研究者業績

小橋 昌司

コバシ ショウジ  (Syoji Kobashi)

基本情報

所属
兵庫県立大学 工学研究科 教授 (研究所長)
学位
博士(工学)(姫路工業大学)

研究者番号
00332966
ORCID ID
 https://orcid.org/0000-0003-3659-4114
J-GLOBAL ID
200901031674454407
researchmap会員ID
6000003807

外部リンク

論文

 317
  • Yusho Kaku, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    PROCEEDINGS OF THE 2012 FIFTH INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING AND TECHNOLOGY (ICETET 2012) 314-317 2012年  査読有り
    Asthma causes the bronchus inflammation, and makes breathing impossible. In worst case, asthma causes death by dyspnea. If we can predict cause asthmatic attacks, they can prevent from asthmatic attacks. Therefore, asthmatic attacks prediction system is desired. As a prediction system using time series data, there is Fuzzy-AR model that can consider multi factors. In this paper, we propose a prediction method of the number of asthmatic attacks on next month based on Fuzzy-AR model. The proposed method considers weather factors; temperature, atmospheric pressure and humidity data. This method is applied to asthmatic attacks data from Himeji city Medical Association. As a comparison method, AR model is applied to same data. The experimental results shown that the proposed method predicts the number of asthmatic attacks better than AR model.
  • Takahiro Takeda, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    PROCEEDINGS OF THE 2012 FIFTH INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING AND TECHNOLOGY (ICETET 2012) 120-123 2012年  査読有り
    This article describes a personal identification method using dynamic foot pressure change while walking. This system acquires foot pressure change by wearable pressure sensor. The sensor has four sensing tips for the each foot, and these tips are fixed on shoes. In the experiment, we acquire eight steps pressure change data. The system extracts gait features from every step. The features are based on peak pressure value of each sensing tip. For all steps and all registered subjects, we calculate Euclidean distance between the feature and template of subject. The template is made from learning data of each registered person. For each step, the system chooses a registered person with the shortest Euclidean distance as a candidate of walking person. Then, the system counts selected number of the steps of a person. Finally, we identify the walking person by the larger number. The number less than threshold, identification of this walking person is failure. We employed 10 volunteers and identify them. In the experiment, we took pressure data 10 times for each volunteer. We used 3 data for learning and used the other 7 data for test data. The proposed method obtained 0.83% in FRR and 0.02% in FAR, when threshold parameter less than 3.
  • Yuya Takashima, Tomomoto Ishikawa, Syoji Kobashi, Kei Kuramoto, Yutaka Hata
    PROCEEDINGS OF THE 2012 FIFTH INTERNATIONAL CONFERENCE ON EMERGING TRENDS IN ENGINEERING AND TECHNOLOGY (ICETET 2012) 7-12 2012年  査読有り
    This paper describes a seminiferous tubules evaluation system using an ultrasonic probe. This system evaluates diameters of seminiferous tubules for azoospermia patients. We employ a 5.0MHz ultrasonic single probe. In the experiment, we employ large and small nylon lines as the healthy and unhealthy seminiferous tubules. We acquired the waveforms by the ultrasonic probe and calculated frequency spectrum by short-time Fourier transform. The system visualizes the position of small and large line by a frequency map. The frequency map shows distance and frequency value. In the results, our proposed method detected the large and small lines. Additionally, the system evaluated distance between probe and these lines clearly. The proposed method thus successfully evaluated the position of the large lines.
  • Hideaki Tanii, Kei Kuramoto, Hiroshi Nakajima, Syoji Kobashi, Naoki Tsuchiya, Yutaka Hata
    2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) 1-6 2012年  査読有り
    This paper proposes a body weight prediction method using Fuzzy-autoregressive (AR) model. New Fuzzy-AR model is formed by including fuzzy membership function which changes AR parameter in autoregressive (AR) model. We employed 452 volunteers, and collected their body weight time-series data during 730 days. We use body weight data from 1st to 365th day as learning data to determine the Fuzzy-AR models. After AR parameters are determined by Yule-Walker equation, we calculate the order, p, of the AR model for each volunteer based on Akaike's Information Criterion (AIC). In our experiment, we predicted body weight change for next p days for those subjects. In the Fuzzy-AR model, we make a fuzzy membership function based on the order of the AR model. As the result, the Fuzzy-AR model obtained higher correlation coefficient between predicted and truth values than the AR model on all volunteers. In addition, the Fuzzy-AR model obtained smaller mean absolute prediction error than the AR model.
  • Yuya Takashima, Tomomoto Ishikawa, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) 1-5 2012年  査読有り
    This paper describes a seminiferous tubules evaluation using an ultrasonic probe. In this system, we evaluate a diameter of seminiferous tubules for azoospermia patients. We employ a 5.0MHz ultrasonic single probe. In the experiment, we employ large and small nylon lines as the healthy and unhealthy seminiferous tubules. We made ball shape phantom from small and large lines in total 24. We acquire the waveforms by the ultrasonic probe and calculate amplitude values from the data that band pass filters applied. We then calculate cumulative relative frequency of amplitude values. Fuzzy if-then rules are made for the cumulative relative frequency of large and small lines. We evaluate a rate of large lines among all lines by using the fuzzy MIN-MAX center-of-gravity method. In the result, the mean absolute error was 5.98 %. The correlation coefficient was 0.98. The proposed method thus successfully evaluated the rate of the large lines.
  • Yusho Kaku, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    2012 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) 1-4 2012年  査読有り
    Asthma causes the bronchus inflammation, and makes breathing impossible. In worst case, asthma leads to death due to dyspnea. If we can predict that children cause asthmatic attacks, they can prevent from asthmatic attacks with minimum attention. Therefore, asthmatic attacks prediction system is desired. As a prediction system using time series data, there is Fuzzy-AR model that can consider multi factors. In this paper, we propose a prediction method of the number of asthmatic attacks on next month based on Fuzzy-AR model. The proposed method considers weather factors; temperature, atmospheric pressure and humidity data. This method is applied to asthmatic attacks data from Himeji city Medical Association. As a comparison method, AR model is applied to same data. The experimental results shown that the proposed method predicts the number of asthmatic attacks better than AR model.
