, J. Ng, C. Tso, S. Choo, A. H. Rozalina","doi":"10.1109/IECBES.2010.5742228","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742228","url":null,"abstract":"Brain plays an important role in human anatomy. The brain ventricular system can often be affected by different kinds of brain lesion, to the extent of creating an imbalance problem in the brain system. Hence, it is useful to develop a method to check the brain condition and to detect the existence of ventricles as well. A template of the ventricle is first created. Then ventricle detection in a slice of brain scan is done by using the Hausdorff distance. The presence of ventricles is smoothed by a median filter. Then, the ventricles are split into left and right halves through the computed centroid. Ratio is calculated to detect the brain abnormality. Result showed that 50% of the patients were detected accurately based on the ventricles.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133673686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Models of nanoparticle transport in dielectrophoretic microdevices: Prediction, parameter estimation and other applications","authors":"D. Bakewell, A. Chichenkov, N. A. M. Yunus","doi":"10.1109/IECBES.2010.5742226","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742226","url":null,"abstract":"This paper describes the applications of Fourier Bessel series models for characterising the transport of nanoparticles driven by dielectrophoretic forces and randomized by Brownian motion. Nanoparticle transport using dielectrophoresis continues to be an active area of research and models are fundamental for characterising the process. The models have very useful capabilities including prediction of nanoparticle transport, estimation of parameter values from experimental data, and data decomposition into space and time components. The models also give a frequency domain representation that will be applicable for time modulated dielectrophoretic nanoparticle transport.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"408 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133350285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Israa Al-Qaysi, Z. Othman, R. Unland, C. Weihs, C. Branki
{"title":"Holonic and optimal medical decision making under uncertainty","authors":"Israa Al-Qaysi, Z. Othman, R. Unland, C. Weihs, C. Branki","doi":"10.1109/IECBES.2010.5742247","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742247","url":null,"abstract":"Holonic multi agent medical diagnosis system combines the advantages of the holonic paradigm, multi agent system technology, and swarm intelligence in order to realize a highly reliable, adaptive, scalable, flexible, and robust Internet based diagnosis system for diseases. This paper concentrate on the decision process within our system and will present our ideas, which are based on decision theory, and here, especially, on Bayesian probability since, among others, uncertainty is inherent feature of a medical diagnosis process. The presented approach focuses on reaching the optimal medical diagnosis with the minimum risk under the given uncertainty. Additional factors that play an important role are the required time for the decision process and the produced costs.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116797047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Blind Deconvolution for retinal image enhancement","authors":"U. Qidwai, U. Qidwai","doi":"10.1109/IECBES.2010.5742192","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742192","url":null,"abstract":"In this paper, a new technique is presented to enhance the blurred images obtained from retinal imaging. One of the main steps in inspecting the eye (especially the deeper image of retina) is to look into the eye using a slit-lamp apparatus that shines a monochromatic light on to the retinal surface and captures the reflection in the camera as the retinal image. While most of the cases, the image produced is quite clean and easily used by the ophthalmologists, there are many cases in which these images come out to be very blurred due to the disease in the eye such a cataract etc… in such cases, having an enhanced image can enable the doctors to start the appropriate treatment for the underlying disease. The proposed technique utilizes the Blind Deconvolution approach using Maximum Likelihood Estimation approach. Further post-processing steps have been proposed as well to extract specific regions from the image automatically to assist the doctors in visualizing these regions related to very specific diseases. The post-processing steps include Image color space conversions, thresholding, Region Growing, and Edge detection.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127349997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Photoplethysmogram signal variability and repeatability assessment","authors":"K. Chellappan","doi":"10.1109/IECBES.2010.5742244","DOIUrl":"https://doi.org/10.1109/IECBES.2010.5742244","url":null,"abstract":"A clinically practiced low cost, non-invasive physiological signal recorder, Photoplethysmogram (PPG) is a blood volume change monitoring method. Various parameters are analysed and used in establishing the PPG usage in physiological health monitoring. Currently our research team is in the pathway of establishing single pulse utilization in formulating PPG fitness index for cardiovascular risk assessment. Even though PPG is widely used by medical practioners and researchers there are many unknown issues concerning this method are yet to be explored. Pulses variability in a single recording session and repeatability of the PPG recording in health monitoring are being unexplored aspects. In our study we recorded PPG signals (from the finger) four times in resting conditions in the interval of 30 minutes. Two different evaluations were approached: (1) variability between pulses in single recording; (2) repeatability in one individual PPG signal in different recording (30 minutes apart). This study has produced repeatability coefficient, CR = 93.22±1.18 and variability coefficient, CV = 6.18 ± 1.51, which is a strong indicator for PPG establishment as a reliable physiological assessment method. The result suggested that single pulse quantification is suitable to be considered as a parameter in PPG assessment.","PeriodicalId":241343,"journal":{"name":"2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES)","volume":"83 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120904116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}