Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367631
I. Kovacic, M. Zukovic, M. Cartmell
This paper offers new results from a study of mixed-mode oscillations, with application to both neural and mechanical systems. It is initially shown that the Fitzhugh-Nagumo model, which is itself a simplification of the previous electrophysiological model of Hodgkin and Huxley, has a direct analogue in the form of the model for a bistable spring mass system. This system is then shown to exhibit three qualitatively different motions and the design parameter space for the oscillator is examined in order to define conditions for these and for mixed-mode oscillations. The paper concludes with a conjecture that an autonomous system of this form can display some of the dynamic characteristics of the autonomous van der Pol oscillator, and one example of this equivalence is examined numerically.
{"title":"Mixed-mode oscillations: From neural phenomena to mechanical modelling","authors":"I. Kovacic, M. Zukovic, M. Cartmell","doi":"10.1109/BIBE.2015.7367631","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367631","url":null,"abstract":"This paper offers new results from a study of mixed-mode oscillations, with application to both neural and mechanical systems. It is initially shown that the Fitzhugh-Nagumo model, which is itself a simplification of the previous electrophysiological model of Hodgkin and Huxley, has a direct analogue in the form of the model for a bistable spring mass system. This system is then shown to exhibit three qualitatively different motions and the design parameter space for the oscillator is examined in order to define conditions for these and for mixed-mode oscillations. The paper concludes with a conjecture that an autonomous system of this form can display some of the dynamic characteristics of the autonomous van der Pol oscillator, and one example of this equivalence is examined numerically.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126397685","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367672
N. Mijailovic, R. Radakovic, A. Peulić, I. Milankovic, N. Filipovic
In this paper the methodology of vertical jump analysis is presented. Measured results of vertical force during jump are presented. Six subjects (members of "Red star" football club) perform different types of jump (flywheel jump, jump without flywheel, and jump with landing on the left and right foot while vertical ground reaction force is measured using a force plate. One axial load cell force sensor is also used. The measure value of force and position of a body part is used together with finite element method simulation in order to obtain von Mises stress distribution on the tibia, femur and cartilage in the knee joint. The average value of von Mises stress has a significant impact on the injuries and condition of the knee cartilage.
{"title":"Using force plate, computer simulation and image alignment in jumping analysis","authors":"N. Mijailovic, R. Radakovic, A. Peulić, I. Milankovic, N. Filipovic","doi":"10.1109/BIBE.2015.7367672","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367672","url":null,"abstract":"In this paper the methodology of vertical jump analysis is presented. Measured results of vertical force during jump are presented. Six subjects (members of \"Red star\" football club) perform different types of jump (flywheel jump, jump without flywheel, and jump with landing on the left and right foot while vertical ground reaction force is measured using a force plate. One axial load cell force sensor is also used. The measure value of force and position of a body part is used together with finite element method simulation in order to obtain von Mises stress distribution on the tibia, femur and cartilage in the knee joint. The average value of von Mises stress has a significant impact on the injuries and condition of the knee cartilage.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128796840","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367714
E. Konstantinidis, P. Bamidis
Gait analysis is nowadays considered, as a promising contributor towards early detection of cognitive and physical status deterioration when it comes to elderly people. However, the majority of recent efforts on indoor gait analysis methodologies are limited as they only exploit the average walking speed. Applying density based clustering algorithms on indoor location datasets could accelerate context awareness on gait analysis and consequently augment information quality with regard to underlying gait disorders. This work presents the application of DBScan, a well-known algorithm for knowledge discovery, on indoor Kinect location datasets collected in the Active and Healthy Aging Living Lab in the Lab of Medical Physics of the Aristotle University of Thessaloniki. The aim of the paper is to provide evidence that such an approach could effectively discriminate indoor activity High Density Regions which may subsequently be transferred to datasets originated from seniors' real homes in the light of context aware gait analysis.
