Cuijuan Lou, J. Song, Liang Zhou, Yang Peng, Mingyue Ding, M. Yuchi
In linear array B-mode imaging, the zero-phase filtered delay multiply and sum beamforming (ZPF-DMAS) weighted by space-time smoothing coherence factor (StS-CF) has been proved to enhance the image contrast resolution than the traditional delay and sum method. However, the large number of virtual received signals may increase the computational cost of this method in ultrasound computed tomography (USCT) with ring array. Here, a method with less computation amount is proposed in USCT: the received signals are separated into different groups by their spatial lag; the groups form a new smaller size receive aperture; StS-CF is finally applied to the new receive aperture. CIRS model 055A is tested to compare the performances of the proposed method and ZPF-DMAS. The results show that the computational complexity has been reduced by N*B*D((M2–3M)/2+1) multiplications, supposing there are N ultrasound waves transmitted, B scan lines, D imaging points on each line and M-element receive aperture. StS-CF with a subarray size of L = 16 (far less than half the receive aperture) and P = 5 time samples gives the best result in USCT, which is different from that in linear array B-mode imaging. The proposed method can enhance contrast ratio about 6.3 dB and 5.7 dB for the cystic mass and dense mass than ZPF-DMAS, respectively.
{"title":"A Fast Contrast Improved Zero-Phase Filtered Delay Multiply and Sum in Ultrasound Computed Tomography","authors":"Cuijuan Lou, J. Song, Liang Zhou, Yang Peng, Mingyue Ding, M. Yuchi","doi":"10.1166/jmihi.2018.2566","DOIUrl":"https://doi.org/10.1166/jmihi.2018.2566","url":null,"abstract":"In linear array B-mode imaging, the zero-phase filtered delay multiply and sum beamforming (ZPF-DMAS) weighted by space-time smoothing coherence factor (StS-CF) has been proved to enhance the image contrast resolution than the traditional delay and sum method. However, the large number\u0000 of virtual received signals may increase the computational cost of this method in ultrasound computed tomography (USCT) with ring array. Here, a method with less computation amount is proposed in USCT: the received signals are separated into different groups by their spatial lag; the groups\u0000 form a new smaller size receive aperture; StS-CF is finally applied to the new receive aperture. CIRS model 055A is tested to compare the performances of the proposed method and ZPF-DMAS. The results show that the computational complexity has been reduced by N*B*D((M2–3M)/2+1)\u0000 multiplications, supposing there are N ultrasound waves transmitted, B scan lines, D imaging points on each line and M-element receive aperture. StS-CF with a subarray size of L = 16 (far less than half the receive aperture) and P = 5 time samples\u0000 gives the best result in USCT, which is different from that in linear array B-mode imaging. The proposed method can enhance contrast ratio about 6.3 dB and 5.7 dB for the cystic mass and dense mass than ZPF-DMAS, respectively.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"91 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84176710","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}
Cerebral Palsy (CP) is a non progressive neurological disorders commonly associated with a spectrum of developmental disabilities such as strabismus (misalignment of eye). In this study, by quantitatively assess the performance analysis of Visual Therapy Method used for CP children. By capturing the Eye Movement of 25 children with CP (aged 3–15 years) with relatively mild motor-impairment and also analyzed the performance of CP children periodically. The Eye image are captured through camera, this make the quick diagnosis and examination the periodical assessment of CP kids. By Visual Therapy Function the CP children vision improvement develops as fast as or even faster. Proposed area of the research segments the eye image into Pupil, Iris, Eyelids and Eye corner detection using different image processing algorithms as well as measure the deviation position of pupil for abnormal eyes taking the normal eye as the reference or threshold value. To further enhance these compare deviation Position of the normal and the abnormal eyes and to find the severity affection of abnormal eye and to measure the performance improvement achievement by Visual Therapy Method for CP rehabilitation. The improvement analyzed for CP kids were maintained and recorded for the periodical month of Initial, 6th and 12th month. As a result, the Physicians can use this report to guide the CP kids in Rehabilitation Center and also this image processing technique offers the greater flexibility for the prospective subjects of improvement in CP Rehabilitation by Visual Therapy Method. In this context, Image processing techniques are being recommended as a performance evaluation tool in children with CP and each of these processes are suggesting method for developing a more systematic understanding of Oculomotor abnormalities.
