Pub Date : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299294
Hüseyin Cihad Güler, V. Yildiz, U. Baysal, ve Funda B. Cinyol, D. Köksal, E. Babaoğlu, S. Sarınç Ulaşlı
Lung sounds can vary according to various respiratory diseases of the person. Specialist physicians use these sound data to make a diagnosis. Diagnostic success varies according to the physician’s experience. computer-aided diagnostic systems can help physicians in this regard. In this study, disease diagnosis system was developed by using lung sound data obtained by auscultation method. In experimental studies, various machine learning methods have been tried on 20 normal, 20 ral and 20 rhoncus sound data taken from 60 patients. In addition, the data set was tripled with two different artificial data generation methods. The results obtained by applying k- Nearest Neighbor (kNN), Support Vector Machine (SVM), Naive Bayes, Decision Tree and Random Forest Classifier to all data obtained by real data set and artificial data production are presented. A 95% accuracy value was obtained with 10 cross- validation using the Naive Bayes classification method. In the results obtained after artificial data production, an accuracy value of 94% was obtained with 10 cross-validation with the kNN method.
{"title":"Classification of Abnormal Respiratory Sounds Using Machine Learning Techniques","authors":"Hüseyin Cihad Güler, V. Yildiz, U. Baysal, ve Funda B. Cinyol, D. Köksal, E. Babaoğlu, S. Sarınç Ulaşlı","doi":"10.1109/TIPTEKNO50054.2020.9299294","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299294","url":null,"abstract":"Lung sounds can vary according to various respiratory diseases of the person. Specialist physicians use these sound data to make a diagnosis. Diagnostic success varies according to the physician’s experience. computer-aided diagnostic systems can help physicians in this regard. In this study, disease diagnosis system was developed by using lung sound data obtained by auscultation method. In experimental studies, various machine learning methods have been tried on 20 normal, 20 ral and 20 rhoncus sound data taken from 60 patients. In addition, the data set was tripled with two different artificial data generation methods. The results obtained by applying k- Nearest Neighbor (kNN), Support Vector Machine (SVM), Naive Bayes, Decision Tree and Random Forest Classifier to all data obtained by real data set and artificial data production are presented. A 95% accuracy value was obtained with 10 cross- validation using the Naive Bayes classification method. In the results obtained after artificial data production, an accuracy value of 94% was obtained with 10 cross-validation with the kNN method.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129746458","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299309
A. Kavsaoğlu, ve Burak Bi̇lece, Besimcan Altiyaprak, ve Furkan Böyükçolak
There are people who have a lost limb or have no innate limb. In this study, it is aimed to create a data processing environment to improve the working performance of the prostheses to be developed for people with hand loss. Basically, Leap Motion and EMG devices were used. Simultaneous recording of data obtained with EMG and Leap Motion is provided using Arduino microcontroller and C # Interface design. In addition, a bionic hand control is provided from finger movements obtained with Leap Motion.
{"title":"C# Interface Design for Real-Time Signal Recording Oriented of Bionic Hand Control with Leap Motion and EMG Devices","authors":"A. Kavsaoğlu, ve Burak Bi̇lece, Besimcan Altiyaprak, ve Furkan Böyükçolak","doi":"10.1109/TIPTEKNO50054.2020.9299309","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299309","url":null,"abstract":"There are people who have a lost limb or have no innate limb. In this study, it is aimed to create a data processing environment to improve the working performance of the prostheses to be developed for people with hand loss. Basically, Leap Motion and EMG devices were used. Simultaneous recording of data obtained with EMG and Leap Motion is provided using Arduino microcontroller and C # Interface design. In addition, a bionic hand control is provided from finger movements obtained with Leap Motion.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"436 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123198974","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299292
T. Aydemir, ve Mehmet Şahi̇n, Önder Aydemir
Hypertension is the condition where the normal blood pressure is high. This situation is manifested by the high pressure of the blood in the vein towards the vessel wall. Hypertension mostly affects the brain, kidneys, eyes, arteries and heart. Therefore, the diagnosis of this common disease is important. It may take days, weeks or even months for diagnosis. Often a device called a blood pressure holter is connected to the person for 24 or 48 hours and the person’s blood pressure is recorded at certain intervals. Diagnosis can be made by the specialist physician considering these results. In recent years, various physiological measurement techniques have been used to accelerate this time-consuming diagnostic phase and propose intelligent models. One of these techniques is photopletesmography (PPG). In this study, a model for the detection of hypertension disease in individuals using the optimal frequency ranges of 2.1 second short-time PPG signals was proposed. The proposed model was tested with PPG data of 219 people and the disease was determined with classification accuracy of 76.15%. The results showed that the diagnosis of hypertension based on machine learning can be performed effectively by using frequency ranges of 1.4-5.7 Hz of short time PPG signals.
