Pub Date : 2015-11-02DOI: 10.1109/BIBE.2015.7367738
Elnaz Pashaei, M. Ozen, N. Aydin
In this study, we propose a novel method for medical data classification, it is the integration of new heuristic algorithm that get inspired the black hole phenomenon called as Black Hole Algorithm (BHA) and decision tree (C4.5). To evaluate the effectiveness of our proposed method, it is implemented on 2 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. The results of BHA + C4.5 implementation are compared to seven well-known benchmark classification methods (support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Linear Discriminant Analysis (LDA), Self-Organizing Map and Naive Bayes). Repeated five-fold cross-validation method is used to justify the performance of classifiers. Two criteria are used for model evaluation. They are Matthews' Correlation Coefficient (MCC) and Accuracy. Experimental results show that our proposed method outperforms the other classification methods in MCC index and have higher accuracy after SVM and LDA classifiers.
{"title":"An application of black hole algorithm and decision tree for medical problem","authors":"Elnaz Pashaei, M. Ozen, N. Aydin","doi":"10.1109/BIBE.2015.7367738","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367738","url":null,"abstract":"In this study, we propose a novel method for medical data classification, it is the integration of new heuristic algorithm that get inspired the black hole phenomenon called as Black Hole Algorithm (BHA) and decision tree (C4.5). To evaluate the effectiveness of our proposed method, it is implemented on 2 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. The results of BHA + C4.5 implementation are compared to seven well-known benchmark classification methods (support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Linear Discriminant Analysis (LDA), Self-Organizing Map and Naive Bayes). Repeated five-fold cross-validation method is used to justify the performance of classifiers. Two criteria are used for model evaluation. They are Matthews' Correlation Coefficient (MCC) and Accuracy. Experimental results show that our proposed method outperforms the other classification methods in MCC index and have higher accuracy after SVM and LDA classifiers.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"7 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":"129499304","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.2015.7367706
E. Oczeretko, M. Borowska, E. Brzozowska, B. Pawliński, A. Borusiewicz, Z. Gajewski
The aim of this paper is to present the use of the Walsh-Hadamard transform in the analysis of electromygraphic signals representing the uterine contractile activity in pigs. The Fourier spectral analysis is widely used in many biomedical applications. However, for binary time series the Walsh-Hadamard transform based on square or rectangular waves with peaks of ±1 is more accurate. Dominant normalized sequency can serve as a parameter describing the biomedical signal, which may have diagnostic importance.
{"title":"Walsh-Hadamard spectral analysis of signals representing bioelectrical activity of the reproductive tract in pigs","authors":"E. Oczeretko, M. Borowska, E. Brzozowska, B. Pawliński, A. Borusiewicz, Z. Gajewski","doi":"10.1109/BIBE.2015.7367706","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367706","url":null,"abstract":"The aim of this paper is to present the use of the Walsh-Hadamard transform in the analysis of electromygraphic signals representing the uterine contractile activity in pigs. The Fourier spectral analysis is widely used in many biomedical applications. However, for binary time series the Walsh-Hadamard transform based on square or rectangular waves with peaks of ±1 is more accurate. Dominant normalized sequency can serve as a parameter describing the biomedical signal, which may have diagnostic importance.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 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":"128281206","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.2015.7367643
I. Šaveljić, O. Jovanikic, V. Isailović, N. Filipovic
The finite element method is increasingly used in the analysis of blood flow through blood vessels. Our work represents the research of blood flow through a cerebral aneurysm. The current work describes the blood flow in 4 patient-specific models of saccular aneurysms. They are located in the region of the anterior and posterior circulation of the circle of Willis. The unstructed grids are constructed from segmented images. Using three-dimensional continuity and momentum equations for unsteady laminar flow and realistic pulsatile flow conditions, we determined the wall shear stress (WSS), pressure and drag forces that acting on the wall of the blood vessel. It is known that wall shear stress (WSS) play an important role in initiation, growth and rupture of cerebral aneurysm, so, determination of the forces that acting in this region helps with understanding aneurysms better.
{"title":"Computational analysis of blood flow in cerebral aneurysms","authors":"I. Šaveljić, O. Jovanikic, V. Isailović, N. Filipovic","doi":"10.1109/BIBE.2015.7367643","DOIUrl":"https://doi.org/10.1109/BIBE.2015.7367643","url":null,"abstract":"The finite element method is increasingly used in the analysis of blood flow through blood vessels. Our work represents the research of blood flow through a cerebral aneurysm. The current work describes the blood flow in 4 patient-specific models of saccular aneurysms. They are located in the region of the anterior and posterior circulation of the circle of Willis. The unstructed grids are constructed from segmented images. Using three-dimensional continuity and momentum equations for unsteady laminar flow and realistic pulsatile flow conditions, we determined the wall shear stress (WSS), pressure and drag forces that acting on the wall of the blood vessel. It is known that wall shear stress (WSS) play an important role in initiation, growth and rupture of cerebral aneurysm, so, determination of the forces that acting in this region helps with understanding aneurysms better.","PeriodicalId":422807,"journal":{"name":"2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"85 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":"122866677","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.2015.7367631
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.2015.7367714
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.2015.7367655
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.2015.7367712
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}
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