{"title":"Density based clustering on indoor kinect location tracking: A new way to exploit active and healthy aging living lab datasets","authors":"E. Konstantinidis, P. Bamidis","doi":"10.1109/BIBE.2015.7367714","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367714","url":null,"abstract":"Gait analysis is nowadays considered, as a promising contributor towards early detection of cognitive and physical status deterioration when it comes to elderly people. However, the majority of recent efforts on indoor gait analysis methodologies are limited as they only exploit the average walking speed. Applying density based clustering algorithms on indoor location datasets could accelerate context awareness on gait analysis and consequently augment information quality with regard to underlying gait disorders. This work presents the application of DBScan, a well-known algorithm for knowledge discovery, on indoor Kinect location datasets collected in the Active and Healthy Aging Living Lab in the Lab of Medical Physics of the Aristotle University of Thessaloniki. The aim of the paper is to provide evidence that such an approach could effectively discriminate indoor activity High Density Regions which may subsequently be transferred to datasets originated from seniors' real homes in the light of context aware gait analysis.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132841691","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367642
A. Jawwad, Hossam H. Abolfotuh, Bassem A. Abdullah, Hani M. K. Mahdi, S. Eldawlatly
Restoring vision is no longer impossible as a result of recent advances in neural interfaces. Successful demonstrations of retinal implants motivate the development of more effective visual prostheses. The thalamic Lateral Geniculate Nucleus (LGN) is one potential deep-brain interfacing site for visual prostheses. A main challenge in developing thalamic as well as other visual prostheses is optimizing the parameters of electrical stimulation. This paper introduces a Kalman-based optimal encoder whose function is to determine the optimal electrical stimulation parameters required to induce a certain visual sensation. The performance of the proposed approach is demonstrated using a probabilistic model of LGN neurons. Results demonstrate a significant similarity between neuronal responses obtained using electrical stimulation and the responses obtained using the corresponding visual stimuli with a mean correlation of 0.62 (P <; 0.01, n = 54). These results indicate the efficacy of the proposed optimal encoder in driving LGN neurons to induce visual sensations.
由于神经接口的最新进展，恢复视力不再是不可能的。视网膜植入物的成功演示激发了更有效的视觉假体的发展。丘脑外侧膝状核(LGN)是一种潜在的视觉假体脑深部界面部位。开发丘脑和其他视觉假体的主要挑战是优化电刺激参数。本文介绍了一种基于卡尔曼的最优编码器，其功能是确定引起某种视觉感觉所需的最优电刺激参数。利用LGN神经元的概率模型证明了该方法的性能。结果表明，使用电刺激获得的神经元反应与使用相应的视觉刺激获得的神经元反应之间存在显著的相似性，平均相关性为0.62 (P <;0.01, n = 54)。这些结果表明所提出的最优编码器在驱动LGN神经元诱导视觉感觉方面的有效性。
{"title":"A Kalman-based encoder for electrical stimulation modulation in a thalamic network model","authors":"A. Jawwad, Hossam H. Abolfotuh, Bassem A. Abdullah, Hani M. K. Mahdi, S. Eldawlatly","doi":"10.1109/BIBE.2015.7367642","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367642","url":null,"abstract":"Restoring vision is no longer impossible as a result of recent advances in neural interfaces. Successful demonstrations of retinal implants motivate the development of more effective visual prostheses. The thalamic Lateral Geniculate Nucleus (LGN) is one potential deep-brain interfacing site for visual prostheses. A main challenge in developing thalamic as well as other visual prostheses is optimizing the parameters of electrical stimulation. This paper introduces a Kalman-based optimal encoder whose function is to determine the optimal electrical stimulation parameters required to induce a certain visual sensation. The performance of the proposed approach is demonstrated using a probabilistic model of LGN neurons. Results demonstrate a significant similarity between neuronal responses obtained using electrical stimulation and the responses obtained using the corresponding visual stimuli with a mean correlation of 0.62 (P <; 0.01, n = 54). These results indicate the efficacy of the proposed optimal encoder in driving LGN neurons to induce visual sensations.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116688749","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367655
S. Starcevic, N. Filipovic, N. Jagic, Nikola Jankovic, L. Velicki
The most common type of heart disease that affects millions of people worldwide is coronary heart disease (coronary artery disease). It is caused by a narrowing or blocking of the arteries due to plaque which restricts blood flow, and reduces the amount of oxygen to the heart [1]. Several tools are used that aid physicians in the treatment of the disease. Angiogram, which represents an X-ray examination of the blood vessels in the heart, is traditional tool. A fractional flow reserve (FFR) indicates the severity of blood flow blockages in the coronary arteries and allows physicians to identify which specific lesion or lesions are responsible for patient ischemia. FFR is measured by a pressure sensor guidewire [2]. In this paper, the mathematical model for measuring FFR is derived. This model helps to measure values of FFR, by noninvasive methods, only by using reconstructed geometry of coronary arteries with stenosis.
