小橋 昌司

言語優位半球の判定は, 脳外科手術の術前計画や, 高次脳機能に関する研究において, 非常に重要な指標のひとつである. 従来はWada-testによる手法が用いられてきたが, 被験者に対する侵襲性等の理由から, 同手法に代わる簡便な手法が求められてきた. そこで, 本研究では小規模で非侵襲的な計測法である近赤外線分光法に基づく脳機能計測システムを用いた, 簡便な言語優位半球判定システムを提案する. 本提案法においては, 同計測システムでの計測点が言語野上に設置された度合いを示すファジィ所属度を導入することで, 計測位置のずれに対し, 高い精度, 再現性を有する手法を実現する. 本提案システムは, 14名の健常被験者に対し適用することで, 有効性を示した.

視覚機能を持ったロボットや,複合・強調現実感に関する研究が盛んである.これらの技術では,カメラの自己位置姿勢情報が必要である.本報告では,簡易なGPSをカメラの初期位置として用い,屋外にて撮影された動画像からカメラの移動軌跡を推定する方法を提案する.提案手法では,画像上の建造物の角の位置を追跡し,地図上の建造物の角の位置と対応させることでカメラ軌跡を推定する.また,地図と撮影画像の建造物の輪郭をマッチングすることで,画像における建造物や地面の正しい位置を得る.実験では,カメラ軌跡を短い処理時間で推定できることを示し,推定したカメラパラメータに対応する画像が入力画像とよく一致することを示すことで,提案手法の有効性を明らかにする.

視覚機能を持ったロボットや,複合・強調現実感に関する研究が盛んである.これらの技術の多くは,ロボットや人間の自己位置姿勢情報が必要である.また都市部を中心として精緻な地図情報が整備されてきている.本報告では,簡易なGPSを用い撮影位置付近の地図情報とカメラ画像を照合することでカメラ位置を推定する方法を提案する.とくに本方法は屋外環境に適用され,地図上の建造物外形情報を利用して簡単にカメラ位置を推定することを目的とする.画像中の建造物の接地角部分を特徴点として用い,地図上の建造物外形と撮影画像との照合を行うことで達成される.実験により,簡易GPSのみより正確なカメラ位置を安定して推定できることを示す.

Measurement of volume and surface area of the frontal, parietal, temporal and occipital lobes from magnetic resonance (MR) images shows promise as a method for use in diagnosis of dementia. This article presents a novel computer-aided system for automatically segmenting the cerebral lobes from 3T human brain MR images. Until now, the anatomical definition of cerebral lobes on the cerebral cortex is somewhat vague for use in automatic delineation of boundary lines, and there is no definition of cerebral lobes in the interior of the cerebrum. Therefore, we have developed a new method for defining cerebral lobes on the cerebral cortex and in the interior of the cerebrum. The proposed method determines the boundaries between the lobes by deforming initial surfaces. The initial surfaces are automatically determined based on user-given landmarks. They are smoothed and deformed so that the deforming boundaries run along the hourglass portion of the three-dimensional shape of the cerebrum with fuzzy rule-based active contour and surface models. The cerebrum is divided into the cerebral lobes according to the boundaries determined using this method. The reproducibility of our system with a given subject was assessed by examining the variability of volume and surface area in three healthy subjects, with measurements performed by three beginners and one expert user. The experimental results show that our system segments the cerebral lobes with high reproducibility.

人工膝関節置換手術後, 生体内の人工膝関節の動作機能評価を目的に, 従来X線透視動画像と3次元形状データを用いた2D/3D位置合わせ手法が提案されてきた.しかし, 多くの手法は膝の動作機能を考慮せず, 前フレームの推定パラメータを次フレームの初期パラメータとして解析を行っているため, 推定結果がばらついたり推定誤差が蓄積したりする問題があった.そこで本研究では, 処理するフレームの順番や膝の動作機能を考慮した3D/4D位置合わせ手法を提案する.提案手法では, キーフレームという概念を導入し, 解析を行うフレームを優先度により選択する.また, 推定値の推移が滑らかになるように制約条件を加える.精度評価を行うため, 本提案手法をシミュレーション動画像, ファントム動画像, 術後被験者の動画像に適用した結果, 従来法と比較して推定結果のばらつきは低減し, シミュレーション動画像では平均推定誤差0.037degで解析できることを確認した.