{"title":"Determination of Hypertension Disease with Optimal Frequency Range of Short-Time Photopletismography Signals","authors":"T. Aydemir, ve Mehmet Şahi̇n, Önder Aydemir","doi":"10.1109/TIPTEKNO50054.2020.9299292","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299292","url":null,"abstract":"Hypertension is the condition where the normal blood pressure is high. This situation is manifested by the high pressure of the blood in the vein towards the vessel wall. Hypertension mostly affects the brain, kidneys, eyes, arteries and heart. Therefore, the diagnosis of this common disease is important. It may take days, weeks or even months for diagnosis. Often a device called a blood pressure holter is connected to the person for 24 or 48 hours and the person’s blood pressure is recorded at certain intervals. Diagnosis can be made by the specialist physician considering these results. In recent years, various physiological measurement techniques have been used to accelerate this time-consuming diagnostic phase and propose intelligent models. One of these techniques is photopletesmography (PPG). In this study, a model for the detection of hypertension disease in individuals using the optimal frequency ranges of 2.1 second short-time PPG signals was proposed. The proposed model was tested with PPG data of 219 people and the disease was determined with classification accuracy of 76.15%. The results showed that the diagnosis of hypertension based on machine learning can be performed effectively by using frequency ranges of 1.4-5.7 Hz of short time PPG signals.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128321261","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299243
F. Nassehi, Başak Erdoğdu, Sena Şişman, Yağmur Sağlam, O. Eroğul
Topic of self-driving mode and transition to this mode is one of the trend topics of biomedical engineering and artificial intelligence studies. Sleeplessness and sleep efficiency to cause inattention in driving and accidents. This study aimed to investigate convenient time to transit self-driving mode respect to number of accidents and sleep efficiency of driver by using Support Vector Machines and K-Nearest neighbors classification algorithms to reduce the accidents. Approximate entropy and Lyapunov exponent for Electroencephalography and dominant frequency, ratio of power of high frequency to low frequency, area under the curve and derivative respiration signals were extracted. This proposal method achieves 93.33% and 100% accuracies to classify drivers and transit car to self-driving mode respect to two criteria.
