小橋 昌司

先行研究でGyftopoulosらは腱板断裂患者の2次元MR画像から腱板3次元形状の再構築を行う方法を報告しているが、論文中に例示された腱板3次元形状は滑らかではないため、腱板の形状を忠実に再現できたとは言い難い。一方、Turkらは複数枚の断面図から物体3次元形状を再構築する方法を報告している。同手法の概要は、3次元形状を陰関数で表現し、断面間の陰関数値を放射基底関数で補間することで滑らかな3次元形状を生成するというものであり、本法を肝臓などの医用画像に応用した報告はあるが、腱板に応用した報告はみられない。そこで今回、腱板断裂例に応用し、本法によって再構築された3次元形状と実際の術中所見を比較した。結果、再構築された3次元形状は滑らか且つ自然であり、術中所見と一致した。

In this paper, we present a method of analyzing relationships between items in specific health examination data, as one of the basic researches to address increases of lifestyle-related diseases. We use self-organizing maps, and pick up the data from the examination dataset according to the condition specified by some item values. We then focus on twelve items such as hemoglobin A1c (HbA1c), aspartate transaminase (AST), alanine transaminase (ALT), gamma-glutamyl transpeptidase (gamma-GTP), and triglyceride (TG). We generate training data presented to a map by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. We label neurons in the map on condition that one of the item values of training data is employed as a parameter. We finally examine the relationships between items by comparing results of labeling (clusters formed in the map) to each other. From experimental results, we separately reveal the relationships among HbA1c, AST, ALT, gamma-GTP and TG in the unfavorable case of HbA1c value increasing and those in the favorable case of HbA1c value decreasing.

Total knee arthroplasty (TKA) is one of the common knee surgeries. Because there are some types of TKA implant, it is hard to select appropriate type of TKA implant for individual patient. For the sake of pre-operative planning, this study presents a novel approach, which predicts post-operative implanted knee function of individuals. It is based on a clinical big data analysis. The big data is composed by a set of pre-operative knee mobility function and post-operative knee function. The method constructs a post-operative knee function prediction model by means of a machine learning approach. It extracts features using principal component analysis, and constructs a mapping function from pre-operative feature space to post-operative feature space. The method was validated by applying to prediction of post-operative anterior-posterior translation in 52 TKA operated knees. Leave-one-out cross validation test revealed the prediction performances with a mean correlation coefficients of 0.79 and a mean root-mean-squared-error of 3.44 mm.

Total knee arthroplasty (TKA) is one of the common knee surgeries. Because there are some types of TKA implant, it is hard to select appropriate type of TKA implant for individual patient. For the sake of pre-operative planning, this study presents a novel approach, which predicts post-operative implanted knee function of individuals. It is based on a clinical big data analysis. The big data is composed by a set of pre-operative knee mobility function and post-operative knee function. The method constructs a post-operative knee function prediction model by means of a machine learning approach. It extracts features using principal component analysis, and constructs a mapping function from pre-operative feature space to post-operative feature space. The method was validated by applying to prediction of post-operative anterior-posterior translation in 52 TKA operated knees. Leave-one-out cross validation test revealed the prediction performances with a mean correlation coefficients of 0.79 and a mean root-mean-squared-error of 3.44 mm.

In recent years, medical institutions have very big data including medical images. The big image data analysis using the collected medical images is effective to increase the accuracy and the reproducibility of the surgery. Anterior cruciate ligament (ACL) injury causes knee joint instability, and affects on sports performance. Therefore, ACL reconstruction surgery is essential to keep their performance high and to prevent osteoarthrosis. We have proposed a MR image based pre-operative planning system of ACL reconstruction. The system manually applies the Quadrant method to the synthesized pseudo radiograph. This paper proposes a fully automated pre-operative planning system based on the clinical big image data analysis. The experimental results showed that the proposed method successfully estimated the bone tunnel opening site to insert the ACL.