  • Hashioka, A., Kobashi, S., Kuramoto, K., Wakata, Y., Ando, K., Ishikura, R., Ishikawa, T., Hirota, S., Hata, Y.
    International Journal of Computer Assisted Radiology and Surgery 7(2) 273-80 2012年  査読有り
    PURPOSE: Magnetic resonance imaging (MRI) is often used to detect and treat neonatal cerebral disorders. However, neonatal MR image interpretation is limited by intra- and inter-observer variability. To reduce such variability, a template-based computer-aided diagnosis system is being developed, and several methods for creating templates were evaluated. METHOD: Spatial normalization for each individual's MR images is used to accommodate the individual variation in brain shape. Because the conventional normalization uses as adult brain template, it can be difficult to analyze the neonatal brain, as there are large difference between the adult brain and the neonatal brain. This article investigates three approaches for defining a neonatal template for 1-week-old newborns for diagnosing neonatal cerebral disorders. The first approach uses an individual neonatal head as the template. The second approach applies skull stripping to the first approach, and the third approach produces a template by averaging brain MR images of 7 neonates. To validate the approaches, the normalization accuracy was evaluated using mutual information and anatomical landmarks. RESULTS: The experimental results of 7 neonates (revised age 5.6 ± 17.6 days) showed that normalization accuracy was significantly higher with the third approach than with the conventional adult template and the other two approaches (P < 0.01). CONCLUSION: Three approaches to neonatal brain template matching for spinal normalization of MRI scans were applied, demonstrating that a population average gave the best results.
  • Hideaki Tanii, Kei Kuramoto, Hiroshi Nakajima, Syoji Kobashi, Naoki Tsuchiya, Yutaka Hata
    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS 1259-1264 2012年  
    This paper proposes a body weight prediction method using Fuzzy prediction model. Fuzzy prediction model is constructed by an autoregressive (AR) model based on body weight data and linear prediction models based on biological data. The biological data are obtained by pedometers such as number of steps, calorie consumption and so on. The Fuzzy prediction model is fixed by solving Yule-Walker equation and minimizing the Akaike's Information Criterion. In our experiment, the model predicts body weight change for next p days where p is the order of AR model. Then, four linear prediction models related to the biological data are constructed by linear regression analysis. We make a fuzzy membership function based on mean absolute error between body weight data and predicted value of each prediction model. Furthermore, these models are optimized for each subject in prediction models which add the biological data to AR model based on the mean absolute error. We employed 452 volunteers, and collected their body weight time-series data and the biological data during 730 days. We use these data from 1st to 365th day as learning data to determine the Fuzzy prediction model. As the result, the Fuzzy prediction model obtained higher correlation coefficient between predicted and truth values than the AR model on most subjects. In addition, the Fuzzy prediction model obtained smaller mean absolute prediction error than the AR model.
  • Aya Hashioka, Kei Kuramoto, Syoji Kobashi, Yuki Wakata, Kumiko Ando, Reiichi Ishikura, Tomomoto Ishikawa, Shozo Hirota, Yutaka Hata
    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS 1253-1258 2012年  
    In magnetic resonance (MR) images, finding a small change of parenchyma in newborn babies' brain significantly helps physicians to diagnose suspicious hypoxic-ischemic encephalopathy patients. However, there are no computer-aided methods because an automated segmentation algorithm has not been established yet. This paper proposes a new image segmentation method for parenchyma segmentation in T2-weighted MR images. The proposed method introduces a fuzzy object model, which has a fuzzy boundary and MR signal learned from training data. It segments the parenchyma by maximizing a fuzzy degree of deformable surface model. The fuzzy degree is estimated by using the fuzzy object model. To validate the proposed method, we recruited 12 newborn babies whose revised ages were -1 month to 1 month. 9 subjects were used to generate the fuzzy object model, and the remained subjects were used to evaluate the segmentation accuracy. The segmentation accuracy has been evaluated by using sensitivity and false-positive ratio, which were calculated by comparing delineation result (ground truth).
  • Aya Hashioka, Syoji Kobashi, Kei Kuramoto, Yuki Wakata, Kumiko Ando, Reiichi Ishikura, Tomomoto Ishikawa, Shozo Hirota, Yutaka Hata
    2012 WORLD AUTOMATION CONGRESS (WAC) 2012年  
    The neonatal cerebral disorders might deform the brain shape, and reduce the cerebral function. For the diagnosis of cerebral disorders, it is effective to measure cerebral volume and surface area using head magnetic resonance (MR) image. The measurement should require a brain segmentation process. However, there are few studies for neonatal brain. This study proposes a brain segmentation method for a neonatal brain. In this study, we propose a shape and appearance knowledge based brain segmentation (SABS) method. SABS method segments a brain and cerebrospinal fluid (CSF) region by using a brain atlas model. Next, it classifies a brain and CSF region into some classes by using Bayesian classification with Gaussian mixture model, and optimizes the brain surface by using fuzzy rule-based active surface model method. Experimental results in 14 neonatal subjects (revised age between -1 month and 1 month) showed that the proposed method segmented the brain region with higher accuracy than the conventional methods.
  • Hokuto Mita, Syoji Kobashi, Kazuya Nakagawa, Kohji Nishiyama, Hitoshi Maeno, Kei Kuramoto, Yutaka Hata
    2012 WORLD AUTOMATION CONGRESS (WAC) 2012年  
