小橋 昌司

Recent studies have demonstrated that immune cells play an important role in the pathogenesis of many neurological conditions. Immune cells constantly survey the brain microvasculature for irregularities in levels of factors that signal homeostasis. Immune responses are initiated when necessary, resulting in mobilisation of the microglial cells resident in the central nervous system (CNS) and/or of infiltrating peripheral cells. However, little is known about the kinetics of immune cells in healthy and diseased CNS, because it is difficult to perform long-term visualisation of cell motility in live tissue with minimal invasion. Here, we describe highly sensitive in vivo MRI techniques for sequential monitoring of cell migration in the CNS at the single-cell level. We show that MRI combined with intravenous administration of super-paramagnetic particles of iron oxide (SPIO) can be used to monitor the transmigration of peripheral phagocytes into healthy or LPS-treated mouse brains. We also demonstrate dynamic cell migration in live animal brains with time-lapse MRI videos. Time-lapse MRI was used to visualise and track cells with low motility in a control mouse brain. High-sensitivity MRI cell tracking using SPIO offers new insights into immune cell kinetics in the brain and the mechanisms of CNS homeostasis.

人間同士の，あるいは人間と機械の間のスムーズなコミュニケーションを実現するには，従来のファジィの概念をブレイクスルーした非定量空間で定義される新しい概念，「ネオファジィ」の創出と，その実用技術の開発が必要である．本パネル討論では，①あいまいな言語を使った推論，連想，類推，比喩の意味理解，②テクスト理解，テクスト生成，言語によるコミュニケーションのメカニズム，③ネオファジィ言語を生成・理解する際の脳内ネットワークの形成過程，④ネオファジィ言語と視聴覚表象の対応メカニズム，⑤ネオファジィ専用のメモリやアーキテクチャなど，ハードウェア，⑥ネオファジィ言語を用いたコミュニケーションの応用等について議論する．

脳機能解析や VBM(voxel-based-morphometry)などにおいてMR画像を用いた個人脳間の画像位置合わせが用いられている．従来法では主に画像位置合わせの尤度を，脳全体の信号値の一致度とし，脳溝の一致を評価していないため，異なる脳回に位置合わせされる危険性がある．また，新生児脳はMR信号特徴が異なり，脳溝が狭いため，成人を対象とする従来法の適用は困難である．本文では，脳表近傍のMR信号値から算出される脳溝特徴分布(SDI; sulcal-distribution index)を用いたFlatteningにより，真球上でSDI相互情報量を最大化する3次元非剛体変形を行い，脳溝の一致度を尤度とした脳形状位置合わせを可能とする．また，変形時に緩和項としてバネモデルを使用することで真球上の制御点密度の偏りを減少させる．提案法を修正齢3から5週間の新生児3名のMR画像に適用した結果，緩和項を用いることで位置合わせ精度の向上が確認できた．

In this paper, a two-staged method of determining the order of medical examinations is presented to reduce sojourn times for patients visiting a department of cardiology. The first stage works as the batch process in the nighttime, and determines the order of medical examinations for patients with appointments. The second stage is designed for patients without appointments, and searches free time slots into which the examinations for such patients can be inserted in the list of examinations that the first stage generated during the night. When determining orders, the proposed method uses values randomly generated from surveillance results as examination times. Experimental results show that the proposed method achieves short sojourn times both for patients without appointments and for those with appointments, compared with the determination manually made by a nurse.

Total hip arthroplasty (THA) is an orthopaedic surgery which replaces the damaged hip joint with implants. The acetabular cup is implanted to the acetabulum. Some studies show that the outcome of THA is strongly correlated to the orientation of the acetabular cup. This paper proposes a fully automated method for measuring the orientation using multidetector-row computed tomography (MDCT) images. The method defines the pelvic anatomical coordinate system using anterior pelvic plane (APP), and measures angles between the cup implanting axis and the pelvic anatomical coordinate axis. The angles are inclination angle and anteversion angle. The method was applied to two phantoms in which the acetabular cup was implanted to the artificial bone. We acquired multiple set of MDCT image for the same phantom with changing the pelvic pose in the MDCT scanner to evaluate the reproducibility. The standard deviations of measured angles in multiple acquisitions were less than 2.0 deg for both of the inclination and the anteversion angles.