© 2009 by Taylor & Francis Group, LLC. Ultrasonic techniques are widely applied in medicine. The most popular usage is to image the inside of the human body. Clinical ultrasonic treatment is also essential to disrupt objects such as gallstones. All systems consist of hardware and software. Current medical ultrasonic systems require system of systems engineering (SoSE) techniques comprising the hardware systems of an ultrasonic probe, a pulser and receiver, an A/D converter that can rapidly process large amounts of data, and the software systems of data synthesis, analysis, and image rendering. In this section, we introduce an ultrasonic SoS for clinical orthopedic surgery.

The paper introduces a proposal for an automated magnetic resonance (MR) image segmentation called Case-Based Genetic Algorithm Location-Dependent Image Classification (CBGA-LDIC) and presents its evaluation results. This method finds an appropriate cell set towards efficient image segmentation. It uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC is a local heuristic, which defines multiple location-dependent classifiers. Each classifier is trained by Gaussian mixture model. CBGA-LDIC decomposes the whole image into some cells, makes a set of cells, and then trains classifiers. The method is applied to knee bones, because these bone formations are similar in their location. Therefore, good combinations of cells are useful and stored in case bases. To show, that this method produces better results that other ones and to find optimal parameters, some experiments have been performed and their results are presented in this paper.

There are 700,000 Rheumatoid Arthritis (RA) patients in Japan, and the number of patients is increased by 30,000 annually. The early detection and appropriate treatment according to the progression of RA are effective to improve the patient's prognosis. The modified Total Sharp (mTS) score is widely used for the progression evaluation of Rheumatoid Arthritis. The mTS score assessments on hand or foot X-ray image is required several times a year, and it takes very long time. The automatic mTS score calculation system is required. This paper proposes the finger joint detection method and the mTS score estimation method using support vector machine. Experimental results on 45 RA patient's X-ray images showed that the proposed method detects finger joints with accuracy of 81.4 %, and estimated the erosion and JSN score with accuracy of 50.9, 64.3 %, respectively.

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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.

Solar activity has various influences on the global environment, in particular on the magnetic storm and the likelihood of natural disasters. Specifically, it may have serious impacts on the Earth such as failure of satellite communication and navigation (GPS), satellite damage, increased radiation exposure to astronauts, geomagnetic storm and aurora, and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, which is basically a daily forecast of the solar flare. In our work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used to forecast the space weather. Here, we propose to extend this technology by integrating Case Based Genetic Algorithm (CBGA) for a more precise forecast and present an evaluation of this approach. Experimental evaluation shows that triple mutation rate on the slowdown of evolution in our Genetic Algorithm improves considerably (e.g. another 5%) more than original mutation rate in the True Skill Statistics TSS.

In this paper, age estimation models introduced with automatic preprocessing of the T-1 weighted images, dimension reduction via principal component analysis, training of a multiple regression model, and then estimating the age of the subjects from the test samples. The regression model is automatically trained from a diverse set of 80 adult subjects (age 60-92 years) exhibiting significant variation to discover anatomical structure related to age and deformation. The methods proved to be a reliable one for age estimation in healthy subjects, yielding a correlation of r = 0.780 between the estimated and real age in the test samples and a mean absolute error of 2.155 years for PCAR method, and r = 0.834 and a mean absolute error of 2.092years for the PCA-ML method. To test the potential of these proposed age estimation models in the clinical situation, very mild to moderate Alzheimer's disease (AD) subject's age has been estimated.

Solar activity has various influences on the global environment. Specifically, it may have serious impacts on the Earth such as satellite damage, etc. and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, a daily forecast of the solar flare. In our work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used. We extended this technology by integrating Case Based Genetic Algorithm (CBGA) for a more precise forecast. It was shown experimentally that triple mutation rate on the slowdown of evolution in our CBGA improves considerably (e.g. another 5%) more than original mutation rate in the True Skill Statistics TSS. For further obtaining the optimality towards more imbalanced data analysis applicable to the recognition of serious disaster or medical disease, Restart CBGA is proposed with its expected effect. Here GA integrating SVM is restarted using highly optimized but diversified solutions in the case base as initial individuals. Further this restart CBGA is repetitively and evolutionary performed, evolving and maintaining the case base by the result of each (restarted) GA.

© 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.