    Marine radar systems have been equipped by almost sea vehicles. However, radar image quality can be easily deteriorated by signal strength decay with increasing distance, blurring due to side lobes, signals of behind objects and of shaded area. And it is difficult to recover the artifacts. This paper proposes a new approach to improve radar image quality by image fusion. The fusion method generating high quality radar images is based on EM algorithm. Performance was validated using radar images acquired from a single radar system at multiple locations. The experimental results showed that the proposed method constructed high quality radar images, and recovered signal decay and blurring due to side lobes.
  • Yutaka Hata, Kei Kuramoto, Syoji Kobashi, Hiroshi Nakajima
    2012 WORLD AUTOMATION CONGRESS (WAC) 2012年  
    This paper describes current state of the art of medical image processing and surgery support system. In it, typical medical image processing processes such as segmentation, registration and enhancement are described as an application of fuzzy logic. Second, orthopedic surgery and surgery support systems are introduced. In it, we meet the fuzzy logic application to ultrasonic system. Third, a bed-side health monitoring system is described. Fuzzy signal processing is employed in this system. Finally, a future direction of medical and health technology are discussed.
  • Aya Hashioka, Kei Kuramoto, Syoji Kobashi, Yuki Wakata, Kumiko Ando, Reiichi Ishikura, Tomomoto Ishikawa, Shozo Hirota, Yutaka Hata
    6TH INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND INTELLIGENT SYSTEMS, AND THE 13TH INTERNATIONAL SYMPOSIUM ON ADVANCED INTELLIGENT SYSTEMS 8672 1253-1258 2012年  
    In magnetic resonance (MR) images, finding a small change of parenchyma in newborn babies' brain significantly helps physicians to diagnose suspicious hypoxic-ischemic encephalopathy patients. However, there are no computer-aided methods because an automated segmentation algorithm has not been established yet. This paper proposes a new image segmentation method for parenchyma segmentation in T2-weighted MR images. The proposed method introduces a fuzzy object model, which has a fuzzy boundary and MR signal learned from training data. It segments the parenchyma by maximizing a fuzzy degree of deformable surface model. The fuzzy degree is estimated by using the fuzzy object model. To validate the proposed method, we recruited 12 newborn babies whose revised ages were -1 month to 1 month. 9 subjects were used to generate the fuzzy object model, and the remained subjects were used to evaluate the segmentation accuracy. The segmentation accuracy has been evaluated by using sensitivity and false-positive ratio, which were calculated by comparing delineation result (ground truth).
  • Yohei Tsumori, Shinichi Yoshiya, Masahiro Kurosaka, Shoji Kobashi, Nao Shibanuma, Motoi Yamaguchi
    JOURNAL OF ARTHROPLASTY 26(8) 1556-1561 2011年12月  査読有り
    We developed a new posterior-stabilized total knee arthroplasty (TKA) with a unique post-cam design that induces and accommodates internal tibial rotation with deep knee flexion. To validate the design concept of this system, we conducted an image analysis study employing a computer-aided diagnosis system for 24 TKA-implanted knees. In the analysis, the tibiofemoral relationship in the following 3 postures was evaluated: standing at extension, forward lunge, and kneeling with maximum knee flexion. The results of the image analysis showed achievement of consistent internal rotation of the tibia in deep flexion with a broad contact area at the post-cam interface as intended by the original design concept of this TKA system.
  • 川上 順祥, 小橋 昌司, 倉本 圭, 喜多村 祐里, 下野 九理子, 石川 智基, 谷池 雅子, 畑 豊
    電子情報通信学会技術研究報告. MI, 医用画像 110(364) 109-114 2011年1月12日  
    小児における難治性てんかんの原因の約8割は大脳皮質形成異常によるものである.現在,皮質形成異常部位の特定には硬膜下電極を用いた手法が用いられているが侵襲性が高く,非侵襲であるMR画像を用いた手法が求められている.しかし,MR画像上での皮質形成異常の画像特徴は未だ明らかではなく,皮質形成異常部位の自動検出法も未だ確立されていない.本論文では,大脳表面のフラクタル次元を特徴量として用い,サポートベクターマシンを用いて皮質形成異常度を推定する手法を提案する.本手法を3例の被験者に適用した結果,平均感度94.3%,平均特異度84.5%,平均有効度87.2%で皮質形成異常領域を検出できた.
  • Takahiro Takeda, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011) 1430-1435 2011年  査読有り
    This paper describes a personal verification method based on fuzzy logic using dynamic sole pressure distribution while walking. The method employs a pair id sole pressure distribution change, and it is acquired by a mat type load distribution sensor. As a preliminary experiment for shoes, we take sole pressure data by bare foot and two kind of slippered foot. We extract thirty nine gait features from each sole pressure distribution change. We calculate a fuzzy degree of a feature from two fuzzy if-then rules and them fuzzy membership functions for a feature. These fuzzy membership functions are statistically determined by learning data. The fuzzy degree of acquisition sole pressure data is calculated by total of fuzzy degree of all features. The method verifies the walking person by using the fuzzy degree of the acquisition sole pressure data. When the fuzzy degree of acquisition data higher than a threshold, we verify the walking person as the target person. In our experiments, we employed 11 volunteers and took sole pressure data six times for each volunteer and foot situation. When the learning data included same kind of test data, we obtained low equal error rate. We obtained low false acceptance rate.
  • Hideaki Tanii, Kei Kuramoto, Hiroshi Nakajima, Syoji Kobashi, Naoki Tsuchiya, Yutaka Hata
    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011) 1022-1025 2011年  査読有り
    This paper proposes a body weight prediction method using autoregressive (AR) model and Fuzzy-AR model. First, we employ 6 persons body weight change data of 365 days. AR model predicts body weight of a day from these time-series data. We calculate an order of AR model for each person by Akaike's Information Criterion. In the experiment, we predicted body weight change of next day for those subjects. The AR model obtained 0.798 in correlation coefficient between predicted and truth values. Second, we propose a Fuzzy-AR model that predicts body weight of next p days from last p days, where p is the order of AR model. In this method, we propose a Fuzzy-AR model with the fuzzy membership function using last p days data. In the experiment, the Fuzzy-AR model obtained 0.558 in correlation coefficient on 2 subjects.
  • Yuya Takashima, Kei Kuramoto, Syoji Kobashi, Yutaka Hata, Tomomoto Ishikawa
    IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011) 1013-1016 2011年  査読有り
