Understanding the rules of facial beauty is important for esthetic plastic surgery. Averageness and ideal proportions are the most investigated rules. In this paper, we integrate the findings on these two aspects to identify race invariant ideal facial proportions. Extensive research on the averageness hypothesis have verified that average faces are beautiful, which provides an objective way to generate representatives of beautiful faces. In order to ensure ethnic variety, 148 average faces from 61 countries/regions around the world have been collected to build the data set. 26 putative ratio rules, including golden ratio, neoclassical canons, etc., are collected to construct a candidate feature set. We first perform k-means clustering and then examine the 26 rules with respect to accuracy and universality on both the entire average face data set and individual clusters. The results show that: 1) the clustering result is consistent with the anthropologic divisions, 2) the top universal ratio features are consistent across different clusters, and 3) the accuracy of putative ratio rules can be improved by using data driven ideal values. The validity of the corrected ideal facial proportions has been verified on both synthesized faces and well-known beautiful faces in the real world.
{"title":"Evaluation of the Putative Ratio Rules for Facial Beauty Indexing","authors":"Fangmei Chen, David Zhang","doi":"10.1109/ICMB.2014.38","DOIUrl":"https://doi.org/10.1109/ICMB.2014.38","url":null,"abstract":"Understanding the rules of facial beauty is important for esthetic plastic surgery. Averageness and ideal proportions are the most investigated rules. In this paper, we integrate the findings on these two aspects to identify race invariant ideal facial proportions. Extensive research on the averageness hypothesis have verified that average faces are beautiful, which provides an objective way to generate representatives of beautiful faces. In order to ensure ethnic variety, 148 average faces from 61 countries/regions around the world have been collected to build the data set. 26 putative ratio rules, including golden ratio, neoclassical canons, etc., are collected to construct a candidate feature set. We first perform k-means clustering and then examine the 26 rules with respect to accuracy and universality on both the entire average face data set and individual clusters. The results show that: 1) the clustering result is consistent with the anthropologic divisions, 2) the top universal ratio features are consistent across different clusters, and 3) the accuracy of putative ratio rules can be improved by using data driven ideal values. The validity of the corrected ideal facial proportions has been verified on both synthesized faces and well-known beautiful faces in the real world.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131757905","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}
Rectus femoris (RF) has long been known to be susceptible to injuries, especially in population occupationally required to stretch quadriceps forcefully. Among various RF injuries, those involve strains about the central tendon (CT) of RF are found to cost longer recovery interval than other sites. To look into the contraction pattern of RF quantitatively, we start with sonography study of CT during isometric knee extensions. Nine healthy male adults participated the experiments. The tilt angle of CT (TACT) was calculated manually. Inter-frame velocity field were computed using a Primal-Dual method. Captured at 25 Hz, totally 1920 sonograms were included in the experiments. TACT and the averaged velocity (AV) demonstrated interesting patterns in ramp increasing/ decreasing phases, compared to piece-wise quasi-linear torque signal. TACT appears sensitive to knee extension during the starting and ending phases only, which imply that during the starting and ending of torque output, CT experiences more dramatic changes of force from it two sides. The preliminary results of TACT and AV could be helpful for understanding of RF injuries during fast quadriceps stretch.
