畑 豊

© 2016 IEEE. In the infertility treatment, medical examinations using ultrasonic devices, which can diagnose the mother's body safely on real time, are major. It is very difficult to judge the existence of an ovum before carrying out the paracentesis. A system which can distinguish the existence of the ovum in the ovarian follicle using ultrasonic devices is required. In this paper, several features of the deformation of ovarian follicle are defined and extracted from the ultrasonic moving image in a paracentesis operation. These features are extracted from the ultrasonic moving image obtained at the time of an ovum extraction operation. We investigate whether some clusters according to the existence of the ovum are formed using the defined features. Moreover, we also investigate whether some tendency exists in these features by the existence of an ovum.

To promote effective guidance by health checkup results, this paper predict a likelihood of developing lifestyle-related diseases from health check data. In this paper, we focus on the fluctuation of hemoglobin A1c (HbA1c) value, which deeply connected with diabetes onset. Here we predict incensement of HbA1c value and examine which kind of health checkup item has important role for HbA1c fluctuation. Our experimental results show that, when we classify the subjects according to their gender and triglyceride (TG) fluctuation value, we will effectively evaluate the risk of diabetes onset for each class.

This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on genderage, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. We show a possibility that we could prevent the lifestyle disease by controlling BMI.

© 2016 TSI Enterprise Inc (TSI Press). This paper proposes a fatigue level estimation method using mat type load distribution sensor. Fatigue level indicates human condition of daily living. And estimated fatigue level is used for notification and suggestion by robot partner through informationally structured space. The sensor measures sole pressure distribution. To analysis the gait, gait speed, step duration, balance abilities and forces are gotten from gait pressure data0. And, fuzzy inference estimates fatigue level with personalized fuzzy membership functions. In experiment, the system was tested by three months' gait pressure data.

© 2016 IEEE. This paper analyzes the relationship between the change of Body Mass Index (BMI) in one year and the medical examination value of a specific health checkup. To determine this, we divided subjects into 13 groups by BMI changes. We calculated the variation in one year in each group and classify this variation into gender, age, and obesity levels. Our result shows men tend to be more susceptible from variation of BMI. For low and high BMI, some inspection items have the different relationships to change as other items. Some relationships are different depending on gender, age, and obesity levels.

© 2016 IEEE. It is difficult to observe the movement of immune cells in vivo deep. However, our recent study, by using 11.7 T magnetic resonance imaging (MRI), shows that it has become possible to observe the macrophages in living brain of mouse. Macrophages are a type of immune cell. In this paper, we propose a three dimensional tracking method of macrophages in the 11.7 T time lapse MR images and consider the application of machine learning for the detection of macrophages. This method was applied to a stroke model mouse. The result showed that we are able to track the macrophages in three dimensions. We applied Support Vector Machine (SVM) for the detection of macrophages. The proposed method and the two-dimensional tracking method were applied to the artificial data. The results showed that SVM have a good success to detect macrophages.

© 2015 IEEE. Immune cells deeply affect human health, however, it has not been investigated well due to difficulty of observing immune cells in vivo. The recent study enables us to acquire single Macrophage, which is the representative cell of immune cells, in vivo using 11.7 T magnetic resonance imaging (MRI). To quantify the kinematics of macrophages, it requires detection and tracking macrophages in MR image. The kinematic analysis will help researchers to investigate the mechanism of autoimmune diseases. This paper proposes an automated single macrophage tracking method in mouse brain 11.7 T time-lapse MR images. The method detects macrophages using background subtraction algorithm, and tracks macrophages using the Hungarian algorithm. The results showed that the proposed method detected and tracked macrophages in MR images successfully.

© 2015 IEEE. To prevent lifestyle diseases, this paper studies disease prediction using periodical health checkup data, daily monitoring to maintain healthy condition, and early life disease detection with medical imaging. To analyse periodical health checkup data, three approaches are introduced. The first approach is based on fuzzy set. It converts all attributes of health checkup data into fuzzy degrees by defining fuzzy membership functions. It enables us to manipulate all attributes in the same scale. The second approach analyses relationships between attributes of specific health examination data to cope with lifestyle diseases. It uses self-organizing maps, and clarifies the relationships among hemoglobin A1c (HbA1c), glutamic-oxaloacetic transaminase, glutamic-pyruvic transaminase, gamma-glutamyl transpeptidase, and triglyceride. The third approach predicts HbA1c fluctuations using decision tree. If we can predict the fluctuation, we can extract knowledge about what element will trigger developing diabetes. Through our examination, BMI will be the largest influencer about HbA1c fluctuations. Daily understanding of own condition is the first step of maintaining our health. A MEMS-based small and flexible monitoring device has been developed by the ERATO Maenaka human-sensing fusion project. We propose a condition estimation method using the monitoring device and FNN-based condition estimation. Experimental results show that it is a promising method for condition understanding. Cerebral vascular disease is one of major lifestyle diseases, and is caused by cerebral aneurysms. To predict the diseases, we should analyse cerebral arteries and aneurysms using magnetic resonance angiography images. This paper introduces an automated analysis method for early detection of aneurysms.