    This paper describes a testicular tubules evaluation using 1.0MHz ultrasonic array probe. In this system, we evaluate a diameter of testicular tubules. We employ an ultrasonic array probe with the center frequency of 1.0MHz. We employ evaluation index that cumulative relative frequency of amplitude values. In the experiment, we employ 24 nylon lines as the testicular tubules. Amplitude of large nylon line echo is larger than that of small nylon echo. For the evaluation, we calculate cumulative relative frequency amplitude of acquisition data. Fuzzy if-then rules are made by the cumulative relative frequency of large and small lines. We evaluate a rate of large lines among all lines by using the fuzzy MIN-MAX center-of-gravity method. In this experiment, the proposed method successfully evaluated the rate of the large lines. We changed the rate of large lines in 24 nylon lines, and tested our method 20 times for each rate. We evaluated the rate with 5.77 % in mean absolute error.
  • Kobashi, S., Yokomichi, D., Wakata, Y., Ando, K., Ishikura, R., Kuramoto, K., Hirota, S., Hata, Y.
    Journal of Advanced Computational Intelligence and Intelligent Informatics 15(3) 362-369 2011年  査読有り
    Cerebral surface extraction from neonatalMR images is the basic work of quantifying the deformation of the cerebrum. Although there are many conventional methods of segmenting the cerebral region, only the rough area is given by counting the number of surface voxels in the segmented region. This article proposes a new method of extraction that is based on the particle method. The method introduces three kinds of particles that correspond to cerebrospinal fluid, gray matter, and white matter; it converts the brain MR images into the set of particles. The proposed method was applied to neonatal magnetic resonance images, and the experimental results showed that the cerebral contour was extracted with a root-mean-square-error of 0.51 mm compared with the ground truth contour given by a physician.
  • Seigo Kanazawa, Kazuhiko Taniguchi, Kazunari Asari, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    INDEPENDENT COMPONENT ANALYSES, WAVELETS, NEURAL NETWORKS, BIOSYSTEMS, AND NANOENGINEERING IX 8058 2011年  
    Home security in night is very important, and the system that watches a person's movements is useful in the security. This paper describes a classification system of adult, child and the other object from distance distribution measured by an infrared laser camera. This camera radiates near infrared waves and receives reflected ones. Then, it converts the time of flight into distance distribution. Our method consists of 4 steps. First, we do background subtraction and noise rejection in the distance distribution. Second, we do fuzzy clustering in the distance distribution, and form several clusters. Third, we extract features such as the height, thickness, aspect ratio, area ratio of the cluster. Then, we make fuzzy if-then rules from knowledge of adult, child and the other object so as to classify the cluster to one of adult, child and the other object. Here, we made the fuzzy membership function with respect to each features. Finally, we classify the clusters to one with the highest fuzzy degree among adult, child and the other object. In our experiment, we set up the camera in room and tested three cases. The method successfully classified them in real time processing.
  • Daisuke Yokomichi, Syoji Kobashi, Yuki Wakata, Kumiko Ando, Reiichi Ishikura, Kei Kuramoto, Tomomoto Ishikawa, Shozo Hirota, Yutaka Hata
    Proceedings of the 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011 2 1052-1057 2011年  
    Cerebral contour extraction from magnetic resonance (MR) images is a fundamental work to analyze brain MR images. The methods can be roughly classified into three approaches, voxel-based, mesh-based and particle-based. Each method has advantages and disadvantages. Especially, particle based method can extract the complicated sulci with sub-voxel accuracy. The remained work is to develop a method for estimating probability of particle transition among gray matter, white matter and cerebrospinal fluid. This paper proposes a new method for calculating the particle transition probability based on fuzzy inference technique. The proposed method was applied to computer synthesized MR images and neonatal brain MR images of volunteers.
  • Syoji Kobashi, Nobuyoshi Kawakami, Yuri T. Kitamura, Kuriko K. Shimono, Kei Kuramoto, Masako Taniike, Tomomoto Ishikawa, Yutaka Hata
    Proceedings of the 2011 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2011 1 467-473 2011年  
    About 80% of paediatric intractable epilepsy patients have accompanying cortical dysplasia. However, there are no established methods for noninvasive detection of cortical dysplasia. This paper proposes a novel method for automatically detecting cortical dysplasia using paediatric MR images. In order to evaluate cortical dysplasia in MR images, texture features and fractal dimension were extracted with an automated method and support vectors were used to evaluate the degree of cortical dysplasia. The proposed method was applied to three paediatric epilepsy patients. The automated method identified the cortical dysplasia lesion with a sensitivity of 94% a mean specificity of 85%, and a mean efficiency of 87%.
  • Syoji Kobashi, Noboru Takagi
    International Journal of Intelligent Computing in Medical Sciences and Image Processing 4(2) 89-91 2011年  
  • Takahiro Takeda, Yoshitada Sakai, Kei Kuramoto, Syoji Kobashi, Tomomoto Ishikawa, Yutaka Hata
    2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) 769-774 2011年  
    This paper describes a foot age estimation system using fuzzy logic. The method employs sole pressure distribution change data. The sole pressure data is acquired by a mat type load distribution sensor. The proposed method extracts step length, step center of sole pressure width, distance of single support period and time of double support period as gait features. The fuzzy degrees for young age, middle age and elderly groups are calculated from these gait features. The foot age of the walking person on the sensor is estimated by fuzzy MIN-MAX center of gravity method. In the experiment, the proposed method estimated subject ages with good correlation coefficient.
  • Syoji Kobashi, Norikazu Ikoma, Fumiaki Imamura, Nao Shibanuma, Kei Kuramoto, Tomomoto Ishikawa, Yutaka Hata
    2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) 746-751 2011年  