It is known that lifestyle habit and genetic factor are main reasons that occur cerebral aneurysms. In addition, some studies suggest that cerebral artery shape might be correlated with a risk of occurring aneurysms. For the purpose of preemptive medical care of the cerebral aneurysm, this study proposes a method to estimate a risk of occurring cerebral aneurysms based on the cerebral artery structure. The method extracts morphometric features of the Wills ring such as 3-D artery shape and bifurcation angle in 3-D magnetic resonance angiography (MRA) images. It then estimates the risk of occurring cerebral aneurysms from the extracted features using support vector machines (SVM). To validate the proposed method, we employed 40 subjects with cerebral aneurysms, and 40 subjects without cerebral aneurysms. Leave-one-out cross validation test was performed, and the method using 3-D artery shape achieved a sensitivity of 75% and a specificity of 75%; one using bifurcation angle did a sensitivity of 33% and a specificity of 71%; one using all features did a sensitivity of 68% and a specificity of 89%. The results showed that 3-D shape is effective for cerebral aneurysm occurrence risk prediction.

Aiming at application to automated recognition of knee bone magnetic resonance (MR) images, an evolutional classification method called CBGA-LDIC is proposed. CBGA-LDIC finds an appropriate cell set towards efficient image segmentation. This method uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC introduces a new but local heuristics for image segmentation, and defines multiple classifiers dependent on location. Each classifier is trained by Gaussian mixture model. CBGA-LDIC decomposes the whole image into some cells, makes a set of cells, and then trains classifiers. Since the knee bones and/or their formations are similar in their location, good combinations of cells seem useful for other clients and are stored in case bases. Thus this method is expected to produce the better results when good combinations of cells are selected from cases as initial individuals of GA, especially through its repetition on restarting GA. This is verified by some experimentations shown in this paper.

Due to inherent characteristic of having priori-information about the shape and appearance, statistical shape models (SSMs) are considered as powerful tools in 3D-MR image analysis. Such as, the SSMs of femoral bone can be used for quantification in knee surgeries, in particular, automated segmentation of femoral bony region to be applied in computer-aided surgical planning of anterior cruciate ligament (ACL) reconstruction. This paper mainly focuses on a method of automatically determining femoral coordinate system, and also constructing SSM of the femoral bone. The coordinate system is exploited to align MR images to a base space of equal voxel size for the purpose of registration. Finally, principal component analysis (PCA) is applied on high dimensional data obtained from signed distances of the registered images. The implemented model is evaluated by reconstructing images, utilizing standard deviation (SD) ratio for each eigenvector obtained from the model.

Solar activity has various influences on the global environment. Specifically, it may have serious impacts on the Earth such as satellite damage, etc. and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, a daily forecast of the solar flare. In our work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used. We extended this technology by integrating Case Based Genetic Algorithm (CBGA) for a more precise forecast. It was shown experimentally that triple mutation rate on the slowdown of evolution in our CBGA improves considerably (e.g. another 5%) more than original mutation rate in the True Skill Statistics TSS. For further obtaining the optimality towards more imbalanced data analysis applicable to the recognition of serious disaster or medical disease, Restart CBGA is proposed with its expected effect. Here GA integrating SVM is restarted using highly optimized but diversified solutions in the case base as initial individuals. Further this restart CBGA is repetitively and evolutionary performed, evolving and maintaining the case base by the result of each (restarted) GA.

In this paper, an application of self-organizing maps (SOM's) in classifying and predicting data of female subjects with unhealthy-level visceral fat is discussed. The proposed method chooses subjects fulfilling the standard specified by body mass index and abdominal circumference. It defines the class with subjects of which hemoglobin A1c (HbA1c) values and item values associated with a liver deteriorate, that with subjects having HbA1c and triglyceride values deteriorate, and that with remaining subjects. Normal SOM learning is conducted, using data generated from original values of twelve items such as HbA1c and glutamic-oxaloacetic. The constructed map consists of neurons with labels. The label of a winner determines the class of the presented unknown data. The prediction depends on the label of a winner for the presented unknown data, a set of original data that determine the label, and a set of next year's data of the subjects with the above original data. Experimental results reveal that the proposed method achieves the reasonably favorable accuracies in classifying data and in predicting HbA1c values.