{"title":"Performance Evaluation of Visual Therapy Method Used for Cerebral Palsy Rehabilitation","authors":"P. Illavarason, AROKIA RENJIT J, P. M. Kumar","doi":"10.1166/JMIHI.2018.2515","DOIUrl":"https://doi.org/10.1166/JMIHI.2018.2515","url":null,"abstract":"Cerebral Palsy (CP) is a non progressive neurological disorders commonly associated with a spectrum of developmental disabilities such as strabismus (misalignment of eye). In this study, by quantitatively assess the performance analysis of Visual Therapy Method used for CP children.\u0000 By capturing the Eye Movement of 25 children with CP (aged 3–15 years) with relatively mild motor-impairment and also analyzed the performance of CP children periodically. The Eye image are captured through camera, this make the quick diagnosis and examination the periodical assessment\u0000 of CP kids. By Visual Therapy Function the CP children vision improvement develops as fast as or even faster. Proposed area of the research segments the eye image into Pupil, Iris, Eyelids and Eye corner detection using different image processing algorithms as well as measure the deviation\u0000 position of pupil for abnormal eyes taking the normal eye as the reference or threshold value. To further enhance these compare deviation Position of the normal and the abnormal eyes and to find the severity affection of abnormal eye and to measure the performance improvement achievement by\u0000 Visual Therapy Method for CP rehabilitation. The improvement analyzed for CP kids were maintained and recorded for the periodical month of Initial, 6th and 12th month. As a result, the Physicians can use this report to guide the CP kids in Rehabilitation Center and also this image processing\u0000 technique offers the greater flexibility for the prospective subjects of improvement in CP Rehabilitation by Visual Therapy Method. In this context, Image processing techniques are being recommended as a performance evaluation tool in children with CP and each of these processes are suggesting\u0000 method for developing a more systematic understanding of Oculomotor abnormalities.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81580506","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}
Z. Hua-feng, Chen Junping, Wang Ruichun, Wu Guorong
To explore the cause of sleep disorders in patients in the resuscitation room for anesthesia, and to put forward corresponding nursing methods. 212 patients with sleep disorder after operation were selected from our hospital's anesthesia resuscitation room from April 2015 to February 2016 to investigate the types and causes of sleep disorders with self-designed questionnaire and to carry out a targeted nursing intervention to them. The results showed that the proportion of frequent restless among the sleep disorders was the highest, and the others were in turn early awakening, difficulty of falling asleep again, difficulty in falling asleep, and sleepless all night. Among the causes of sleep disorders, the postoperative pain accounted for the highest percentage, and other major causes include abdominal distension, urgent urination, postural discomfort and other discomforts. According to the different types, causes and symptoms of sleep disorder, medical staff should have provide a targeted and planned nursing to improve the quality of sleep and facilitate a rapid resuscitation of patients.
{"title":"Non Linear Regression Analysis on the Effect of General Anesthesia on Postoperative Sleep Impairment in Elderly Patients Performed with a Laparotomy","authors":"Z. Hua-feng, Chen Junping, Wang Ruichun, Wu Guorong","doi":"10.1166/JMIHI.2018.2533","DOIUrl":"https://doi.org/10.1166/JMIHI.2018.2533","url":null,"abstract":"To explore the cause of sleep disorders in patients in the resuscitation room for anesthesia, and to put forward corresponding nursing methods. 212 patients with sleep disorder after operation were selected from our hospital's anesthesia resuscitation room from April 2015 to February\u0000 2016 to investigate the types and causes of sleep disorders with self-designed questionnaire and to carry out a targeted nursing intervention to them. The results showed that the proportion of frequent restless among the sleep disorders was the highest, and the others were in turn early awakening,\u0000 difficulty of falling asleep again, difficulty in falling asleep, and sleepless all night. Among the causes of sleep disorders, the postoperative pain accounted for the highest percentage, and other major causes include abdominal distension, urgent urination, postural discomfort and other\u0000 discomforts. According to the different types, causes and symptoms of sleep disorder, medical staff should have provide a targeted and planned nursing to improve the quality of sleep and facilitate a rapid resuscitation of patients.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83906488","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}
Jie Zhang, Licai Yang, Zhonghua Su, Xueqin Mao, Kan Luo, Chengyu Liu
Background: Noise is unavoidable in the physiological signal measurement system. Poor quality signals can affect the results of analysis and disable the following clinical diagnosis. Thus, it is necessary to perform signal quality assessment before we interpreting the signal. Objective: In this work, we describe a method combing support vector machine (SVM) and multi-feature fusion for assessing the signal quality of pulsatile waveforms, concentrating on the photoplethysmogram (PPG). Methods: PPG signals from 53 healthy volunteers were recorded. Each had a 5 min length. Signal quality in each heart beat was manual annotated by clinical expert, and then the signal quality in 5 s episode was automatically calculated according to the results from each beat segments, resulting in a total of 13,294 5-s PPG segments. Then a SVM was trained to classify clean/noisy PPG recordings by inputting a set of twelve signal quality features. Further experiments were carried out to verify the proposed SVM based signal quality classifier method. Results: An average accuracy of 87.90%, a sensitivity of 88.10% and a specificity of 87.66% were found on the 10-fold cross validation. Conclusions: The signal quality of PPGs can be accurately classified by using the proposed method.