{"title":"Fractional flow reserve: A predictive model with reconstructed geometry of coronary arteries","authors":"S. Starcevic, N. Filipovic, N. Jagic, Nikola Jankovic, L. Velicki","doi":"10.1109/BIBE.2015.7367655","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367655","url":null,"abstract":"The most common type of heart disease that affects millions of people worldwide is coronary heart disease (coronary artery disease). It is caused by a narrowing or blocking of the arteries due to plaque which restricts blood flow, and reduces the amount of oxygen to the heart [1]. Several tools are used that aid physicians in the treatment of the disease. Angiogram, which represents an X-ray examination of the blood vessels in the heart, is traditional tool. A fractional flow reserve (FFR) indicates the severity of blood flow blockages in the coronary arteries and allows physicians to identify which specific lesion or lesions are responsible for patient ischemia. FFR is measured by a pressure sensor guidewire [2]. In this paper, the mathematical model for measuring FFR is derived. This model helps to measure values of FFR, by noninvasive methods, only by using reconstructed geometry of coronary arteries with stenosis.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134060207","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367702
H. Sharma, N. Zerbe, I. Klempert, Sebastian Lohmann, B. Lindequist, O. Hellwich, P. Hufnagl
Automatic detection of necrosis in histological images is an interesting problem of digital pathology that needs to be addressed. Determination of presence and extent of necrosis can provide useful information for disease diagnosis and prognosis, and the detected necrotic regions can also be excluded before analyzing the remaining living tissue. This paper describes a novel appearance-based method to detect tumor necrosis in histopathogical whole slide images. Studies are performed on heterogeneous microscopic images of gastric cancer containing tissue regions with variation in malignancy level and stain intensity. Textural image features are extracted from image patches to efficiently represent necrotic appearance in the tissue and machine learning is performed using support vector machines followed by discriminative thresholding for our complex datasets. The classification results are quantitatively evaluated for different image patch sizes using two cross validation approaches namely three-fold and leave one out cross validation, and the best average cross validation rate of 85.31% is achieved for the most suitable patch size. Therefore, the proposed method is a promising tool to detect necrosis in heterogeneous whole slide images, showing its robustness to varying visual appearances.
{"title":"Appearance-based necrosis detection using textural features and SVM with discriminative thresholding in histopathological whole slide images","authors":"H. Sharma, N. Zerbe, I. Klempert, Sebastian Lohmann, B. Lindequist, O. Hellwich, P. Hufnagl","doi":"10.1109/BIBE.2015.7367702","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367702","url":null,"abstract":"Automatic detection of necrosis in histological images is an interesting problem of digital pathology that needs to be addressed. Determination of presence and extent of necrosis can provide useful information for disease diagnosis and prognosis, and the detected necrotic regions can also be excluded before analyzing the remaining living tissue. This paper describes a novel appearance-based method to detect tumor necrosis in histopathogical whole slide images. Studies are performed on heterogeneous microscopic images of gastric cancer containing tissue regions with variation in malignancy level and stain intensity. Textural image features are extracted from image patches to efficiently represent necrotic appearance in the tissue and machine learning is performed using support vector machines followed by discriminative thresholding for our complex datasets. The classification results are quantitatively evaluated for different image patch sizes using two cross validation approaches namely three-fold and leave one out cross validation, and the best average cross validation rate of 85.31% is achieved for the most suitable patch size. Therefore, the proposed method is a promising tool to detect necrosis in heterogeneous whole slide images, showing its robustness to varying visual appearances.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131950434","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367712
Keisuke Tsunoda, Akihiro Chiba, H. Chigira, Tetsuya Ura, Osamu Mizuno
This paper presents a low-invasive framework for estimating changes in a cognitive performance using heart rate variability (HRV). Although HRV is a common physiological indicator of autonomous nerve activity or central nervous fatigue, there are individual differences in the relationship between HRV and such internal state. The new framework enables an estimation model to be determined using the HRV characteristics of individuals performing tasks through cognitive efforts. They also enable users working in a chair to have their changes in the cognitive performance estimated without interrupting their work or having to use a lot of devices as most previous methods require. Experimental results show the framework can estimate mental fatigue; defined based on cognitive performance, using HRV as the same level as the previous work did using higher-invasive method(using multi-channel electroencephalogram (EEG) sensor or multiple vital sensors). It can also estimate changes in a cognitive performance for most of subjects, and one of our proposed method in the framework realizes more effective and useful estimation than the others. It therefore has the potential to help managerial personnel in making performance change reports for their workers, suggesting reasons for changes in the performance, and urging them to change their working styles using HRV.