    Implanted knee kinematic analysis plays one of important role in clinical and research fields of total knee arthroplasty. Although there are some studies to analyze X-ray images for estimating 3-D knee kinematics, most of them cannot analyze dynamic video because they strongly depend on manual interaction of giving initial pose/position. This paper utilizes particle filter for analyzing dynamic radiograph video of implanted knee. By using particle filter, the proposed method does not require not only user interaction but also computational iteration of parameter optimization. As the result, we shorten the computation time and improved the estimation accuracy. The estimation error was lower than 0.7 mm for rotation, and 0.5 mm for translation including out-plane, and the computation time was 1.27 sec per frame using a cluster computer.
  • Masato Nakamura, Tomomoto Ishikawa, Syoji Kobashi, Kei Kuramoto, Yutaka Hata
    2011 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) 758-763 2011年  
    This paper describes a trans-skull ultrasonic system that measures the blood flow velocity in brain under skull. In this system, we use an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the blood flow and locate blood vessel by Doppler effect. This Doppler effect is examined by the center of gravity shift in the frequency domain. We use three silicon tubes of different thickness imitated to blood vessel. We confirmed the change of frequency quantity of Doppler effect according to the water current velocity. The experimental result shows that the system detects the flow velocity by Doppler effect under skull and do automatic extracting method of water current.
  • 中島 祐介, 小橋 昌司, 今村 史明
    臨床バイオメカニクス 32 497-502 2011年  
  • Takeda, J.T., Kuramoto, K., Kobashi, S., Hata, Y.
    Scientia Iranica 18(3 D) 655-662 2011年  
    This paper describes a biometric personal authentication method, using a pair of right and left sole pressure distribution changes, while walking. This system acquires sole pressure distribution changes via a mat type load distribution sensor, and does personal authentication. We employ twelve features based on the shape of a footprint, and twenty seven features based on weight movement for sole pressure data. Fuzzy if-then rules for each registered person are introduced, within which, their parameters are statistically determined in the learning process. We calculate the fuzzy degree of a pair of right and left sole pressure data for any registered person, and identify the walking person as the person with the highest fuzzy degree; the fuzzy degree being higher than a threshold. We employed 90 volunteers and authenticated them. We evaluate the proposed fuzzy method by five hold cross validation on which low false rejection and false acceptance rates are achieved. Thus, this fuzzy logic approach is precise for this biometric system. (C) 2011 Sharif University of Technology. Production and hosting by Elsevier B.V. All rights reserved.
  • Hata, Y., Kobashi, S., Kuramoto, K., Nakajima, H.
    Applied and Computational Mathematics 10(1) 133-145 2011年  
    This paper proposes a biosignal detection algorithm aided by fuzzy logic for heart rate and respiration. We employ two kinds of sensors to evaluate the algorithm. One is a air mat sensor consisting of an air tube and a pressure detection device, and the other is an ultrasonic sensor consisting of an ultrasonic probe with 2MHz and water in a small tank. Both sensors detect biosignals of human in bed. This algorithm has dynamically updated parameters to adopt the variety in the biosignal amplitudes and periods of the individuals in fuzzy membership functions. In our experiments, we applied this to healthy male volunteers and evaluated the accuracies of detecting heart beat and respiration. As the results, the algorithm successfully detected the heart beat in the both sensor data and respiration number in the air mat sensor data. We compared them with truth values obtained by ECG or Respiration belt. In addition, we evaluated the accuracies of the dynamically updated parameters in the heart rate detection. Consequently, this algorithm detected both heart and respiration signals with high accuracies.
  • Yutaka Hata, Syoji Kobashi, Kei Kuramoto, Hiroshi Nakajima
    IEEE SSCI 2011: Symposium Series on Computational Intelligence - RIISS 2011: 2011 IEEE Workshop on Robotic Intelligence in Informationally Structured Space 1-6 2011年  
    This paper describes a home care system for elderly by four detached multi-sensor network. The sensor network detects several bio-signals in bed. It consists of the following four sensors. An ultrasonic sensor detects human motion. An optical fiber sensor detects respiration. A mat-type sensor detects heart rate and respiration, and heart rate accesses autonomous nerve system. A microphone detects cough with phlegm to suction. The ultrasonic sensor attaches to bed frame, the mat sensor set under the bottom of the mattress in bed, the optical fiber is in mattress, and the microphone set in bed side. The sensor network can detect essential bio-signals such as heart rate, respiration, cough with phlegm to suction and body movement with high accuracy. © 2011 IEEE.
  • Hokuto Mita, Syoji Kobashi, Kazuya Nakagawa, Kohji Nishiyama, Hitoshi Maeno, Kei Kuramoto, Yutaka Hata
    Proceedings of 2011 6th International Conference on System of Systems Engineering: SoSE in Cloud Computing, Smart Grid, and Cyber Security, SoSE 2011 270-275 2011年  
    It is important to improve the quality of marine radar images because marine radar systems play a principle role of sea surveillance. However, it is difficult to recover signal strength decay with increasing distance, signals of behind objects and of shaded area. This paper proposes a new approach to improve radar image quality with system of systems (SoS) technology. The SoS is based on a ship-to-ship communication in which ships send and receive radar images, and the SoS constructs the high quality radar images using EM algorithm. Performance was validated using a computer simulation and using radar images acquired from a single radar system at multiple locations. The experimental results showed that the proposed method constructed high quality radar images, and recovered signal decay and signals of behind objects and of shaded area. © 2011 IEEE.
  • Aya Hashioka, Kosuke Yamaguchi, Syoji Kobashi, Yuki Wakata, Kumiko Ando, Reiichi Ishikura, Kei Kuramoto, Tomomoto Ishikawa, Shozo Hirota, Yutaka Hata
    Proceedings of 2011 6th International Conference on System of Systems Engineering: SoSE in Cloud Computing, Smart Grid, and Cyber Security, SoSE 2011 107-112 2011年  