{"title":"Characterization of the Rectus Femoris Activation Patterns during Isometric Contraction in Transverse Plane: An Ultrasonography Study","authors":"Jizhou Li, Yaoqin Xie, Lei Wang, Yongjin Zhou","doi":"10.1109/ICMB.2014.27","DOIUrl":"https://doi.org/10.1109/ICMB.2014.27","url":null,"abstract":"Rectus femoris (RF) has long been known to be susceptible to injuries, especially in population occupationally required to stretch quadriceps forcefully. Among various RF injuries, those involve strains about the central tendon (CT) of RF are found to cost longer recovery interval than other sites. To look into the contraction pattern of RF quantitatively, we start with sonography study of CT during isometric knee extensions. Nine healthy male adults participated the experiments. The tilt angle of CT (TACT) was calculated manually. Inter-frame velocity field were computed using a Primal-Dual method. Captured at 25 Hz, totally 1920 sonograms were included in the experiments. TACT and the averaged velocity (AV) demonstrated interesting patterns in ramp increasing/ decreasing phases, compared to piece-wise quasi-linear torque signal. TACT appears sensitive to knee extension during the starting and ending phases only, which imply that during the starting and ending of torque output, CT experiences more dramatic changes of force from it two sides. The preliminary results of TACT and AV could be helpful for understanding of RF injuries during fast quadriceps stretch.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127651113","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}
The electroencephalogram (EEG) signals are commonly used signals for detection of epileptic seizures. In this paper, we present a new method for classification of two classes of EEG signals namely focal and non-focal EEG signals. The proposed method uses the sample entropies and variances of the intrinsic mode functions (IMFs) obtained by empirical mode decomposition (EMD) of EEG signals. The average sample entropy (ASE) of IMFs and average variance of instantaneous frequencies (AVIF) of IMFs for separate EEG signals have been used as features for classification of focal and non-focal EEG signals. These two parameters have been used as an input feature set to the least square support vector machine (LS-SVM) classifier. The experimental results for various IMFs of focal and non-focal EEG signals have been included to show the effectiveness of the proposed method. The proposed method has provided promising classification accuracy for classification of focal and non-focal seizure EEG signals when radial basis function (RBF) has been employed as a kernel with LS-SVM classifier.
{"title":"Empirical Mode Decomposition Based Classification of Focal and Non-focal Seizure EEG Signals","authors":"Rajeev Sharma, R. B. Pachori, Shreya Gautam","doi":"10.1109/ICMB.2014.31","DOIUrl":"https://doi.org/10.1109/ICMB.2014.31","url":null,"abstract":"The electroencephalogram (EEG) signals are commonly used signals for detection of epileptic seizures. In this paper, we present a new method for classification of two classes of EEG signals namely focal and non-focal EEG signals. The proposed method uses the sample entropies and variances of the intrinsic mode functions (IMFs) obtained by empirical mode decomposition (EMD) of EEG signals. The average sample entropy (ASE) of IMFs and average variance of instantaneous frequencies (AVIF) of IMFs for separate EEG signals have been used as features for classification of focal and non-focal EEG signals. These two parameters have been used as an input feature set to the least square support vector machine (LS-SVM) classifier. The experimental results for various IMFs of focal and non-focal EEG signals have been included to show the effectiveness of the proposed method. The proposed method has provided promising classification accuracy for classification of focal and non-focal seizure EEG signals when radial basis function (RBF) has been employed as a kernel with LS-SVM classifier.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133282194","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}
Guangming Lu, Zhixing Jiang, Liying Ye, Yaotian Huang
Wrist pulse contains important information about the health status of a person. The pathological changes of organ could be perceived by pulse-feeling which has been popular for thousands of years in China. However, the traditional Chinese medicine usually portrays the pulse types in a vague and general language, and the diagnoses from physicians often diverge greatly due to their subjective experience. Thus, the objectification of pulse diagnosis is imperative under the modern computer technology circumstance. This paper proposes a novel pulse feature extraction method based on improved Gaussian model, the experiments has been done on a dataset which is collected from 148 healthy persons and 288 patients by using the self-designed pulse collecting system, the results show that the method is efficient for diagnosis.
{"title":"Pulse Feature Extraction Based on Improved Gaussian Model","authors":"Guangming Lu, Zhixing Jiang, Liying Ye, Yaotian Huang","doi":"10.1109/ICMB.2014.23","DOIUrl":"https://doi.org/10.1109/ICMB.2014.23","url":null,"abstract":"Wrist pulse contains important information about the health status of a person. The pathological changes of organ could be perceived by pulse-feeling which has been popular for thousands of years in China. However, the traditional Chinese medicine usually portrays the pulse types in a vague and general language, and the diagnoses from physicians often diverge greatly due to their subjective experience. Thus, the objectification of pulse diagnosis is imperative under the modern computer technology circumstance. This paper proposes a novel pulse feature extraction method based on improved Gaussian model, the experiments has been done on a dataset which is collected from 148 healthy persons and 288 patients by using the self-designed pulse collecting system, the results show that the method is efficient for diagnosis.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123406136","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}
Humans have different react to the different the auditory inputs and have the capability to automatically differentiate them. But, people cannot feel auditory change when auditory brain areas are damaged, in this paper a novel evaluation method is proposed based on induced event-related coherence (ERCoh), which quantified auditory brain areas response during auditory input change. To investigate the functional coupling of brain areas under auditory stimuli, sixtyfour-channel scalp electroencephalogram (EEG) were recorded for 14 subjects who were asked to listen different sounds. ERCoh of the EEG different bands (δ, θ, α1, α2, β and y) was calculated and compared statistically among different stimuli. The results suggest that alpha frequency band may reflect the cerebral processing of auditory changes by showing differential ERCoh phenomena among standard-, deviantand novel-elicited responses. The bilateral hemisphere coherence of the standard stimulus is higher than the deviant and novel stimulus in alpha band. For each stimulus, the coherence difference was clearly larger in the right hemisphere, which is mainly involved in auditory perception. Furthermore, the right frontal and temporal ERCoh is brought about by an increase in auditory changes. Thus, induced ERCoh does not only appear to be generated by distinct neurophysiological mechanisms but also differ with regard to their functional significance to evaluation auditory brain areas impairment. In addition, EEG classification task is accomplished based on ERCoh for potential BCI applications, which provide potentially the only communication channel for severely disabled people who are otherwise unable to articulate their thoughts and needs, Therefore, ERCoh is a very promising approach in the future.