人工股関節全置換術(THA; total hip arthroplasty)後における再置換術により人体に埋め込まれたサポートインプラントにおいて, 術後の経年劣化や日常生活での荷重により生じた形状の歪み・折損は, 日常動作の妨げや痛みを与える危険性がある.そこで本論文では, 有限要素法と遺伝的アルゴリズムを用いて3次元形状データを変形させ, 生体内インプラントと最も合致する形状を効率的に探索する方法を提案する.シミュレーション実験結果より, 142.4Nの推定精度で応力を算出することができた.

This paper proposes an ultrasonic estimation system for the degradation of the insulating oil of electric power supply aided by fuzzy inference. In general, it is known that the viscosity of insulating oil depends on the acidity. We employ the viscosity as characteristic value for estimating the degradation. First, we show a strong positive correlation between the acidity and the viscosity. Second, we construct a fuzzy estimation system. Third, we infer the acidity of insulating oil by using the system. Our experimental results show that the fuzzy system can identify the acidity with the accuracy of 74% on 50 samples. Thus, this system can successfully estimate the degradation of the oil.

This paper proposes an ultrasonic estimation system for the degradation of the insulating oil of electric power supply aided by fuzzy inference. In general, it is known that the viscosity of insulating oil depends on the acidity. We employ the viscosity as characteristic value for estimating the degradation. First, we show a strong positive correlation between the acidity and the viscosity. Second, we construct a fuzzy estimation system. Third, we infer the acidity of insulating oil by using the system. Our experimental results show that the fuzzy system can identify the acidity with the accuracy of 74% on 50 samples. Thus, this system can successfully estimate the degradation of the oil.

It is difficult to reconstruct 3D shape of an object from information of a monocular image. In this paper, we propose a method for 3D shape acquisition and arbitrary view image generation of objects on the ground. By using 3D spatial information of background and dividing object's shape into simple shape such as rectangular parallelepiped and triangle pole, we achieve to acquire it from a monocular image. In some experimental results, we show that 3D shape can be simply estimated and arbitrary view images can be generated from monocular vision.

In the field of orthopedic surgery, many types of implant have been used. Especially, revision total hip arthroplasty (THA) uses a metallic implant called support implant to reconstruct the damaged acetabulum. After the revision THA, the support implant might be deformed or be broken in serious cases by the strong load or by the other factors. However, there are few effective diagnosing methods for evaluating the 3-D deformation of the support implant in vivo. This study proposes a novel approach for finding the deformation of the support implant in vivo. The proposed method uses Multidetector-row CT images to take the 3-D shape of the support implant, and employs the combination of finite element method (FEM) and genetic algorithm (GA). The new combination gives us the new application of FEM. The proposed system was evaluated by computer-simulation experiment, and the effective of the system was demonstrated by applying it to the revision THA patient.

Hypoxic ischemic encephalopathy (HIE) caused by asphyxia in the womb causes the decrement of the white matter (WM). Therefore, calculating the volume of the cerebral tissues for the infant with such symptom helps us for the purpose of quantifying the acuteness of symptom. Many methods for classifying the adult brain tissues with magnetic resonance (MR) images have been studied. However, these methods cannot be applied to classify the infantile brain tissues because the WM undergoes a myelination process in infantile brain, and the infantile brain image features are very different from adult one. This paper aims to propose a method for classifying the brain tissues in the myelination process. The proposed method addresses the intensity nonuniformity (INU) artifact by locally adapting a fuzzy spatial model of MR signals. The fuzzy model represents transition of MR signals on a line from the cerebral contour to inside the cerebrum. By using the fuzzy spatial model, the proposed method assigns fuzzy degree belonging to the cerebral cortex into voxels dependent of their locations.

Based on principals of blood oxygenation level dependent (BOLD) effect, MR signals raised according to brain neural activity. Such temporal change of MR signal is called Hemodynamic response function (HRF). Because HRF varies among activation sites and among subjects, we have been considering that estimating HRF will be available for analyzing neurological condition of cerebrum and for investigating and diagnosing cerebral diseases. Many conventional methods for analyzing functional MR images have been proposed, however, there are few methods that can detect activation areas in the cerebral cortex and can estimate HRF. This paper proposes a method that detects activation areas and estimate HRF simultaneously. The performance of estimating HRF is evaluated by phantom study, and results are compared with statistical parametric mapping (SPM).

This paper describes a design method for fuzzy ultrasonic medical system and its applications. First, we describe a design method for manipulating features of ultrasonic data. Second we apply this design method to the system identifying a surface roughness. A fuzzy inference system is designed with the average of amplitudes and the standard derivation of the echo duration for the learning waves. The system identifies the degree of roughness: one of smooth, medium and rough. In the experiments on phantoms, it has successfully identified their surface roughness. Third, we apply this design method to an location system for the screw hole position of the intramedullary nail in a bone. The screw hole position is determined by applying the fuzzy inference system. As the results, the accuracy can guarantee the clinical practice usage.