    Measurement of cerebral volume and surface area using magnetic resonance (MR) image is effective for quantitative diagnosis of cerebral diseases. The measurement should require a brain segmentation process. Although many approaches for adult brain have been studied, there are few studies for neonatal brain. This study proposes a brain segmentation method for neonatal brain. Based on system of systems engineering technology, the proposed approach is composed from two systems; automated fuzzy logic based skull striping (AFSS) system and contour shape based modeling (CSM) system. AFSS segments the cerebral region based on Bayesian classification with Gaussian mixture model. CSM evaluates the skull stripping result of AFSS, and updates AFSS system parameters. Experimental results in 34 neonates (revised age between 2 weeks 1 day and 2 years 5 months) showed that the proposed approach segmented the brain region with sensitivity of 98.1% and false-positive rate of 27.9%. © 2011 IEEE.
  • Takahiro Takeda, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    INDEPENDENT COMPONENT ANALYSES, WAVELETS, NEURAL NETWORKS, BIOSYSTEMS, AND NANOENGINEERING IX 8058 2011年  
    This paper describes a biometric personal authentication method based on fuzzy logic using dynamics of sole pressure distribution while walking. The method employs a pair of right and left sole pressure data. These data are acquired by a mat type load distribution sensor. The proposed method has two processes. First, we calculate a fuzzy degree of each sole pressure data. In this process, we extract several gait features based on weight shift and shape of footprint. Fuzzy if- then rules for each registered person are introduced. In it, their parameters are statistically optimized in learning process. Second, we combine fuzzy degrees of right and left sole. In this process, we employ five operators. The method authenticates walking person with the combined fuzzy degree. We calculate the fuzzy degree of an interest person for all registered persons, and identify the interest person as the registered person with the highest fuzzy degree. While, we verify the interest person as the target person if the fuzzy degree of the interest person calculated for a target person is higher than a threshold. In an experiment on 50 volunteers, we obtained low false rejection and false acceptance rates.
  • Yutaka Hata, Kiyotaka Ho, Kei Kuramoto, Syoji Kobashi, Naoki Tsuchiya, Hiroshi Nakajima
    INDEPENDENT COMPONENT ANALYSES, WAVELETS, NEURAL NETWORKS, BIOSYSTEMS, AND NANOENGINEERING IX 8058 2011年  
    This paper discusses a data analysis by YURAGI for a heart rate non-constraining monitoring system Three signals are employed: primary signal is obtained by a mat-type sensor, which is placed between a bed and subject, the second one is obtained by an ultrasonic vibration senor attached to bed frame, and third one is Gaussian noise. We compare the results from the synthesized data of the first and second signals with those of first signal and the noise. We employ weighted sum as the synthesized method. We consider Gaussian noise as YURAGI. The extraction algorithm was developed based on fuzzy logic. The comparison was done on 10 healthy volunteers and we evaluated the accuracy for various weight ratio. Here, we must concern the accuracy because the tiny accuracy difference causes large difference in the autonomic nerve system assessment. As the result, the results obtained from both synthesized signals were superior to that from mat-type sensor signal only. Thus, YURAGI analysis is useful to for detecting heart rate by mat-type sensor.
  • 小橋 昌司, 畑 豊
    知能と情報 : 日本知能情報ファジィ学会誌 : journal of Japan Society for Fuzzy Theory and Intelligent Informatics 22(6) 787-788 2010年12月15日  
  • Sachiko Tsuji-Akimoto, Shinsuke Hamada, Ichiro Yabe, Itaru Tamura, Mika Otsuki, Syoji Kobashi, Hidenao Sasaki
    Journal of neurology 257(12) 2071-7 2010年12月  査読有り
    Loss of communication is a critical problem for advanced amyotrophic lateral sclerosis (ALS) patients. This loss of communication is mainly caused by severe dysarthria and disability of the dominant hand. However, reports show that about 50% of ALS patients have mild cognitive dysfunction, and there are a considerable number of case reports on Japanese ALS patients with agraphia. To clarify writing disabilities in non-demented ALS patients, eighteen non-demented ALS patients and 16 controls without neurological disorders were examined for frontal cognitive function and writing ability. To assess writing errors statistically, we scored them on their composition ability with the original writing error index (WEI). The ALS and control groups did not differ significantly with regard to age, years of education, or general cognitive level. Two patients could not write a letter because of disability of the dominant hand. The WEI and results of picture arrangement tests indicated significant impairment in the ALS patients. Auditory comprehension (Western Aphasia Battery; WAB IIC) and kanji dictation also showed mild impairment. Patients' writing errors consisted of both syntactic and letter-writing mistakes. Omission, substitution, displacement, and inappropriate placement of the phonic marks of kana were observed; these features have often been reported in Japanese patients with agraphia resulted from a frontal lobe lesion. The most frequent type of error was an omission of kana, the next most common was a missing subject. Writing errors might be a specific deficit for some non-demented ALS patients.
  • 山口弘祐, 小橋昌司, 倉本 圭, 喜多村祐里, 毛利育子, 今脇節朗, 谷池雅子, 畑 豊
    信学技報 2010年10月  査読有り
  • Syoji Kobashi, Yutaka Hata
    International Journal of Innovative Computing, Information and Control 6(3) 829-842 2010年3月  
    In evaluating thoracic function, it is effective to segment the five lung lobes from multidetector-row computed tomography (MDCT) images. Almost all of the conventional methods are based on extracting the lobar fissures however, some parts of the fissures may not be observed from MDCT images due to CT artifacts and/or adhesions between the lung lobes. This article proposes an alternative method for segmenting the lung lobes. It is based on tubular tissue density, and is not based on lobar fissure extraction. The tubular tissues are the peripheral blood vessels and peripheral bronchi. Because tubular tissues do not exist on the boundary between the lung lobes, our method determines the boundary by finding a continuous three-dimensional space in which tubular tissues are absent. The boundary determination process is automatically performed using fuzzy control. The proposed method was applied to five normal subjects, one patient with chronic obstructive pulmonary disease, and one patient with emphysema. The absolute mean error of detecting lobar boundaries was 3.4 mm and that the volumetric accuracy for the proposed method was an absolute ratio of 3.8 and 5.9% for inspiration and expiration, respectively. The proposed method is also applicable to MDCT images in which the lobar fissures cannot be distinguished. © 2010 ISSN 1349-4198.