© Springer International Publishing Switzerland 2014. This paper proposes a muscular thickness measurement method using acoustic velocity dependency according to temperature. It is known that the acoustic velocity for temperature change depends on the materials is slower than warm ones. From this principal, we measured the muscular thickness. We employ a 1.0 MHz ultrasonic probe, and acquire two kind ultrasonic echoes from same position of body with temperature variation. From these echoes, we extract boundary surface echoes. From echoes, regions of muscular and fat are extracted by difference between the acoustic velocity-temperature characteristics of muscular and fat. In our experiment, we employ a piece of pork as an experimental phantom, and we acquire ultrasonic echoes reflected from the phantom. Our proposed method successfully measured the thicknesses in muscular and fat region.

This paper demonstrates the possibility of in vivo imaging for neuroimmunological assessments using ultra high-field magnetic resonance imaging (UHF-MRI). UHF-MRI provides a highly sensitive MR microimaging technique could be used to identify previously invisible pathologies and cellular dynamics in the central nervous system (CNS) of living animals. Our technique could reveal the mechanisms underlying the immune responses and cell dynamics during neuroinflammation, CNS diseases, and also in the normal state.

© Springer International Publishing Switzerland 2014. This paper proposes an energy visualization system by a camera. For monitoring, a single camera captures gas meter image at fixed intervals. The system applies edge detection and the connected-component labeling to extract numeral regions in counters of a gas mater. Gas consumption is estimated based on shape characteristics of numerals. The system uses number of endpoints and holes in numerical character, and it calculates a direction histogram and sum of absolute difference (SAD). The system recognizes the numeral by fuzzy inference from the acquired shape characteristic. When the system failed to recognize gas consumption by some accidents, the consumption is interpolated from time-serious data. In the result, our method estimated 32 and 29 numerals in 33 pieces for front and slant measurement respectively. For a continual monitoring in a day, the system successfully estimated dynamic gasconsumption change and visualized them.

This paper describes a foot-age estimation system based on fuzzy logic. The foot-age is one of age related indexes, and it shows the degree of aging by the gait condition. The system estimates the foot-age from sole pressure distribution change during walking. The sole pressure distribution is acquired by a mat-type load distribution sensor. Our estimation system extracts four gait features from sole pressure data, and calculates fuzzy degrees for young age,middle age and elderly age groups from these gait features. The footage of the walking person on the sensor is calculated by fuzzy MIN-MAX center of gravity method. In our experiment, we employed 93 male and 132 female volunteers, and the system estimated their foot-ages with low mean absolute error for their true ages. Additionally, we developed a diagnosis system based on estimated foot-age.

© Springer International Publishing Switzerland 2014. The number of asthmatic attacks was predicted by a time series data analysis in the areas divided into the coastal place and the inland place in Himeji city. As a result, SARIMA model obtained the highest total of CC=0.733, MAPE = 13.4 in inland place, and AR model obtained the highest total of CC=0.549, MAPE = 13.9 in coastal place. The prediction in inland place got enough precision. On the other hand, the prediction in the coastal place didn’t get enough precision. Therefore, it was confirmed that the prediction in some areas by time series models was difficult.

Macrophage is one of white blood cells, and plays an important role of the immune system. Tracking the single macrophage cells in vivo will be a powerful tool for immunology studies. State-of-the-arts imaging using magnetic resonance imaging (MRI) enables us to acquire images of 3-D dynamic single macrophage cells in vivo. However, due to motion artifacts and magnetic field fluctuations, a post-processing is required to observe macrophage cells. This paper proposes an image analysis method for 11.7T animal MRI images of macro phages in the mouse brain. The method adjusts the motion artifacts by a rigid image registration technique, and calibrates MR signal intensity fluctuation by using an optimization technique. The method was applied to mouse brain MR images, and the results were validated by observers.