It is known that lifestyle habit and genetic factor are main reasons that occur cerebral aneurysms. In addition, some studies suggest that cerebral artery shape might be correlated with a risk of occurring aneurysms. For the purpose of preemptive medical care of the cerebral aneurysm, this study proposes a method to estimate a risk of occurring cerebral aneurysms based on the cerebral artery structure. The method extracts morphometric features of the Wills ring such as 3-D artery shape and bifurcation angle in 3-D magnetic resonance angiography (MRA) images. It then estimates the risk of occurring cerebral aneurysms from the extracted features using support vector machines (SVM). To validate the proposed method, we employed 40 subjects with cerebral aneurysms, and 40 subjects without cerebral aneurysms. Leave-one-out cross validation test was performed, and the method using 3-D artery shape achieved a sensitivity of 75% and a specificity of 75%; one using bifurcation angle did a sensitivity of 33% and a specificity of 71%; one using all features did a sensitivity of 68% and a specificity of 89%. The results showed that 3-D shape is effective for cerebral aneurysm occurrence risk prediction.

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.

Aiming at application to automated recognition of knee bone magnetic resonance (MR) images, an evolutional classification method called CBGA-LDIC is proposed. CBGA-LDIC finds an appropriate cell set towards efficient image segmentation. This method uses location-dependent image classification (LDIC), which is integrated by genetic algorithm (GA) combined with case based reasoning (CB). LDIC introduces a new but local heuristics for image segmentation, and defines multiple classifiers dependent on location. Each classifier is trained by Gaussian mixture model. CBGA-LDIC decomposes the whole image into some cells, makes a set of cells, and then trains classifiers. Since the knee bones and/or their formations are similar in their location, good combinations of cells seem useful for other clients and are stored in case bases. Thus this method is expected to produce the better results when good combinations of cells are selected from cases as initial individuals of GA, especially through its repetition on restarting GA. This is verified by some experimentations shown in this paper.

Osteoarthrosis is a kind of knee disease, and it decreases the patients' Quality of Life. The total knee arthroplasty surgery replaces the damaged knee joint with artificial knee joint. The 3-D kinematics of implanted knee is effective to diagnose and improve the function of the artificial knee joint. The still image analysis based conventional knee kinematics analysis methods do not provide the smooth knee kinematics. This paper proposes the knee kinematics analysis method using particle filter. The experimental results showed that the proposed method successfully estimated the 3-D implanted knee kinematics, and the distance transformation based new likelihood improved the applicability.

Total knee arthroscopy (TKA) is a very effective surgery for damaged knee joint treatment. Because, there are some TKA operation methods and TKA implant products, it is difficult to decide an appropriate one at the pre-operative planning. This study introduces a novel approach to assist surgeon for the pre-operative planning, and proposes a prediction method of post-operative knee joint kinematics. The method is based on principal component analysis (PCA) for characteristics extraction, and machine learning algorithms. The proposed method was validated by leave-one-out cross validation test in 46 osteoarthritis (OA) knee patients. The results show that the proposed method can predict the post-operative knee joint kinematics from the pre-operative one with a mean correlation coefficient of 0.69, and a root-mean-squared-error (RMSE) of 1.8 mm.

Anterior cruciate ligament (ACL) injury causes knee joint instability, and affects on sports performance. Therefore, ACL reconstruction is essential to keep their performance high and to prevent osteoarthrosis. It is well known that the outcome of ACL reconstruction is strongly related to the placement and orientation of the bone tunnel. 2-D X-ray radiograph and CT images have been used to evaluate the placement and orientation of the bone tunnel. Quadrant method evaluates the bone tunnel placement based on the Blumensaat's line which has high intensity on 2-D X-ray lateral radiograph. There is problem of invasiveness using X-ray radiograph or CT image. Therefore, we have proposed an MR image based computer-aided surgical planning of ACL reconstruction. The system evaluates the bone tunnel placement and orientation based on Quadrant method. The remained problem of our system is Blumensaat's line is manually determined. This paper proposes that a method to synthesize the pseudo lateral radiograph from MR images, and extract the Blumensaat's line on the synthesized pseudo lateral radiograph. The experimental results showed that the proposed method successfully determined the Blumensaat's line on the pseudo lateral radiograph.