{"title":"A Study On Finding The Optimal Time For Automatic Transition To Self-Driving Mode","authors":"F. Nassehi, Başak Erdoğdu, Sena Şişman, Yağmur Sağlam, O. Eroğul","doi":"10.1109/TIPTEKNO50054.2020.9299243","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299243","url":null,"abstract":"Topic of self-driving mode and transition to this mode is one of the trend topics of biomedical engineering and artificial intelligence studies. Sleeplessness and sleep efficiency to cause inattention in driving and accidents. This study aimed to investigate convenient time to transit self-driving mode respect to number of accidents and sleep efficiency of driver by using Support Vector Machines and K-Nearest neighbors classification algorithms to reduce the accidents. Approximate entropy and Lyapunov exponent for Electroencephalography and dominant frequency, ratio of power of high frequency to low frequency, area under the curve and derivative respiration signals were extracted. This proposal method achieves 93.33% and 100% accuracies to classify drivers and transit car to self-driving mode respect to two criteria.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129228541","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299286
Emrah Irmak
The novel coronavirus, generally known as COVID19, is a new type of coronavirus which first appeared in Wuhan Province of China in December 2019. The biggest impact of this new coronavirus is its very high contagious feature which brings the life to a halt. As soon as data about the nature of this dangerous virus are collected, the research on the diagnosis of COVID-19 has started to gain a lot of momentum. Today, the gold standard for COVID-19 disease diagnosis is typically based on swabs from the nose and throat, which is time-consuming and prone to manual errors. The sensitivity of these tests are not high enough for early detection. These disadvantages show how essential it is to perform a fully automated framework for COVID-19 disease diagnosis based on deep learning methods using widely available X-ray protocols. In this paper, a novel, powerful and robust Convolutional Neural Network (CNN) model is designed and proposed for the detection of COVID-19 disease using publicly available datasets. This model is used to decide whether a given chest X-ray image of a patient has COVID-19 or not with an accuracy of 99.20%. Experimental results on clinical datasets show the effectiveness of the proposed model. It is believed that study proposed in this research paper can be used in practice to help the physicians for diagnosing the COVID-19 disease.
{"title":"A Novel Deep Convolutional Neural Network Model for COVID-19 Disease Detection","authors":"Emrah Irmak","doi":"10.1109/TIPTEKNO50054.2020.9299286","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299286","url":null,"abstract":"The novel coronavirus, generally known as COVID19, is a new type of coronavirus which first appeared in Wuhan Province of China in December 2019. The biggest impact of this new coronavirus is its very high contagious feature which brings the life to a halt. As soon as data about the nature of this dangerous virus are collected, the research on the diagnosis of COVID-19 has started to gain a lot of momentum. Today, the gold standard for COVID-19 disease diagnosis is typically based on swabs from the nose and throat, which is time-consuming and prone to manual errors. The sensitivity of these tests are not high enough for early detection. These disadvantages show how essential it is to perform a fully automated framework for COVID-19 disease diagnosis based on deep learning methods using widely available X-ray protocols. In this paper, a novel, powerful and robust Convolutional Neural Network (CNN) model is designed and proposed for the detection of COVID-19 disease using publicly available datasets. This model is used to decide whether a given chest X-ray image of a patient has COVID-19 or not with an accuracy of 99.20%. Experimental results on clinical datasets show the effectiveness of the proposed model. It is believed that study proposed in this research paper can be used in practice to help the physicians for diagnosing the COVID-19 disease.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122134890","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299297
Nadide Gülşah Gülenç, M. Kartal
Many devices have been developed in order to increase the life standards of the medical device industry with the development of wireless communication technology today. Real- time monitoring of medical data and to inform users in case of emergencies has been indispensable. In this study, it was aimed to measure respiration, heart rate, SpO2 and body temperature of babies simultaneously with the wireless communication system. Thanks to this system we have designed, it will be an important convenience for the correct diagnosis to be easily monitored by the healthcare professional of the data of babies who need to be under surveillance in the home environment despite the end of their treatment in the hospital. Thanks to this implemented system, the follower can easily follow the baby’s status with the mobile application and receive alerts in sudden situations.