© 2015 IEEE. This paper describes a framework of blood pressure variation factor analysis. We design a system in order to support improving our lifestyle habit. The system classifies subjects into several variation groups in order to evaluate a personal characteristic of blood pressure change. Furthermore, we describe a blood pressure seasonal variation evaluation method by using normalized cross-correlation coefficient between blood pressure and outdoor temperature. As the result, we showed the dependencies among blood pressure, body weight and the temperature.

© 2015 IEEE. This paper proposes a method for predicting human posture by thermal array sensors. In our previous work, we studied a system for estimating human posture by the sensors. The system successfully estimated human posture for elderly people living in a nursing home. As a further step to our previous work, this paper presents a human posture prediction method using Bayesian inference with the sensors. Our method predicts that posture changes at the next sample with Bayesian inference which employs characteristics of a past posture. In the experiment, we applied the results of our previous work and synthesized data of assumed daily movements. In conclusion, we obtained the basis of an effective posture prediction method using Bayesian inference and a thermal array sensor network.

<p>This paper describes a gas consumption monitoring system with a conventional camera. In it,this system captures the image and extracts the meter area. Next, it recognizes the number by fuzzy inference. Finally, it evaluates the gas consumption by interpolation of the lacked or unsuccessfully recognized numbers. The experimental result shows that this system successfully identified the gas consumption in the various conditions and shows high recognition rate in 24 hours measurement in real situation. These facts show the system is available for elderly home alone to care the life through the gas consumption monitoring.</p>

© 2016 - IOS Press and the authors. In this paper, broadband ultrasonic imaging is presented to check the diameters of tubules. The proposed method applies continuous wavelet transform instead of short-time Fourier transform, and hence overcomes both issues of spatial resolution and frequency resolution. The proposed method can visualize positions of test objects more clearly than the previous work.

© 2016 - IOS Press and the authors. This paper first describes the parameters of normal gait from the center of the foot pressure (CoP). In our experimental system, we obtain the foot bottom pressure distribution data by using load distribution sensor. After that, we calculate CoP by image processing and evaluate the movement of CoP by the parameters of normal gait. In this study, we employed 153 healthy subjects and acquired their foot bottom pressure distribution while walking. Next, we applied the normal gait parameters to one Down's syndrome child in 2 cases; with insoles and barefoot. As the results, we confirmed that the parameters of the child was improved by using insoles.

© 2016 - IOS Press and the authors. Chronic thromboembolic pulmonary hypertension (CTEPH) is one of the lung diseases caused by thrombi, which occurs in pulmonary arteries. By measuring a size of region dominated by arterial subtree which has thrombi, physicians find a higher treatment effect point. This paper proposes an automated method to extract the lung region dominated by an arterial subtree from MDCT images. The method extracts an arterial subtree associated with a seed point and a region dominated by the extracted arterial subtree. And visualizes them. The results show a clinical ability of visualization and extraction of dominant region from MDCT Images.

© 2016 - IOS Press and the authors. This paper describes an air-coupled ultrasound measurement system to evaluate activities of inner muscle. The system measures inner muscle with lower constrain than conventional method by using MR image, X-ray CT and contacted ultrasound. In generally, sound intensity of air-coupled ultrasound is too small to measure it. To measure the small signal, the system employs a pulsar-receiver with high sensitive pre-amplifier, and wave detection method based on fuzzy inference finds transmitted wave from noisy received wave. The fuzzy inference is derived from characteristics of transmitted wave such as amplitude, frequency, similarity and traveling time. In the experiment, we evaluate the accuracies of wave detection method for human body.

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.

© 2014 TSI Press. This paper describes an estimating system of human numbers for resolution change by a thermal array sensor. In this system, the sensor is attached to ceiling of a room and acquires temperature distributions by 16 × 16 elements. The temperature distributions represent a state of temperature in the room, and they are analyzed to estimate human numbers. Temperature distributions consist of 16 × 16 pixels. The, temperature distributions are squared of each pixel temperature and reduced their resolution to 8 × 8 and 4 × 4. Then, human numbers are estimated by fuzzy inference. In our experiment, we measured temperature distribution in a room to evaluate of our system. From the results, the proposed method obtained higher accuracy than previous work in 8 × 8 and 4 × 4 resolution. Estimation accuracy of 8 × 8 resolution is better than 4 × 4 resolution.