Brain region segmentation in neonatal magnetic resonance (MR) images is an essential task for computer-aided diagnosis of neonatal brain disorders using MR images. We have proposed a neonatal brain segmentation method using a fuzzy object model (FOM), which represents a prior knowledge of brain shape and location. The FOM is constructed from multiple neonatal brain MR images whose revised age was between 0 and 4 weeks. The method segmented the brain region with a good accuracy for subjects whose age matches of the training data set. To enhance the method, we need multiple FOMs for each age. The other solution is to develop a growable model. This paper introduces 4-D FOM and applies it to neonatal brain segmentation. This paper introduces a neonatal brain segmentation method using 4-D FOM. The proposed method consists of three components. The first part proposes a method for estimating the brain development progress, called growth index in this study, from MR images based on Manifold learning. The second part shows a procedure for generating 4-D FOM using the estimated growth index. The third part is to segment brain region based on fuzzy-connectedness image segmentation using 4-D FOM. The proposed method was applied to 16 neonatal subjects. The results show that 4-D FOM is superior to stable 3-D FOM for segmenting neonatal brain region from MR images.

Most of computer-assisted planning systems need to determine the anatomical axis based on the anterior pelvic plane (APP). We analysed that our new system is more reproducible for determination of APP than previous methods. A pelvic model bone and two subjects suffering from hip osteoarthritis were evaluated. Multidetector-row computed tomography (MDCT) images were scanned with various rotations by MDCT scanner. The pelvic rotation was calibrated using silhouette images. APP was determined by an optimisation technique. The values of variation of APP caused by pelvic rotation were analysed with statistical analysis. APP determination with calibration and optimisation was most reproducible.The values of variance of APP were within 0.05° in model bone and 0.2° even in patient pelvis. Furthermore, the values of variance of APP with calibration/optimisation were significantly lower in comparison without calibration/optimisation. Both calibration and optimisation are actually required for determination of APP. This system could contribute to the evaluation of hip joint kinematics and computer-assisted surgery.

This paper describes noninvasive cellular quantity measurement in Bone Marrow Stromal Cells/ β-tricalcium phosphate. We attempt to identify cellular quantity with an ultrasonic system. The ultrasonic waves are reflected at boundaries where there is a difference in acoustic impedances of the materials on each side of the boundary. Therefore, we focus on the reflected signal. From the obtained ultrasonic data, we extract two features; amplitude and frequency. Amplitude is obtained from the raw ultrasonic wave, and frequency is calculated from frequency spectrum obtained by applying cross-spectrum method. Therefore, we suggest the superiority of frequency to analyze Bone Marrow Stromal Cells. This study shows the ability of intervention to produce the desired beneficial effect.

Newborn's brain has a various shape, and easily changes with not only brain developing and cerebral diseases. Although the brain segmentation in MR images is an effective way to quantify the brain shape and size, there are few studies in neonatal brain MR image analysis. This paper introduces a novel method based on fuzzy connectedness (FC) with fuzzy object model (FOM). FOM is built from a training dataset, and gives fuzzy degree belonging to parenchyma with respect to location and intensity. FC is calculated from object affinity and homogeneous affinity, and the object affinity is given by the FOM. The method first segments the white matter, and then segments the surrounding cortex. The propose method has been applied to 10 newborn subjects whose revised age was between -1 month and +2 month. Leave-on-out cross-validation (LOOCV) was conducted, and the mean false-positive volume fraction was 1.33%, the mean false-negative volume fraction was 2.90%, and geometric-mean was 1.42%.

Bone tissue engineering techniques have become new approaches in bone regeneration. Before clinical implantation, the preconditioning is needed. Therefore, we implement the ultrasonic evaluation system without cellular destruction. This study focuses the cellular proliferation into the composites of bone marrow stromal cells (BMSCs) / beta-tricalcium phosphate (beta-TCP) and composes the ultrasonic cell quantity determination on frequency domain for the BMSCs / beta-TCP composites after being cultured: 4 types BMSCs to 24 beta-TCP scaffolds. This system aims viscous attenuation because viscosity is proportional to frequency-squared. On frequency domain, we confirmed the attenuation in the immediate vicinity of 1.0 MHz, which is the center frequency of the probe. Moreover, it is discussed and concluded; the findings in this work illustrate that the frequency properties of BMSCs / beta-TCP composites have the prominent osteoconductive activity and the potential for applications/approaches in future regenerative medicine.