  • Syoji Kobashi, Nao Shibanuma, Yutaka Hata
    Journal of Advanced Computational Intelligence and Intelligent Informatics 14(2) 122-127 2010年  
    Three-Dimensional (3-D) shape reconstruction of total knee arthroplasty (TKA) implants in vivo plays a key role to investigate implanted knee kinematics. TKA implants typically consist of metal femoral and tibial components and a polyethylene tibial insert. X-ray computed tomography (CT) causes severe metal artifacts, making the 3-D shape in reconstructed images extremely difficult to understand. This article proposes a new method of 3-D reconstruction from X-ray cone-beam images. Called a fuzzy visual hull, it introduces fuzzy logic in recognizing X-ray images. X-ray cone-beam images are fuzzified and back-projected into a fuzzy voxel space. Defuzzifying the fuzzy voxel space enables the 3-D TKA implant shape to be reconstructed. The results of evaluation using TKA implants in vitro and computer-synthesized images demonstrated that the fuzzy visual hull provides high robustness against noise added to X-ray cone-beam images. The new approach also reconstructed the 3-D polyethylene insert despite the difficulty of recognizing the region in conventional X-ray CT.
  • Hayato Yamaguchi, Hiroshi Nakajima, Kazuhiko Taniguchi, Syoji Kobashi, Yutaka Hata
    IEICE Transactions on Information and Systems E93-D(3) 542-549 2010年  
    This paper proposes a sensing system for a behavior detection system using an ultrasonic oscillosensor and an air pressure sensor. The ultrasonic oscillosensor sensor has a cylindrical tank filled with water. It detects the vibration of the target object from the signal reflected from the water surface. This sensor can detect a biological vibration by setting to the bottom bed frame. The air pressure sensor consists of a polypropylene sheet and an air pressure sensor, and detects the pressure information by setting under the bed's mattress. An increase (decrease) in the load placed on the bed is detected by the increase (decrease) in the pressure of the air held in the tube attached to the sheet. We propose a behavior detection system using both sensors, complementally. The system recognizes three states (nobody in bed, keeping quiet in bed, moving in bed) using both sensors, and we detect the behavior before getting out of bed by recognized these states. Fuzzy logic plays a primary role in the system. As the fundamental experiment, we applied the system to five healthy volunteers, the system successfully recognized three states, and detected the behavior before getting out of bed. As the clinical experiment, we applied the system to four elderly patients with dementia, the system exactly detected the behavior before getting out of the bed with enough time for medical care support. Copyright © 2010 The Institute of Electronics, Information and Communication Engineers.
  • 小橋 昌司, 中島 祐介, 吉矢 晋一
    Medical Imaging Technology 28(5) 317-321 2010年  
    人工膝関節置換術(Total knee arthroplasty:TKA)後の膝関節の動態解析は,TKA術後膝の診断や,TKA術に関する研究開発において主要な役割を示す.これまでの研究において2次元のX線透視画像と人工膝関節の3次元形状モデルを用いた2-D/3-Dイメージマッチング手法が多く提案されているが,これらは静止画像による解析に留まり,膝関節の連続的な動作を動画像により解析する研究はほとんど行われていない.本論文では,モンテカルロ法を用いた人工膝関節の3次元動態解析システムを提案する.提案手法は複数の候補を作成するパラメータ探索による2-D/3-Dイメージマッチングを用いて人工膝関節の3次元位置姿勢を推定する.これにより動画像の連続フレーム解析を可能とする.実験の結果,提案手法により膝関節の連続的な動作の解析,膝関節角度の推定が可能となり,7例の被験者に対する適用結果よりいくつかの膝関節動態のパターンを観察できた.
  • Syoji Kobashi, Daisuke Yokomichi, Kosuke Yamaguchi, Kei Kuramoto, Setsuro Imawaki, Yutaka Hata
    2010 5th International Conference on System of Systems Engineering, SoSE 2010 2010年  
    There are few studies on computer-aided diagnosis (CAD) system for neonatal magnetic resonance (MR) images. In addition, because it is difficult to apply multiple examinations for neonate, we should maximize data which are derived from a set of MR images. Under these requirements, this paper introduces system of systems (SoS) framework into analysis of neonatal MR images. The present system called SoS-CAD for neonatal brain is composed from skull stripping system, brain shape homologous modeling system, gyral labeling system and cerebral contour extraction system. By collaborating the systems with each other, the SoS-CAD produces multiple numerical and geometrical data from a set of MR images. © 2010 IEEE.
  • Kiyotaka Ho, Kenta Yamamoto, Naoki Tsuchiya, Hiroshi Nakajima, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010) 2010年  
    This paper describes a method for a heartbeat and respiratory rate monitoring system using air pressure sensors and ultrasonic oscillosensor. By using these sensors, we propose a detection method of the state of human and an extraction method of heartbeat and respiratory rate in bed by fuzzy logic. Our method was examined on four healthy volunteers. We successfully detected the state of human and extracted heartbeat and respiratory signals. In our method, fuzzy logic plays a primary role in the detection of the state and extraction of heartbeat and respiratory signals. An experiment on four healthy volunteers was done. Consequently, our proposed method noninvasively and successfully detects the state of human and extracted heartbeat and respiratory rate in the bed by using the unconstrained sensors.
  • Yusuke Nakajima, Syoji Kobashi, Yohei Tsumori, Nao Shibanuma, Fumiaki Imamura, Kei Kuramoto, Seturo Imawaki, Shinichi Yoshiya, Yutaka Hata
    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010) 2010年  
    For estimating 3-D pose position of artificial knee implants in vivo, there are some studies based on 2-D/3-D image registration of 2-D fluoroscopy images and 3-D geometrical model. Knee implant mainly consists of femoral component and tibial component. Most conventional studies estimate 3-D pose position of femoral component and tibial component individually. Rather, they don't evaluate relative position between the femoral and tibial components. This paper proposes a method for estimating 3-D pose position of implanted knee based on particle filter. A priori knowledge on the relational position of the components are utilized by using fuzzy membership functions. The experimental results for a patient and simulation DR images showed that the proposed method adequately estimate 3-D pose position of the femoral and tibial components with respect to relational position between the components.
  • Takahiro Takeda, Kazuhiko Taniguchi, Kazunari Asari, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010) 2010年  