{"title":"Photoplethysmogram Signal Quality Assessment Using Support Vector Machine and Multi-Feature Fusion","authors":"Jie Zhang, Licai Yang, Zhonghua Su, Xueqin Mao, Kan Luo, Chengyu Liu","doi":"10.1166/JMIHI.2018.2530","DOIUrl":"https://doi.org/10.1166/JMIHI.2018.2530","url":null,"abstract":"Background: Noise is unavoidable in the physiological signal measurement system. Poor quality signals can affect the results of analysis and disable the following clinical diagnosis. Thus, it is necessary to perform signal quality assessment before we interpreting the signal.\u0000 Objective: In this work, we describe a method combing support vector machine (SVM) and multi-feature fusion for assessing the signal quality of pulsatile waveforms, concentrating on the photoplethysmogram (PPG). Methods: PPG signals from 53 healthy volunteers were recorded. Each\u0000 had a 5 min length. Signal quality in each heart beat was manual annotated by clinical expert, and then the signal quality in 5 s episode was automatically calculated according to the results from each beat segments, resulting in a total of 13,294 5-s PPG segments. Then a SVM was trained to\u0000 classify clean/noisy PPG recordings by inputting a set of twelve signal quality features. Further experiments were carried out to verify the proposed SVM based signal quality classifier method. Results: An average accuracy of 87.90%, a sensitivity of 88.10% and a specificity of 87.66%\u0000 were found on the 10-fold cross validation. Conclusions: The signal quality of PPGs can be accurately classified by using the proposed method.","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90963082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Special Section on Methods and Application in Biomedical Imaging – Part 2","authors":"Luis Gomez Deniz Palmas Gran Canaria, E. Ng","doi":"10.1166/JMIHI.2018.2480","DOIUrl":"https://doi.org/10.1166/JMIHI.2018.2480","url":null,"abstract":"","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85143719","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Registration of Paired Inspiratory-Expiratory Lung CT Images Using Fissural Information","authors":"Z. Bian, Wenjun Tan, Jiren Liu, Dazhe Zhao","doi":"10.1166/JMIHI.2018.2491","DOIUrl":"https://doi.org/10.1166/JMIHI.2018.2491","url":null,"abstract":"","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"83 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76271356","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Research on Controllable Electrical Impedance Tomography System Based on Field Programmable Gate Array","authors":"Shiqiang Li, Guoqiang Liu, Xing Xu","doi":"10.1166/jmihi.2018.2495","DOIUrl":"https://doi.org/10.1166/jmihi.2018.2495","url":null,"abstract":"","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72874685","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}
Shiliang Lou, Lei Geng, Zhitao Xiao, Fang Zhang, Jun Wu, Yanbei Liu, Wen Wang
{"title":"Design of an Optical System for Nonmydriatic Stereoscopic Imaging Fundus Camera","authors":"Shiliang Lou, Lei Geng, Zhitao Xiao, Fang Zhang, Jun Wu, Yanbei Liu, Wen Wang","doi":"10.1166/jmihi.2018.2485","DOIUrl":"https://doi.org/10.1166/jmihi.2018.2485","url":null,"abstract":"","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72749984","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}
Liyong Ma, C. Ma, Yuejun Liu, Xugang Wang, Wei Xie
{"title":"Diagnosis of Thyroid Diseases Using SPECT Images Based on Convolutional Neural Network","authors":"Liyong Ma, C. Ma, Yuejun Liu, Xugang Wang, Wei Xie","doi":"10.1166/JMIHI.2018.2493","DOIUrl":"https://doi.org/10.1166/JMIHI.2018.2493","url":null,"abstract":"","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84374104","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}
Hang Sun, Hongli Zhang, Siqi Liu, Hong Li, Wei Zhang, F. M. Arukalam, W. Qian
{"title":"Automatic Breast Segmentation in Magnetic Resonance Imaging Using Improved Fully Convolutional Network","authors":"Hang Sun, Hongli Zhang, Siqi Liu, Hong Li, Wei Zhang, F. M. Arukalam, W. Qian","doi":"10.1166/JMIHI.2018.2489","DOIUrl":"https://doi.org/10.1166/JMIHI.2018.2489","url":null,"abstract":"","PeriodicalId":49032,"journal":{"name":"Journal of Medical Imaging and Health Informatics","volume":"6 6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80567136","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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