{"title":"Induced Event-Related Coherence Measures during Auditory Change Detection","authors":"Fang Chunying, Li Haifeng, Ma Lin, Jiang Bing","doi":"10.1109/ICMB.2014.28","DOIUrl":"https://doi.org/10.1109/ICMB.2014.28","url":null,"abstract":"Humans have different react to the different the auditory inputs and have the capability to automatically differentiate them. But, people cannot feel auditory change when auditory brain areas are damaged, in this paper a novel evaluation method is proposed based on induced event-related coherence (ERCoh), which quantified auditory brain areas response during auditory input change. To investigate the functional coupling of brain areas under auditory stimuli, sixtyfour-channel scalp electroencephalogram (EEG) were recorded for 14 subjects who were asked to listen different sounds. ERCoh of the EEG different bands (δ, θ, α1, α2, β and y) was calculated and compared statistically among different stimuli. The results suggest that alpha frequency band may reflect the cerebral processing of auditory changes by showing differential ERCoh phenomena among standard-, deviantand novel-elicited responses. The bilateral hemisphere coherence of the standard stimulus is higher than the deviant and novel stimulus in alpha band. For each stimulus, the coherence difference was clearly larger in the right hemisphere, which is mainly involved in auditory perception. Furthermore, the right frontal and temporal ERCoh is brought about by an increase in auditory changes. Thus, induced ERCoh does not only appear to be generated by distinct neurophysiological mechanisms but also differ with regard to their functional significance to evaluation auditory brain areas impairment. In addition, EEG classification task is accomplished based on ERCoh for potential BCI applications, which provide potentially the only communication channel for severely disabled people who are otherwise unable to articulate their thoughts and needs, Therefore, ERCoh is a very promising approach in the future.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122781653","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}
Wrist pulse signal has been regarded as a physical health indicator for a long history in Traditional Chinese Medicine (TCM). The quantized pulse diagnosis by using the signal processing and pattern recognition technology is introduced to take over the traditional subjective judgments in recent years, and it's attracting more and more attention. However, the previous researches with pulse pre-processing mainly concentrate on the denoising and baseline wander correction procedure. The evaluation criterion isn't associated with the feature analysis, and the performance with shape classification doesn't give any contributions to the pulse diagnosis. Moreover, the signals are processed in a simulated environment by adding disturbance manually. In this paper, we propose a period segmentation method based on adaptive cascade thresholding and machine learning for extracting the information within single period. It's a novel pre-processing stage and the pulse data collected in real conditions for practical usage is analyzed. The experiments show that our method is significant in the pulse pre-processing stage and improves the accuracy for the disease classification between healthy subjects and diabetes.