{"title":"Noninvasive Measurement of Baby’s Vital Datas and Mobile Monitoring - Analysis System Design","authors":"Nadide Gülşah Gülenç, M. Kartal","doi":"10.1109/TIPTEKNO50054.2020.9299297","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299297","url":null,"abstract":"Many devices have been developed in order to increase the life standards of the medical device industry with the development of wireless communication technology today. Real- time monitoring of medical data and to inform users in case of emergencies has been indispensable. In this study, it was aimed to measure respiration, heart rate, SpO2 and body temperature of babies simultaneously with the wireless communication system. Thanks to this system we have designed, it will be an important convenience for the correct diagnosis to be easily monitored by the healthcare professional of the data of babies who need to be under surveillance in the home environment despite the end of their treatment in the hospital. Thanks to this implemented system, the follower can easily follow the baby’s status with the mobile application and receive alerts in sudden situations.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121355165","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299245
Başak Lara Günal, V. Keskin, F. Kartufan, ve Özge Köner
Zoonotic retroviruses can cause widespread morbidity and mortality. Preventive vaccines are currently available for a limited number of viruses. Since an effective vaccine against COVID19 cannot be developed yet, personal protection equipment (PPE) is essential, especially for protecting the healthcare providers against such contaminations. Full face protecting equipment has a vital role in PPE. During the April 2020 spreading period of the COVID-19 epidemic, filter adapters were required to create a snorkel based full face mask as a PPE. This study aimed to report different production methods for filter adapters, features, advantages-disadvantages and combining the resulting mask’s physical characteristics and cost analysis.
{"title":"Development of a Full Face Mask during the COVID-19 Epidemic Spread Period","authors":"Başak Lara Günal, V. Keskin, F. Kartufan, ve Özge Köner","doi":"10.1109/TIPTEKNO50054.2020.9299245","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299245","url":null,"abstract":"Zoonotic retroviruses can cause widespread morbidity and mortality. Preventive vaccines are currently available for a limited number of viruses. Since an effective vaccine against COVID19 cannot be developed yet, personal protection equipment (PPE) is essential, especially for protecting the healthcare providers against such contaminations. Full face protecting equipment has a vital role in PPE. During the April 2020 spreading period of the COVID-19 epidemic, filter adapters were required to create a snorkel based full face mask as a PPE. This study aimed to report different production methods for filter adapters, features, advantages-disadvantages and combining the resulting mask’s physical characteristics and cost analysis.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"258 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114547734","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299254
Abbas Memiş, Songül Varlı, F. Bilgili
This paper introduces a study of automatic femoral head detection in magnetic resonance imaging (MRI) data sequences. For the 3D detection of the multiform femoral heads having both spheric and aspheric shape structures, the threedimensional form of the Integro-differential Operator (IDO) was performed. Following a set of image pre-processing operations including image intensity normalization, histogram equalization, morphological correction, hip joint separation and image binarization performed on bilateral hip MRI data sequences, the hip joints images are obtained in binary form in 3D. Then, the 3D form of IDO is performed in a predefined image volume to detect the femoral heads. Within the experimental studies performed on 8 bilateral hip MRI data sequences belonging to 6 LeggCalve-Perthes disease (LCPD) patients, promising success rates were observed. In detection of a total of 16 femoral heads, 8 of which are spheric and 8 of which are aspheric, 0.7021 (± 0.3160) and 0.6757 (± 0.2989) DSC values measured for the spheric and aspheric femoral heads, respectively.
{"title":"3D Femoral Head Detection in MRI Data Sequences with the Integro-differential Operator","authors":"Abbas Memiş, Songül Varlı, F. Bilgili","doi":"10.1109/TIPTEKNO50054.2020.9299254","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299254","url":null,"abstract":"This paper introduces a study of automatic femoral head detection in magnetic resonance imaging (MRI) data sequences. For the 3D detection of the multiform femoral heads having both spheric and aspheric shape structures, the threedimensional form of the Integro-differential Operator (IDO) was performed. Following a set of image pre-processing operations including image intensity normalization, histogram equalization, morphological correction, hip joint separation and image binarization performed on bilateral hip MRI data sequences, the hip joints images are obtained in binary form in 3D. Then, the 3D form of IDO is performed in a predefined image volume to detect the femoral heads. Within the experimental studies performed on 8 bilateral hip MRI data sequences belonging to 6 LeggCalve-Perthes disease (LCPD) patients, promising success rates were observed. In detection of a total of 16 femoral heads, 8 of which are spheric and 8 of which are aspheric, 0.7021 (± 0.3160) and 0.6757 (± 0.2989) DSC values measured for the spheric and aspheric femoral heads, respectively.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114910871","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299314
Hayriye Aktaş Dinçer, D. Gökçay
Conventional MRI studies have reported several structural changes such as brain atrophy and ventricular enlargement in healthy aging. Quantitative MRI (qMRI) allows the measurement of tissue characteristics such as the longitudinal relaxation times (T1) which provides unique and complementary information to widely used measures of brain signal characteristics. In this study, the T1 values on entire brain were mapped with an ROI based method. T1 prolongation with aging was demonstrated on numerous cortical and subcortical areas such as caudate, thalamus and prefrontal cortex. This outcome was interpreted as increased demyelination in these structures.