Image segmentation plays a fundamental work to analyze medical images. Although many literatures studied automated image segmentation, it is still difficult to segment region-of-interest in any kind of images. Thus, manual delineation is important yet. In order to shorten the processing time and to decrease the effort of users, this paper introduces two approaches of interactive image segmentation method based on fuzzy connectedness image segmentation (FCIS). The first approach interactively updates object affinity of FCIS according to users' additional seed voxels. The second approach models the profile of the object affinity using radial-basis function network (RBFN), and applies online training for users' additional seed voxels. The proposed methods updates segmentation results for not only the seed voxels but also the other miss-classified voxels. The methods had been applied to neonatal brain magnetic resonance (MR) images. The experimental results showed the second approach produced the best results.

Patient specific instruments (PSI) system has been attracting considerable attention for navigation-free surgical operation of total knee arthroplasty (TKA). PSI is a jig which guides a cutting section of the femoral or the tibia bones where TKA implant is attached to ensure accurate and reproducible surgery, and is prepared for a specific patient. However, another problem has been raised because the attachment of PSI will cause errors of TKA guidance. This paper proposes a novel system to evaluate the accuracy of attaching PSI during TKA operation using cone-beam CT. The system acquires 3-D sectional images of PSI attached to knee, and evaluates the attachment accuracy by means of image registration techniques with computer-aided design (CAD). It calculates the position and the angle of guidance pin, and compares with the preoperative planning. The system has been applied to three subjects which had been operated TKA with PSI. The results produced 3-D renderings of the attached PSI and of the planned PSI, and calculated angle differences between the attached and the planned guidance pin. By using them, we can evaluate the attachment accuracy of PSI, and also evaluate the implanting accuracy of TKA.

Patient specific instruments (PSI) system has been attracting considerable attention for navigation-free surgical operation of total knee arthroplasty (TKA). PSI is a jig which guides a cutting section of the femoral or the tibia bones where TKA implant is attached to ensure accurate and reproducible surgery, and is prepared for a specific patient. However, another problem has been raised because the attachment of PSI will cause errors of TKA guidance. This paper proposes a novel system to evaluate the accuracy of attaching PSI during TKA operation using cone-beam CT. The system acquires 3-D sectional images of PSI attached to knee, and evaluates the attachment accuracy by means of image registration techniques with computer-aided design (CAD). It calculates the position and the angle of guidance pin, and compares with the preoperative planning. The system has been applied to three subjects which had been operated TKA with PSI. The results produced 3D renderings of the attached PSI and of the planned PSI, and calculated angle differences between the attached and the planned guidance pin. By using them, we can evaluate the attachment accuracy of PSI, and also evaluate the implanting accuracy of TKA. © 2013 The Authors.

This paper proposes multi human location estimation system using four thermopile array sensors. These sensors are attached to the ceiling and acquire 8 × 8 place-dependent thermal distributions. Firstly, human area is detected by background removal with fuzzy inference based on human characteristics. Secondly, human areas are identified by Connected Component Labeling. Thirdly, label is checked to detect adjoining people. Finally, the number of people and their locations are estimated as the labels. In our experiment, we employed four adult persons and they performed four motion patterns. As the result, the system estimated the peoples successfully. © 2013 IEEE.

This paper describes an object classification method using infrared laser camera. The method detects moving objects from a distance distribution data. We classify the object to adults, children and other objects. The objects are extracted and clustered by fuzzy c-means clustering method. To classify the object, we calculate the height, thickness, aspect ratio and occupancy of the object as features from a sampling time. Fuzzy if-then rules and fuzzy membership functions are derived from knowledge of human and object. The system classifies the object based on fuzzy logic. In our experiment, we employed seven volunteers, two dogs and a box, and the system successfully classified them. © 2013 IEEE.

This paper describes fuzzy damage extraction method for ultrasonic nondestructive testing images. In our experiment, we employ a piece of wind turbine blade as a specimen has artificial damages. We acquire ultrasonic waveforms from scanning lines on surface of the specimen using an ultrasonic single probe. To extract the damages, we calculate fuzzy degrees of average difference data of all scanning lines, and make fuzzy images whose intensities calculated by the fuzzy degrees. As the results, we found the line image with all damage portions, and we estimated depth of damage surface with high accuracy.