    This paper proposes a biometric personal authentication method based on one step foot pressure distribution change. We acquire the foot pressure distribution change by mat type load distribution sensor and use it as a personal authentication. We employ twelve features based on shape of footprint, and twenty seven features based on movement of weight while walking. A classifier for each feature is developed on the basis of fuzzy inference. The classifier is trained by a clonal selection algorithm in artificial immune system. A personal authentication system for one step is made every classifier for all features. We employed 10 volunteers, and we took the step data five times. We evaluated our method by five-fold cross validation method. We obtained low false rejection and acceptance rates in identification and verification.
  • Kosuke Yamaguchi, Yuko Fujimoto, Syoji Kobashi, Yuki Wakata, Reiichi Ishikura, Kei Kuramoto, Seturo Imawaki, Shozo Hirota, Yutaka Hata
    2010 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE 2010) 2010年  
    Automated morphometric analysis using human brain magnetic resonance (MR) images is an effective approach to investigate the morphological changes of the brain. However, even though many methods for adult brain have been studied, there are few studies for infantile brain. Same as the adult brain, it is effective to measure cerebral surface and for quantitative diagnosis of neonatal and infantile brain diseases. This article proposes a skull stripping method that can be applied to the neonatal and infantile brain. The proposed method can be applied to both of T1 weighted and T2 weighted MR images. First, the proposed method estimates intensity distribution of white matter, gray matter, cerebrospinal fluid, fat, and others using a priori knowledge based Bayesian classification with Gaussian mixture model. The priori knowledge is embedded by representing them with fuzzy membership functions. Second, the proposed method optimizes the whole brain by using fuzzy active surface model, which evaluates the deforming model with fuzzy rules. The proposed method was applied to 26 neonatal and infantile subjects between -4 weeks and 4 years 1 month old. The results showed that the proposed method stripped skull well from any neonatal and infantile MR images.
  • Yusuke Nakajima, Syoji Kobashi, Yohei Tsumori, Nao Shibanuma, Fumiaki Imamura, Kei Kuramoto, Seturo Imawaki, Shinichi Yoshiya, Yutaka Hata
    2010 World Automation Congress, WAC 2010 2010年  
    Analyzing knee kinematics of implanted knee after Total Knee Arthroplasty (TKA) is affective approach in the research area of orthopaedics. There are some studies based on 2-D/3-D image registration of 2-D fluoroscopy images and 3-D geometric model. However, these conventional studies are designed for still statics image analysis, and there are a few studies for dynamic image of continuously knee movement. In addition, the another problem are to fall into local maxima in high-dimensional search space, and not to consider continuous knee movement. This paper proposes an analysis method of the continuous implanted knee kinematics based on Particle Filter. The proposed method estimates 3-D pose position of the implanted knee joint using 2-D/3-D image registration based on Particle filter. Particle filter is able to estimate optimal solution from multiple candidate pose position. The experimental results showed that the proposed method analyzed the continuous movement of the knee joint and estimated the knee joint angles. © 2010 TSI Press.
  • Takahiro Takeda, Kazuhiko Taniguchi, Kazunari Asari, Kei Kuramoto, Syoji Kobashi, Yutaka Hata
    2010 World Automation Congress, WAC 2010 2010年  
    This paper propose a biometric personal identification method based on a pair of right and left sole pressure distribution change. We acquire the sole pressure distribution change by load distribution sensor and use it for a personal identification. We employ twelve features based on shape of footprint, and twenty seven features based on movement of weight during walking for each sole pressure data. We make these fuzzy if-then rules. We calculate a fuzzy degree of a pair of right and left sole pressure data for one person, and identify person by this fuzzy degree. We evaluated our method by five-hold cross validation method. The low false rejection and acceptance rates are evaluated from 20 to 90 persons. © 2010 TSI Press.
  • Syoji Kobashi, Takahiro Hozumi, Shigeyuki Kan, Takahiko Koike, Kei Kuramoto, Setsuro Imawaki, Satoru Miyauchi, Yutaka Hata
    SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems 1612-1616 2010年  
    Conventional eye gaze tracking are based on special contact, optical equipment, Electrooculogram, etc. Although they have been produced valuable results in many areas, they cannot obtain the absolute eye gaze but obtain relative one based on calibration. This study proposes a method for detecting absolute eye gaze using MR images. Because MR images can obtain the sectional images of the eye ball, the method needs no calibration. The proposed method detects the eye gaze by segmenting the Vitreous, and lens of the eye ball. The method was applied to healthy subjects successfully. The estimated results were validated by comparing with the conventional method based on optical equipment.
  • Syoji Kobashi, Akitomo Tomaru, Yohei Tsumori, Shinichi Yoshiya, Kei Kuramoto, Seturo Imawaki, Yutaka Hata
    Proceedings - 3rd International Conference on Emerging Trends in Engineering and Technology, ICETET 2010 450-455 2010年  
    Anterior cruciate ligament (ACL) injury and knee osteoarthritis are well-known injuries of the knee joint, and pivot shift is a symptom of the ACL injury. Today, to diagnose the pivot shift phenomenon, the manual testing is examined for the early diagnosis, however, it should be affected by inter- and intra observer variability. This article proposes a novel system for detecting and quantifying the pivot shift phenomenon during pivot-shift testing using inertial and magnetic composite micro electro mechanical systems (MEMS) sensor. The system estimated 3-D acceleration vector caused by the pivot shift phenomenon using wavelet transform. The experimental results indicated that the pivot shift phenomenon was detected and quantified. And, there are correlation between the maximum acceleration of the knee movement at the pivot shift and the conventional evaluation by assessors. © 2010 IEEE.

MISC

 283

講演・口頭発表等

 234

担当経験のある科目(授業)

 17

共同研究・競争的資金等の研究課題

 25

学術貢献活動

 5

社会貢献活動

 2

メディア報道

 11