Solar flare has various influences on the global environment, in particular on the magnetic storm and the likelihood of natural disasters. Specifically, it may have serious impacts on the Earth such as failure of satellite communication and navigation (GPS), satellite damage, increased radiation exposure to astronauts, geomagnetic storm and aurora, and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, which is basically a daily forecast of the solar flare. In the work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used to forecast the space weather. We extend this technology by integrating Genetic Algorithm (GA) elaborately combined with Case Based Reasoning for more precise forecast or imbalanced data classification. Finally, basic evaluation of this architectural idea called CBGALO shows it is promising in improving solar flare prediction.

<p>Total knee Arthroscopy (TKA) is an operation which replaces the damaged knee with an artificial knee implant. There are some kinds of TKA procedures, and various kinds of prosthesis. Thus, it becomes a tough work for surgeons to select an appropriate procedure and prosthesis for individual patients. This study proposes a prediction method of post-operative implanted knee kinematics. It predicts the post-operative kinematics from only pre-operative kinematics using a machine learning method with clinical big data. In 46 TKA subjects, the method predicts the post-operative anterior-posterior translation with a correlation coefficient of 0.77 and a root-mean-squared error of 0.7mm.</p>

To realize automated checker system of cafeteria, we have developed a meal menu recognition system. This system employs a RGB-D sensor to get both color image and depth image. From these images, first we extract and distinguish each dishes. By knowing what type of dish is used for the meal, we can reduce candidates for meal menus, it leads up to improvement of menu recognition accuracy. Some experimental result show that we can recognize dish type over 99% accuracy except for black dishes. Further, we can recognize a dish menu about 97% accuracy.

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.

Magnetic resonance (MR) images are widely used to diagnose cerebral diseases. The diseases may deform the brain shape, and the deformed region differs among types of diseases. To evaluate the brain shape deformation, MR image registration (IR) has been used. There are some IR methods for brain MR images but they mainly use MR signal based likelihood. We cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal distribution and brain shape. This paper focuses on neonatal brain MR images, and introduces a sulcus extraction method using Hessian matrix based on a feature called sulcal-distribution index (SDI). SDI is calculated from MR signal on the cerebral surface. Next, this paper proposes an iterative closest point (ICP) based brain shape registration method using the extracted sulci. The proposed method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the correspondence of cerebral sulci distribution. Results in seven neonates (modified age was between 3 weeks and 2 years) showed that the method registered one brain with the other brain successfully.

Anterior cruciate ligament (ACL) injury causes knee joint instability, and effects on sports performance. Therefore, ACL reconstruction is essential to keep their high performance. It is well known that the outcome of ACL reconstruction is strongly related to the placement and orientation of the bone tunnel. Therefore, optimization of tunnel drilling technique is an important factor to obtain satisfactory surgical results. Current procedure relies on arthroscopic evaluation and there is a risk of damaging arteries and ligaments during surgery. The damages may reduce the accuracy and reproducibility of ACL reconstruction. As a postoperative evaluation method, a quadrant method has been used to evaluate the placement and orientation of the bone tunnel in X-ray radiography. This study proposes a computer-aided surgical planning system for evaluating ACL insertion site and orientation using magnetic resonance (MR) images. We first introduce MR image based the quadrant method to determine the ACL insertion site for preoperative patients. It also evaluates the 3-D spatial relationship between the planning femoral drilling hole and arteries around the femoral condyle. This system has been applied to ACL injured patients, it may increase the accuracy and reproducibility of ACL bone tunnel, and it can evaluate a risk of damaging the surrounding arteries and ligaments. (C) 2015 The Authors. Published by Elsevier B.V.