{"title":"Period Segmentation for Wrist Pulse Signal Based on Adaptive Cascade Thresholding and Machine Learning","authors":"Dimin Wang, Guangming Lu","doi":"10.1109/ICMB.2014.18","DOIUrl":"https://doi.org/10.1109/ICMB.2014.18","url":null,"abstract":"Wrist pulse signal has been regarded as a physical health indicator for a long history in Traditional Chinese Medicine (TCM). The quantized pulse diagnosis by using the signal processing and pattern recognition technology is introduced to take over the traditional subjective judgments in recent years, and it's attracting more and more attention. However, the previous researches with pulse pre-processing mainly concentrate on the denoising and baseline wander correction procedure. The evaluation criterion isn't associated with the feature analysis, and the performance with shape classification doesn't give any contributions to the pulse diagnosis. Moreover, the signals are processed in a simulated environment by adding disturbance manually. In this paper, we propose a period segmentation method based on adaptive cascade thresholding and machine learning for extracting the information within single period. It's a novel pre-processing stage and the pulse data collected in real conditions for practical usage is analyzed. The experiments show that our method is significant in the pulse pre-processing stage and improves the accuracy for the disease classification between healthy subjects and diabetes.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125294988","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}
Jiaqian Li, K. Tseng, Haiting Dong, Yifan Li, Ming Zhao, Mingyue Ding
Sperm morphology is an important diagnostic basis to identify if a sperm cell is healthy or not. This paper presents a method that using principal component analysis (PCA) to extract image features and k-nearest neighbor (KNN) algorithm to diagnose sperm health. We first accurately locate the position of sperm in the microscope images, and segment some small sperm division with a fixed size. Then some of divisions are selected as the training set to classify the remaining small sperm divisions. In this experiment, while the diagnosis accuracy depends on the training set, we have already selected a better training set and obtained a good performance with 87.53% compared with other feature extraction methods such as scale-invariant feature transform (SIFT) and other classifier such as back propagation neural network (BPNN).
{"title":"Human Sperm Health Diagnosis with Principal Component Analysis and K-nearest Neighbor Algorithm","authors":"Jiaqian Li, K. Tseng, Haiting Dong, Yifan Li, Ming Zhao, Mingyue Ding","doi":"10.1109/ICMB.2014.26","DOIUrl":"https://doi.org/10.1109/ICMB.2014.26","url":null,"abstract":"Sperm morphology is an important diagnostic basis to identify if a sperm cell is healthy or not. This paper presents a method that using principal component analysis (PCA) to extract image features and k-nearest neighbor (KNN) algorithm to diagnose sperm health. We first accurately locate the position of sperm in the microscope images, and segment some small sperm division with a fixed size. Then some of divisions are selected as the training set to classify the remaining small sperm divisions. In this experiment, while the diagnosis accuracy depends on the training set, we have already selected a better training set and obtained a good performance with 87.53% compared with other feature extraction methods such as scale-invariant feature transform (SIFT) and other classifier such as back propagation neural network (BPNN).","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126934912","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}
Tongue diagnosis is an important method in TCM. Teeth marks are objective indexes of the diagnosis of qi deficiency. One method on teeth marks recognition is based on detecting the size of concave regions. The concave regions are caused by the bending of the tip and margin of the tongue, and the lip shades are easy to be misjudged into teeth marks. The pose and asymmetry of the tongue body also affect the judgment. Another method to identify teeth marks is to calculate concavity and convexity of the margin of the tongue. The narrow and long concave regions whose concavity is not deep are easy to be misjudged into teeth marks. Accordingly, this paper proposed a new method to extract teeth marks by calculating the slope of the margin of the tongue and the length and degree of the concave regions. Experimental results demonstrate the effectiveness of the method.
{"title":"Research on Teeth Marks Recognition in Tongue Image","authors":"Hong Wang, Xinfeng Zhang, Yiheng Cai","doi":"10.1109/ICMB.2014.21","DOIUrl":"https://doi.org/10.1109/ICMB.2014.21","url":null,"abstract":"Tongue diagnosis is an important method in TCM. Teeth marks are objective indexes of the diagnosis of qi deficiency. One method on teeth marks recognition is based on detecting the size of concave regions. The concave regions are caused by the bending of the tip and margin of the tongue, and the lip shades are easy to be misjudged into teeth marks. The pose and asymmetry of the tongue body also affect the judgment. Another method to identify teeth marks is to calculate concavity and convexity of the margin of the tongue. The narrow and long concave regions whose concavity is not deep are easy to be misjudged into teeth marks. Accordingly, this paper proposed a new method to extract teeth marks by calculating the slope of the margin of the tongue and the length and degree of the concave regions. Experimental results demonstrate the effectiveness of the method.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130451069","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}
Haoqian Wang, Chengli Du, Xingzheng Wang, Yongbing Zhang, Lei Zhang
Disparity estimation and mode decisions are key techniques in multi-view video coding (MVC) which could improve the compression efficiency when the computational complexity increasing greatly. Based on Kalman filtering, a novel fast disparity estimation and mode decision algorithm is presented in this paper. We firstly built a autoregressive (AR) model of disparity vectors on the basis of spatio-temporal correlation so as to achieve a preliminary result of disparity estimation. Furthermore, the Kalman filter is utilized to optimize and improve the estimation speed. Moreover, an effective reliability judgment method for mode prediction is presented, with which, a more precious mode prediction result can be obtained and the selected range of coding mode is effectively reduced to achieve low complexity mode decision. The experimental results show that the computational complexity is significantly reduced while the compression efficiency is still maintained.