{"title":"Estimating changes in a cognitive performance using heart rate variability","authors":"Keisuke Tsunoda, Akihiro Chiba, H. Chigira, Tetsuya Ura, Osamu Mizuno","doi":"10.1109/BIBE.2015.7367712","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367712","url":null,"abstract":"This paper presents a low-invasive framework for estimating changes in a cognitive performance using heart rate variability (HRV). Although HRV is a common physiological indicator of autonomous nerve activity or central nervous fatigue, there are individual differences in the relationship between HRV and such internal state. The new framework enables an estimation model to be determined using the HRV characteristics of individuals performing tasks through cognitive efforts. They also enable users working in a chair to have their changes in the cognitive performance estimated without interrupting their work or having to use a lot of devices as most previous methods require. Experimental results show the framework can estimate mental fatigue; defined based on cognitive performance, using HRV as the same level as the previous work did using higher-invasive method(using multi-channel electroencephalogram (EEG) sensor or multiple vital sensors). It can also estimate changes in a cognitive performance for most of subjects, and one of our proposed method in the framework realizes more effective and useful estimation than the others. It therefore has the potential to help managerial personnel in making performance change reports for their workers, suggesting reasons for changes in the performance, and urging them to change their working styles using HRV.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"147 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113953828","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367683
G. Ni, S. Elliott
The reasons for hearing loss are complex and currently the mechanics are not entirely clear. Outer hair cell (OHC) loss is believed to play an important role. Experimental observations shown that damage on OHCs due to ototoxic acid starts from the outermost row to the innermost row, whereas, loss of OHCs due to intense noise exposure occurs from the innermost row to the outermost row. Inspired by these experiments, this study employs the finite element method to develop a detailed model of a slice of the human cochlea including cochlear fine structures. OHC motility is implemented by applying forces at the two ends of the OHCs in response to stereocilia deflection, which are believed to be a key process in cochlear amplification. In this way, the effects of a loss of OHCs due to either intense noise exposure or ototoxic acid can be studied by suppressing forces on individual OHCs. Change of cochlear mechanical amplification and vibration patterns inside the organ of Corti due to different hearing loss mechanisms can thus be predicted.
{"title":"Change of cochlear micromechanics due to different types of hearing loss","authors":"G. Ni, S. Elliott","doi":"10.1109/BIBE.2015.7367683","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367683","url":null,"abstract":"The reasons for hearing loss are complex and currently the mechanics are not entirely clear. Outer hair cell (OHC) loss is believed to play an important role. Experimental observations shown that damage on OHCs due to ototoxic acid starts from the outermost row to the innermost row, whereas, loss of OHCs due to intense noise exposure occurs from the innermost row to the outermost row. Inspired by these experiments, this study employs the finite element method to develop a detailed model of a slice of the human cochlea including cochlear fine structures. OHC motility is implemented by applying forces at the two ends of the OHCs in response to stereocilia deflection, which are believed to be a key process in cochlear amplification. In this way, the effects of a loss of OHCs due to either intense noise exposure or ototoxic acid can be studied by suppressing forces on individual OHCs. Change of cochlear mechanical amplification and vibration patterns inside the organ of Corti due to different hearing loss mechanisms can thus be predicted.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115938930","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367694
Ferdi Sarac, Volkan Uslan, H. Seker, A. Bouridane
Computational methods such as clustering, classification and regression methods can be applied in immunoin-formatics to construct predictive models to reveal relationships between antibody features and their functional outcomes. This paper studies the effect of antibody features and the functional outcome obtained on RV144 vaccine recipients. The RV144 vaccine data set contains 100 data samples in which 20 of them are the placebo samples and 80 of them are the vaccine injected samples. Each data sample has twenty antibody features that consist of features related to IgG subclass and antigen specificity. Unlike semi-supervised and supervised feature selection methods, unsupervised feature selection methods provide unbiased approach as they are not dependent to response variable. In this paper, four different unsupervised feature selection methods are used in order to reveal the discriminating antibody features. Then, the support vector based methods are used in order to predict natural killer (NK) cell cytokine release effect. The results yield a high correlation coefficient as much as 0.59 and 0.72 for the support vector based regression (SVR) and classification (SVM) predictive models, respectively.