Cerebral atrophy treated as one of the common feature of many diseases that affect the brain. In general, atrophy means that all of the brain has shrunk or it can be regional, affecting a limited area of the brain which ends up resulting in neural decrease related to functions that area of brain controls. Detection of early brain atrophy can help physicians to detect the disease at curable stage. In this paper brain atrophy with some given landmark positions has been evaluated using dimensionality reduction methods. A comparative study has been done between principal component analysis and manifold learning using Laplacian eigenmaps to quantify brain atrophy. In addition, a novel method has been proposed with combination of PCA and Manifold learning which evaluates brain atrophy with corresponding age groups. Selection of principal component scores to optimize manifold learning parameters added effective feature to the findings. The method has been applied to open database (IXI database). We applied principal component analysis to deformation maps derived from MR images of 250 normal subjects. After sampling, 42 subjects were taken whose principal component scores were used to discriminate between older subject and younger subject. We found a significant regional pattern of atrophy between distance of Anterior Commissure, Posterior Commissure, Anterior Commissure to both frontal lobe, Posterior Commissure to both frontal lobe with corresponding age. After going through T-test principal component analysis showed the best value of significant difference (p&lt;0.0036) over the Manifold learning (p&lt;0.4095). The proposed method outperformed both the dimensionality reduction method with a score of (p&lt;0.0030). Our findings indicates that multivariate network analysis of deformation maps detects typical feature of atrophy and provides a powerful tool to predict brain atrophy with age.

Solar activity has various influences on the global environment, in particular on the weather and the likelihood of natural disasters. In particular, it may have serious impacts on Earth such as failure of satellite communication and navigation (GPS), satellite damage, increased radiation exposure to astronauts, geomagnetic storm and aurora, and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, which is basically a daily forecast of the solar flare. In our work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used to forecast the space weather. Here, we propose to extend this technology by integrating a Genetic Algorithm (GA) for a more precise forecast and present an evaluation of this approach.

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.

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.

The neonatal cerebral disorders severly languish the quality of life (QOL) of patients and also their families. It is required to detect and cure in their early stage for the sake of decreasing the degree of symptoms. However, it is difficult to evaluate neonatal brain disorders based on morphological analysis because the neonatal brain grows quickly and the brain development progress is different from person to person. Previously, we proposed a method of calculating growth index using Manifold learning. The growth index is effective to evaluate the brain morphological development progress, although, it does not directly correspond to the brain development delay. To evaluate brain development delay, this paper proposes an estimation method of neonatal brain age using Manifold learning, principal component analysis, and multiple regression model. The regression model is trained using a 4-D standard brain, which is constructed using training subjects with growth index. To evaluate the proposed method, we constructed a multiple regression model using 11 normal subjects (revised age: 0-4 month old), and estimated brain age of 4 normal subjects. And, we estimated brain age of 4 abnormal subjects to evaluate the detection accuracy of brain development abnormality. The results showed that the method found the differences of brain development for abnormal subjects.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that starts in early childhood and has a comprehensive impact on psychosocial activity and education as well as general health across the lifespan. Despite its prevalence, the current diagnostic criteria for ADHD are debated. Saccadic eye movements are easy to quantify and may be a quantitative biomarker for a wide variety of neurological and psychiatric disorders, including ADHD. The goal of this study was to examine whether children with ADHD exhibit abnormalities during a visually guided pro-saccadic eye-movement and to clarify the neurophysiological mechanisms associated with their behavioral impairments. Thirty-seven children with ADHD (aged 5-11 years) and 88 typically developing (TD) children (aged 5-11 years) were asked to perform a simple saccadic eye-movement task in which step and gap conditions were randomly interleaved. We evaluated the gap effect, which is the difference in the reaction time between the two conditions. Children with ADHD had a significantly longer reaction time than TD children (p < 0.01) and the gap effect was markedly attenuated (p < 0.01). These results suggest that the measurement of saccadic eye movements may provide a novel method for evaluating the behavioral symptoms and clinical features of ADHD, and that the gap effect is a potential biomarker for the diagnosis of ADHD in early childhood.