© 2014 TSI Press. 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.

© 2014 TSI Press. 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 macrophages 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.

© 2014 TSI Press. This paper describes an evaluation method based on fuzzy set for health checkup data. This method converts health data into fuzzy degree to operate a multivariate data analysis. The obtained fuzzy degree is considered as an attribute value in closed interval [0, 1]. The degree shows a normality of health condition. In this study, fuzzy membership functions are made from standard divisions of reference interval of health checkup data. As an example, we calculated fuzzy degrees and disease index from Japanese health checkup data. In this result, we confirmed that the obtained disease index corresponded with medical established theories.

© 2014 IEEE. To develop a computer-aided diagnosis system for neonatal cerebral disorders, some literatures have shown atlas-based methods for segmenting parenchymal region in MR images. Because neonatal cerebrum deforms quickly by natural growth, we desire an atlas growth model to improve the accuracy of segmenting parenchymal region. This paper proposes a method for generating fuzzy object growth model (FOGM), which is an extension of fuzzy object model (FOM). FOGM is composed of some growth index weighted FOMs. To define the growth index, this paper introduces two methods. The first method calculates the growth index from revised age. Because the growth index will be different from person to person even through the same age, the second method estimates the growth index from cerebral shape using Manifold learning. To evaluate the proposed methods, we segment the parenchymal region of 16 subjects (revised age; 0-2 years old) using the synthesized FOGM. The results showed that FOGM was superior to FOM, and the Manifold learning based method gave the best accuracy. And, the growth index estimated with Manifold learning was significantly correlated with both of revised age and cerebral volume (p<0.001).

This paper gives a survey of intelligent computational techniques in medical and health care system. First, we briefly describe diagnosable techniques in medical image processing. Next, we demonstrate two ultrasonic surgery support systems for orthopedic and rectum cancer surgeons. In them, intelligent computational technique plays a primary role. Third, computational techniques are introduced in human health care system. Usually, this goal is not to apply clinical treatment but to home use to pay consciousness to health. In it, a simple ECG and respiration meter are introduced with a mat sheet which detects heart rate and respiration. Finally, a medical big data application is introduced, that is, body weight prediction is shown based on autoregressive model. Thus, we show that intelligent computing is effective and essential in modern medical and health care system. Copyright © 2014 The Institute of Electronics, Information and Communication Engineers.

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.

© 2014 IEEE. This paper describes a falling detection system by using two thermal array sensors. In the system, the sensors are attached to the ceiling and wall in a room, and acquire temperature distributions by each 16 × 16 pixels. The temperature distributions represent a state of temperature in the room, and they are analyzed to detect human falling. The falling is detected by time-series posture transition diagram and the sum of temperature. In our experiment, we measured the temperature distributions in a room modeled as private room in a nursing home. As the results, the system successfully detected the falling.

To develop a computer-aided diagnosis system for neonatal cerebral disorders, some literatures have shown atlas-based methods for segmenting parenchymal region in MR images. Because neonatal cerebrum deforms quickly by natural growth, we desire an atlas growth model to improve the accuracy of segmenting parenchymal region. This paper proposes a method for generating fuzzy object growth model (FOGM), which is an extension of fuzzy object model (FOM). FOGM is composed of some growth index weighted FOMs. To define the growth index, this paper introduces two methods. The first method calculates the growth index from revised age. Because the growth index will be different from person to person even through the same age, the second method estimates the growth index from cerebral shape using Manifold learning. To evaluate the proposed methods, we segment the parenchymal region of 16 subjects (revised age; 0-2 years old) using the synthesized FOGM. The results showed that FOGM was superior to FOM, and the Manifold learning based method gave the best accuracy. And, the growth index estimated with Manifold learning was significantly correlated with both of revised age and cerebral volume (p &lt; 0.001).

This paper describes an ultrasonic system that estimates the cell quantity of an artificial culture bone, which is effective for appropriate treat with a composite of this material and Bone Marrow Stromal Cells. For this system, we examine two approaches for analyzing the ultrasound waves transmitted through the cultured bone, including stem cells to estimate cell quantity: multiple regression and fuzzy inference. We employ two characteristics from the obtained wave for applying each method. These features are the amplitude and the frequency; the amplitude is measured from the obtained wave, and the frequency is calculated by the crossspectrum method. The results confirmed that the fuzzy inference method yields the accurate estimates of cell quantity in artificial culture bone. Using this ultrasonic estimation system, the orthopaedic surgeons can choose the composites that contain favorable number of cells before the implantation. Copyright © 2014 The Institute of Electronics, Information and Communication Engineers.