This paper proposes a human activity estimation system using a wearable multi-sensor with a built-in electrocardiograph and triaxial accelerometers. The multi-sensor unconstraintly measures biological information, and provides these data to personal computer by wireless communication. We estimate human activity in a series of activities by the biological information. In our experiment, the subjects have several activities such as "Walking", "Rest" and "Strength training". The system estimates these activities by a decision tree. Branch conditions of the decision tree are aided by fuzzy logic and state of activity transition from previous activity. Fuzzy membership functions are constructed from exercise intensity, distinction frequency and transitional probability. As the results, the proposed method estimated activities with good accuracy.

Newborn brain MR image segmentation is a challenging problem because of variety of size, shape and MR signal although it is the fundamental study for quantitative radiology in brain MR images. Because of the large difference between the adult brain and the newborn brain, it is difficult to directly apply the conventional methods for the newborn brain. Inspired by the original fuzzy object model introduced by Udupa et al. at SPIE Medical Imaging 2011, called fuzzy shape object model (FSOM) here, this paper introduces fuzzy intensity object model (FIOM), and proposes a new image segmentation method which combines the FSOM and FIOM into fuzzy connected (FC) image segmentation. The fuzzy object models are built from training datasets in which the cerebral parenchyma is delineated by experts. After registering FSOM with the evaluating image, the proposed method roughly recognizes the cerebral parenchyma region based on a prior knowledge of location, shape, and the MR signal given by the registered FSOM and FIOM. Then, FC image segmentation delineates the cerebral parenchyma using the fuzzy object models. The proposed method has been evaluated using 9 newborn brain MR images using the leave-one-out strategy. The revised age was between -1 and 2 months. Quantitative evaluation using false positive volume fraction (FPVF) and false negative volume fraction (FNVF) has been conducted. Using the evaluation data, a FPVF of 0.75% and FNVF of 3.75% were achieved. More data collection and testing are underway.

This paper contains experimental procedure of webcam-based eye-tracker specially for low power devices. This paper consists of five processes. First one is background suppression for reducing average processing requirement. Second one is Haar-cascade feature-based face detection algorithm. Third one is geometrically eye-position determination. The fourth part is to detect and track eye-ball center using mean of gradient vector. The fifth and last step is to detect where the user looking. We simply calculate percentage of movement of eye to detect either it looking left, right, up or down. This procedure is highly effective with satisfactory accuracy. It also requires less processing power.

Total knee arthroplasty (TKA) is an orthopedic surgery which replaces the damaged knee joint with the artificial one. To diagnose the function of the implanted knee joint, it is effective to estimate 3-D knee kinematics in vivo. There are some conventional methods for estimating kinematics of the implanted knee using 2-D/3-D image registration for X-ray fluoroscopic images and 3-D geometrical models of the knee implant. This paper proposes a method for analyzing knee kinematics based on particle filter which became high precision using priori knowledge. The experimental results showed that the proposed method left the grade that was better than non-priori-knowledge method.

This paper describes noninvasive diagnosis of blood flow for Transcranial Doppler (TCD) ultrasonography. This trans-skull ultrasonic system measures the blood flow velocity in the human brain using an ultrasonic array probe with the center frequency of 1.0 MHz. The system determines the blood flow and locates the blood vessel using Doppler effects, which are defined by the center of gravity in the frequency domain. We employ three different thickness silicon tubes, which imitate blood vessels. This system detected the flow direction by Doppler Effect under skull and visualized the skull and flow direction. Under the conditions with 0.16-0.27 mm thickness bone and our equipment specification, we confirmed to enable to diagnose the blood flow under the bone. This result is superior to measure the blood flow from any part of the adult human skull. Moreover, we extracted the region of skull and blood vessel automatically. © 2013 Copyright TSI® Press.

This paper describes an object classification method using infrared TOF camera. The method detects moving objects from a distance distribution data. The objects are extracted by k-means clustering method based on fuzzy inference. To classify them, fuzzy if-then rules and fuzzy membership functions are derived from the knowledge of human characteristics. The system classifies them to toddler, child, adult and the other object such as baggage and animal. In the experiment, we employed seven volunteers, two dogs and a box. The system successfully classified them. © 2013, TSI® Press.