{"title":"Prolongation of Longitudinal Relaxometry Characteristics in Healthy Aging: a Whole Brain MRI Study","authors":"Hayriye Aktaş Dinçer, D. Gökçay","doi":"10.1109/TIPTEKNO50054.2020.9299314","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299314","url":null,"abstract":"Conventional MRI studies have reported several structural changes such as brain atrophy and ventricular enlargement in healthy aging. Quantitative MRI (qMRI) allows the measurement of tissue characteristics such as the longitudinal relaxation times (T1) which provides unique and complementary information to widely used measures of brain signal characteristics. In this study, the T1 values on entire brain were mapped with an ROI based method. T1 prolongation with aging was demonstrated on numerous cortical and subcortical areas such as caudate, thalamus and prefrontal cortex. This outcome was interpreted as increased demyelination in these structures.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115563269","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 : 2020-11-19DOI: 10.1109/TIPTEKNO50054.2020.9299302
Ş. K. Özen, M. Aksahin
For the segmentation of brain vessels from MRA images, brain tissue is used in the head, eye, skull, etc. must be separated from the structures. For this reason, studies are carried out for the segmentation of brain tissue. In this study, the method that automatically segregates brain tissue from magnetic resonance angiography images taken with time of flight (TOF) technique is presented. The method in the study consists of five steps. First of all, the tip contrast values in the image are filtered by anisotropic diffusion filtering method. Parameters of anisotropic diffusion method are determined automatically by the natural image quality evaluator method. Sudden density transitions are detected by applying LoG edge detection filter on the filtered image. It is made ready for image analysis by applying etching on the image with density transitions. According to the conditions determined in image analysis, brain tissue is obtained separated from other head structures. As a result of this study, an easy-to-apply, fast-delivering, high-accuracy automatic algorithm has been introduced.
{"title":"Automatic Brain Tissue Segmentation on TOF MRA Image","authors":"Ş. K. Özen, M. Aksahin","doi":"10.1109/TIPTEKNO50054.2020.9299302","DOIUrl":"https://doi.org/10.1109/TIPTEKNO50054.2020.9299302","url":null,"abstract":"For the segmentation of brain vessels from MRA images, brain tissue is used in the head, eye, skull, etc. must be separated from the structures. For this reason, studies are carried out for the segmentation of brain tissue. In this study, the method that automatically segregates brain tissue from magnetic resonance angiography images taken with time of flight (TOF) technique is presented. The method in the study consists of five steps. First of all, the tip contrast values in the image are filtered by anisotropic diffusion filtering method. Parameters of anisotropic diffusion method are determined automatically by the natural image quality evaluator method. Sudden density transitions are detected by applying LoG edge detection filter on the filtered image. It is made ready for image analysis by applying etching on the image with density transitions. According to the conditions determined in image analysis, brain tissue is obtained separated from other head structures. As a result of this study, an easy-to-apply, fast-delivering, high-accuracy automatic algorithm has been introduced.","PeriodicalId":426945,"journal":{"name":"2020 Medical Technologies Congress (TIPTEKNO)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127694154","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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