{"title":"Fast Disparity Estimation and Mode Decision for Multi-view Video Coding","authors":"Haoqian Wang, Chengli Du, Xingzheng Wang, Yongbing Zhang, Lei Zhang","doi":"10.1109/ICMB.2014.40","DOIUrl":"https://doi.org/10.1109/ICMB.2014.40","url":null,"abstract":"Disparity estimation and mode decisions are key techniques in multi-view video coding (MVC) which could improve the compression efficiency when the computational complexity increasing greatly. Based on Kalman filtering, a novel fast disparity estimation and mode decision algorithm is presented in this paper. We firstly built a autoregressive (AR) model of disparity vectors on the basis of spatio-temporal correlation so as to achieve a preliminary result of disparity estimation. Furthermore, the Kalman filter is utilized to optimize and improve the estimation speed. Moreover, an effective reliability judgment method for mode prediction is presented, with which, a more precious mode prediction result can be obtained and the selected range of coding mode is effectively reduced to achieve low complexity mode decision. The experimental results show that the computational complexity is significantly reduced while the compression efficiency is still maintained.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"203 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126036408","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}
Shear wave elstography based on acoustic radiation force is used for quantitative assessment of liver fibrosis in a rat model. The results show that the mean shear elasticity increases with the stage of liver fibrosis. The range of mean shear elasticity for all liver fibrosis stages is 1.25-3.17 kPa. The 95% confidence intervals of mean shear elasticity are overlapping for F1, F2, F3 and F4 stage. The results of ANOVA suggest that shear elasticity has significance difference between F0, F1 stage and F2, F3, F4 stage (P<;0.02), while shear elasticity has no significance between F0 and F1 stage (P=0.128), between F2, F3 and F4 stage (P>0.23). The AUC values of ROC curve of shear elasticity at METAVIR score threshold are 0.98 (≥F1), 0.95 (≥F2), 0.83(≥F3) and 0.83 (≥F4) respectively. The results suggest that shear wave elastography base on acoustic radiation force can be used potentially for early diagnosis and study of liver fibrosis.
{"title":"Quantitative Shear Elasticity Assessment of Liver Fibrosis in Rat Model with Shear Wave Elastography Base on Acoustic Radiation Force","authors":"Haoming Lin, Xin Chen, Yanrong Guo, Yuanyuan Shen, Siping Chen","doi":"10.1109/ICMB.2014.30","DOIUrl":"https://doi.org/10.1109/ICMB.2014.30","url":null,"abstract":"Shear wave elstography based on acoustic radiation force is used for quantitative assessment of liver fibrosis in a rat model. The results show that the mean shear elasticity increases with the stage of liver fibrosis. The range of mean shear elasticity for all liver fibrosis stages is 1.25-3.17 kPa. The 95% confidence intervals of mean shear elasticity are overlapping for F1, F2, F3 and F4 stage. The results of ANOVA suggest that shear elasticity has significance difference between F0, F1 stage and F2, F3, F4 stage (P<;0.02), while shear elasticity has no significance between F0 and F1 stage (P=0.128), between F2, F3 and F4 stage (P>0.23). The AUC values of ROC curve of shear elasticity at METAVIR score threshold are 0.98 (≥F1), 0.95 (≥F2), 0.83(≥F3) and 0.83 (≥F4) respectively. The results suggest that shear wave elastography base on acoustic radiation force can be used potentially for early diagnosis and study of liver fibrosis.","PeriodicalId":273636,"journal":{"name":"2014 International Conference on Medical Biometrics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126110240","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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