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.

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. © 2013 IEEE.

This paper proposes YURAGI synthesis for brain imaging under the skull. The advantage of the proposed method over conventional methods is that, using YURAGI synthesis, it is possible to obtain the effective results without image registration. Image registration is generally needed when more than two images are to be synthesized into one image. YURAGI synthesis does not need image registration; thus, its method is simpler than other methods that need image synthesis. The effectiveness of the proposed method was confirmed by comparing its error rate and accuracy with those of other methods. YURAGI leads the simple and energy-saving system with performing autoregulation. Autoregulation is utilized in many biological systems. In this study, YURAGI was applied to an ultrasound-based diagnostic medical imaging technique. The experimental results using YURAGI were superior to those using othermethods. Thus, YURAGI is useful for visualizing the human brain.

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 pecimen 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 ntensities 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. © 2013 IEEE.

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 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. © 2013 IEEE.

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. © 2013 IEEE.

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. © 2013 IEEE.

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. © 2013 IEEE.

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.

This paper describes a autonomy walk evaluation method based on fuzzy logic for rehabilitation field. The method acquires dynamic foot pressure distribution change while walking by mat type load distribution sensor. To evaluate the walk, four gait features such as gait speed, time of double support, gait balance and total time of stance phase and are extracted from the foot pressure data. From these features, the method derives eight fuzzy if-then rules and fuzzy membership functions. These functions are learned by the foot pressure of commons and patients. A gait level index is estimated by fuzzy degree. In experiment, we employed ten patients and ninety commons, and took their pressure distribution data. To evaluate our method, we classified them to Patient or Common by their gait level indexes. We compared our method with support vector machines. The proposed method obtained higher classification rate. © 2013 IEEE.

This paper describes an ultrasonic imaging technique for seminiferous tubules in testicle. An image is made by particular frequency data derived by short-time Fourier transform. Thus, the image has particular frequency components. The image indicates an object that has a frequency component being different from others. In our experiment, we employ two kind nylon lines with different diameter as seminiferous tubules. For a performance test, we make a measurement object consisting of the nylon lines. For a phantom test, we make a phantom of a testicle. The phantom consists of a water filled rubber tube including the nylon lines. We scan and acquire ultrasonic reflection wave data of them. Next, we make images of them by our imaging technique. As the results, the image of the object indicated the line depth with high accuracy, and the image of the phantom successfully extracted echoes of two kind lines. © 2013 IEEE.

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) / β-tricalcium phosphate (β-TCP) and composes the ultrasonic cell quantity determination on frequency domain for the BMSCs / β-TCP composites after being cultured: 4 types BMSCs to 24 β-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 / β-TCP composites have the prominent osteoconductive activity and the potential for applications/approaches in future regenerative medicine. © 2013 IEEE.

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. © 2013 IEEE.

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. © 2012 IEEE.

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. © 2012 IEEE.

This paper describes a fuzzy estimation system on gait independence level by footprint dynamics. We use a load distribution sensor and obtain a sole pressure distribution from ten inpatients. From that date, we extract feature values of gait. We make fuzzy membership functions from those values and calculate fuzzy degree for each feature value. We calculate fuzzy degree of gait as the sum of what multiplied fuzzy degree for each feature value to the coefficient. Furthermore, we estimate the gait independence level from fuzzy degree of gait by setting a threshold. In the result, we could separate inpatients for each gait independence level exactly except two cases. The result of this research suggests that we have to consider not only the physical state but also the psychological state and so on. © 2012 IEEE.

This paper describes a visualization technique to analyze causalities among the productivity indices and energy for improving energy efficiency of factory equipment. Recently, energy-saving has been important worldwide. Especially, energy-saving for factories is very important in manufacturing. Meanwhile, productivity indices must be kept in manufacturing process. Thus, we realize the improvement of energy efficiency on factory equipment by adding our visualization technique to conventional Factory Energy Management System. Our visualization technique quantifies the operational condition of equipment by the energy consumption and the equipment behavior. As the result of the visualization in our factory, our proposed technique could successfully improve the energy efficiency on a molding machine, a press machine and compressors without a negative effect to the productivity that means production volume and supply pressure. © 2012 IEEE.

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). © 2012 IEEE.