{"title":"Exploration of unsupervised feature selection methods in relation to the prediction of cytokine release effect correlated to antibody features in RV144 vaccines","authors":"Ferdi Sarac, Volkan Uslan, H. Seker, A. Bouridane","doi":"10.1109/BIBE.2015.7367694","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367694","url":null,"abstract":"Computational methods such as clustering, classification and regression methods can be applied in immunoin-formatics to construct predictive models to reveal relationships between antibody features and their functional outcomes. This paper studies the effect of antibody features and the functional outcome obtained on RV144 vaccine recipients. The RV144 vaccine data set contains 100 data samples in which 20 of them are the placebo samples and 80 of them are the vaccine injected samples. Each data sample has twenty antibody features that consist of features related to IgG subclass and antigen specificity. Unlike semi-supervised and supervised feature selection methods, unsupervised feature selection methods provide unbiased approach as they are not dependent to response variable. In this paper, four different unsupervised feature selection methods are used in order to reveal the discriminating antibody features. Then, the support vector based methods are used in order to predict natural killer (NK) cell cytokine release effect. The results yield a high correlation coefficient as much as 0.59 and 0.72 for the support vector based regression (SVR) and classification (SVM) predictive models, respectively.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117128645","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}
Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367727
Vladimir Zlokolica, L. Velicki, Bojan Banjac, M. Janev, Lidija Krstanović, N. Ralević, R. Obradović, B. Mihajlovic
3D heart registration has become an important issue in cardio-vascular diagnosis and treatment. This is mainly due to more accessible medical imaging technologies that can nowadays provide high precision imaging data at relatively lower cost. One of the important features of the heart that has recently drawn attention is epicardial fat (surrounds the heart), which according to some preliminary studies can indicate risk level of various cardiovascular diseases. As such, 2D/3D registration of epicardial fat, through automatic or semi-automatic detection/segmentation, is considered as valuable task for medical doctors (MDs) to include as additional feature within the already existing software for medical imaging and visualization. Although MDs can visually detect regions of epicardial fat from the image slices manually, i.e., subjectively, it is usually time consuming and error prone task. Moreover, due to considerable amount of parameters used for image pre-processing, which can strongly influence visibility of certain features in the image by MD, it often happens that some important features are missed. Consequently, the most preferable solution is the one that combines objective and subjective (by MD) description of particular image feature (in this example epicardial fat) and then subsequently employs semi-automatic segmentation approach, where in execution stage MD would only roughly indicate particular region of interest (ROI), based on which designed algorithm would process the whole heart volume and compute the 3D volume of the heart and epicardial fat. In this paper, we aim at optimizing and enhancing previously developed algorithm for 2D fat segmentation based on (i) pre-knowledge about epicardial structure (provided by the MDs) and (ii) subjective and objective metric correspondence. Based on the 2D segmentation method we compute the 3D volume in order to perform 3D registration. This new optimized approach is shown to considerably improve the accuracy of the epicardial fat registration using CT images.
{"title":"3D epicardial fat registration optimization based on structural prior knowledge and subjective-objective correspondence","authors":"Vladimir Zlokolica, L. Velicki, Bojan Banjac, M. Janev, Lidija Krstanović, N. Ralević, R. Obradović, B. Mihajlovic","doi":"10.1109/BIBE.2015.7367727","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367727","url":null,"abstract":"3D heart registration has become an important issue in cardio-vascular diagnosis and treatment. This is mainly due to more accessible medical imaging technologies that can nowadays provide high precision imaging data at relatively lower cost. One of the important features of the heart that has recently drawn attention is epicardial fat (surrounds the heart), which according to some preliminary studies can indicate risk level of various cardiovascular diseases. As such, 2D/3D registration of epicardial fat, through automatic or semi-automatic detection/segmentation, is considered as valuable task for medical doctors (MDs) to include as additional feature within the already existing software for medical imaging and visualization. Although MDs can visually detect regions of epicardial fat from the image slices manually, i.e., subjectively, it is usually time consuming and error prone task. Moreover, due to considerable amount of parameters used for image pre-processing, which can strongly influence visibility of certain features in the image by MD, it often happens that some important features are missed. Consequently, the most preferable solution is the one that combines objective and subjective (by MD) description of particular image feature (in this example epicardial fat) and then subsequently employs semi-automatic segmentation approach, where in execution stage MD would only roughly indicate particular region of interest (ROI), based on which designed algorithm would process the whole heart volume and compute the 3D volume of the heart and epicardial fat. In this paper, we aim at optimizing and enhancing previously developed algorithm for 2D fat segmentation based on (i) pre-knowledge about epicardial structure (provided by the MDs) and (ii) subjective and objective metric correspondence. Based on the 2D segmentation method we compute the 3D volume in order to perform 3D registration. This new optimized approach is shown to considerably improve the accuracy of the epicardial fat registration using CT images.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115419921","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：