In this paper, a simple personal identification system is presented, using data associated with corneal thickness measured by OCT (Optical Coherence Tomography). The proposed method divides the cornea into thirty two fan-shaped segments, each of which has the same area, using thirty two radiuses. It generates the thirty-two-dimensional (or sixty-four-dimensional) vector for some test subject as the registered data at some time point, and adds it to a set. The data consists of element values equal to minimum values and/or maximum values on the above radiuses. When the test subject takes medical practice, the proposed method generates thirty-two-dimensional (or sixty-four-dimensional) vector for the corresponding test subject as the collation data, and calculates the Euclidean distance between the collation data with each of the vectors in the set. It judges the test subject corresponding to the given collation data to be that of the registered data with the shortest distance to the given data. Experimental results establish that the proposed method achieves one hundred percentage as the identification rate on assumption that the number of test subjects is 30, when element values of the registered data and collation data are associated with pachymetry.

前十字靭帯(ACL;Anterior cruciate ligament)の損傷により出現した膝関節の不安定性はスポーツ成績に悪影響を与えるため，ACLを再建し安定した膝を作ることが必要である．ACL再建術の術後成績はACL挿入のための骨孔の位置と角度に強い相関があることが知られているため，骨孔開口の最適化は術後成績向上の重要な因子である．しかし，現在の術式では医師が内視鏡下で骨孔位置と角度を決定しているため，血管や靭帯を傷つける可能性があり，これがACL再建術の正確性や再現性を低下させていると考えられる．ACL再建術の術後評価法として単純X線画像を用いて骨孔位置，角度を評価するQuadrant法がある．本研究では，MR画像を用いた骨孔の位置と角度の評価を行う計算機診断支援システムを提案する．提案手法ではMR画像を用いたQuadrant法により，大腿顆周辺の血管や靭帯の3次元位置を考慮した骨孔位置を決定する．本システムをACL損傷患者に適用した結果，正確性と再現性の向上が確認できた．

In this paper, a method of analyzing relationships between items in specific health examination data is presented to cope with lifestyle-related diseases. The proposed method uses self-organizing maps, and focuses on twelve items such as hemoglobin A1c (HbA1c), glutamic-oxaloacetic transaminase (GOT), glutamic-pyruvic transaminase (GPT), gamma-glutamyl transpeptidase (gamma-GPT), and triglyceride (TG). The proposed method picks up the data from the examination dataset according to the standard specified by some item values. The training data are then generated by calculating the difference between item values associated with successive two years and normalizing the values of this calculation. The proposed method labels neurons in the map by using item values of training data as parameters, and examine the relationships between items in the examination data by observing clusters formed in the map. Experimental results reveal the relationships among HbA1c, GOT, GPT, gamma-GTP and TG both in the unfavorable case of HbA1c deteriorating and in the favorable case of HbA1c being improved.

Magnetic resonance (MR) images are widely used to diagnose cerebral diseases. The diseases may deform the brain shape, and the deformed region differs among types of diseases. To evaluate the brain shape deformation, MR image registration (IR) has been used. There are some IR methods for brain MR images but they mainly use MR signal based likelihood. We cannot directly apply methods for adult brain to neonatal brain because there are large differences in MR signal distribution and brain shape. This paper focuses on neonatal brain MR images, and introduces a sulcus extraction method using Hessian matrix based on a feature called sulcal-distribution index (SDI). SDI is calculated from MR signal on the cerebral surface. Next, this paper proposes an iterative closest point (ICP) based brain shape registration method using the extracted sulci. The proposed method will be effective for neonatal brain in which the accurate delineation of cerebral surface is difficult because the method evaluates the correspondence of cerebral sulci distribution. Results in seven neonates (modified age was between 3 weeks and 2 years) showed that the method registered one brain with the other brain successfully.

The human brain atrophies with aging. By investigating the morphological change of brain structure and comparing with the deformation of normal aging, we can diagnose the cerebral diseases such as Alzheimer's diseases, Perkinson disease, etc. Magnetic resonance imaging (MRI) is a good diagnostic tool because it is non-invasive to the human body, and it can take the thin-sectional images with high contrast. This paper shows a method to investigate the morphological structure of the human brain using MRI. The method segments the brain region, liner and non-liner registration to a standard brain, and inverse wrapping from the standard brain. By giving some landmarks to the standard brain, we can obtain the landmark position of each subject. We investigate the morphological change using the position changes. The method has been applied to open database (IXI dataset) that contains 619 subjects whose age is 19.98-83.62 years old.