Hypoxic-ischemic encephalopathy is a typical cerebral disorder of newborn babies. For diagnosis of the neonatal cerebral disorders, the measurement of cerebral volume and surface area is effective for quantitatively evaluating the morphological change. The measurement needs brain segmentation process in magnetic resonance (MR) images. However, there are few studies for newborn brain segmentation. This study proposes an automated brain segmentation method for the newborn brain in MR images. The automated method can improve the diagnosis of the newborn cerebral disorders with efficiency and high accuracy. The proposed method first constructs fuzzy models using learning dataset. The fuzzy models express brain features by fuzzy membership functions. Next, the proposed method applies the deformable surface model based on the fuzzy models to subject's head MR images, and estimates the subject's brain region. To validate the proposed method, it has been applied to 10 newborn subjects (revised ages are - 1 month and 1 month), and compare the segmentation result with those of the conventional methods. Leave-one-out-cross validation (LOOCV) test was conducted. The mean accuracy was 93.6 ± 3.7%, the mean sensitivity was 98.9 ± 0.5, and the mean G-metrics was 4.4 ± 0.8. © 2013, TSI® Press.

Newborn brain MR image segmentation is a crucial procedure for computer-aided diagnosis of brain disorders using MR images. We have previously proposed an automated method for segmenting parenchymal region. The method is based on a fuzzy rule based deformable surface model. In order to improve the segmentation accuracy, this paper introduces a priori knowledge represented by fuzzy object radial model called FORM. The FORM is generated from learning data set, and represents knowledge on shape and MR signal of parenchymal region in MR images. The performance of the proposed method has been validated by using 12 newborn volunteers whose revised age was between -1 month and 1 month. In comparison with the previous method, the proposed method showed the best performance, and the sensitivity was 87.6 % and false-positive-rate (FPR) was 5.68 %. And, leave-one-out cross validation (LOOCV) was conducted to evaluate the robustness. Mean sensitivity and FPR in LOOCV was 86.7 % and 12.1 %. © 2013 IEEE.

Understanding a space where autonomous robots work is an open problem. In case of maritime space, it is also very important. For this purpose, marine radar has been used to acquire images around the maritime vehicles. However, radar images are easily distorted by signal attenuation with distance, blurring due to antenna directivity, reflection by obstructs, etc. There are some conventional methods to improve the image quality. However, there is a limitation due to the acquisition mechanism of radar systems. To overcome the limitation, this study shows a novel approach, which uses multiple radar images to understand the maritime space. The method estimates radar cross section (RCS) from multiple radar images by iterative image reconstruction algorithm. Performance of the proposed method is validated using actual radar images taken by marine radar system equipped on a ship. The experimental results showed that the proposed method presents a maritime space map with high image quality and without distance attenuation, sidelobe diffusion.

This paper describes optimal operator for combining left and right sole pressure data in a personal authentication method by dynamic change of sole pressure distribution while walking. The method employs a pair of right and left sole pressure distribution change data. These data are acquired by a mat-type load distribution sensor. The system extracts features based on shape of sole and weight shift from each sole pressure distribution. We calculate fuzzy degrees of right and left sole pressures for a registered person. Fuzzy if-then rules for each registered person are statistically determined by learning data set. Next, we combine the fuzzy degrees of right and left sole pressure data. In this process, we consider six combination operators. We examine which operator achieves best accuracy for the personal authentication. In the authentication system, we identify the walking persons as a registered person with the highest fuzzy degree. We verify the walking person as the target person when the combined fuzzy degree of the walking person is higher than a threshold. In our experiment, we employed 90 volunteers, and our method obtained higher authentication performance by mean and weighted sum operators. © 2013 Takahiro Takeda et al.

Cerebral parenchyma segmentation in newborn magnetic resonance (MR) images is crucial for developing computer-aided diagnosis systems in newborn cerebral diseases. However, there is limited number of studies on newborn brain MR image analysis. This study presents a novel method for fully automatically segmenting the cerebral parenchyma region using scale-based fuzzy connected image segmentation and fuzzy object models. The proposed method evaluates object affinity and homogeneity using the MR signal, and employs a fuzzy object model, which is built from training datasets. We have evaluated the proposed method based on 10 newborn MR images with subject revised age between -1 month and 2 months. These studies indicate that the use of a fuzzy object model is effective in improving the segmentation accuracy.

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.

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.

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.

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.

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.

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.

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.

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.

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).

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.

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.

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.

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).

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.

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.