This paper describes an ultrasonic testing system with a columnar rod. The general ultrasonic probe is affected by transmission pulse for measurement using the direct contact method. However, if we use a columnar rod between an ultrasonic probe and a target object, we can measure without the transmission pulse. This paper describes the measurement system of the object thickness by the rod and fuzzy logic. The evaluation method consists of three stages. First, the surface echo position is determined from the acquisition ultrasonic wave. Second, the bottom echo position is decided by using fuzzy inference. Finally, the object thickness is calculated from the surface position and the bottom position. We applied our method to ten materials with different thickness. As the result, our method was able to evaluate the thickness of all materials within the error rate of 6.0%.

In this paper, we detect a pressure change of vital information and a heart pulse using an air pressure sensor. We could detect the heart pulse at higher S/N ratio for the various positions using an air pressure sensor. As the result for five volunteers, we could extract the heart pulse at the 1.28% error ratio. In the temporal transitional interval time of heart pulse between our method and a sphygmograph, the mean error is 4.73. This accuracy is enough to processing the heart rate monitoring in clinical usage.

The reconstruction and interpretation of 3D scene from 2D image have been studied. In this paper, we propose a method for the identification of the scene including the buildings by estimating the self-location of a camera. The scene is identified by matching the input image with the projected map corresponding to the estimated pose/position of a camera. The camera motion is dynamically estimated by using the image sequence. In this method, the corners of the building in the image are tracked by projecting the corners with the estimated camera pose/position in the previous frame. The validity of the proposed method is confirmed by evaluation of the estimated camera trajectory and the comparison of the input images with the image generated by estimated camera parameters.

Applications as visual navigation of mobile robot with image sensor and mixed/augmented reality have been investigated actively. Many of these techniques require the localization of human or robot. In this report, we propose a novel method for estimating the position of a camera by using edge and feature point information in time-series of images. The technique can be applied to the indoor environment and the environment spatial model including feature points is obtained by tracking background feature points with the position of camera. The prediction model image is generated based on trajectory of the moving camera from the spatial model and the camera position is estimated by correction of model image from comparison with the input image. Copyright - World Automation Congress (WAC) 2006,.

Development of an ultrasonic device to image the hardness of tissues is important for a cancer diagnosis. Conventional methods cannot precisely measure the size of object, because the sound speed of a material is unknown. We propose a fuzzy inference system for ultrasonic imaging based on the velocity of materials. This method consists of three stages. In the first stage, the surface of the object and the bottom are decided by using a fuzzy inference. In the second stage, we measure the thickness and the velocity of the material simultaneously by ultrasonic pulse-echo method using Multi-Direction probe. In the third stage, the velocity data and the boundary data are integrated to visualize the object. As the result, our method could visualize object shape within error rate of 6%. Copyright - World Automation Congress (WAC) 2006.

Rotator cuff tears of the shoulder are caused by bruise or age-related deterioration. They have been diagnosed by image diagnosis using magnetic resonance imaging (MRI), MR images provide the anatomical structures such as the muscle, the ligament and the bone by high resolution. However, it takes a long time and requires much effort for physicians to diagnose several tens of MR images. Additionally, physicians may overlook the disease of the shoulder because the structure of the shoulder is very complicate. To resolve these problems, we propose a method that estimates the rotator cuff by combining T1 weighted and T2 weighted MR images using fuzzy inference techniques and visualizes as 3-D image. Copyright - World Automation Congress (WAC) 2006,.

Atrophy of the human brain is observed as the progress of dementia. The atrophy occurs at the different rate on each gyrus. Estimating the atrophy for each gyrus helps us for the purpose of distinguishing diagnosis. To estimate the atrophy for each gyrus, gyri on the cortical surface should be identified. This paper aims to propose a fully automated method for identifying the gyri in three-dimensional (3-D) human brain magnetic resonance (MR) images. To clarify the effectiveness of the proposed method, it was applied to identify the superior frontal gyrus, the middle frontal gyrus, the precentral gyrus and the central gyrus. Experimental results showed that the proposed method correctly segmented the appropriate gyri. Copyright - World Automation Congress (WAC) 2006,.

Rotator cuff tears of the shoulder are caused by bruise or age-related deterioration. They have been diagnosed by image diagnosis using magnetic resonance imaging (MRI), MR images provide the anatomical structures such as the muscle, the ligament and the bone by high resolution. However, it takes a long time and requires much effort for physicians to diagnose several tens of MR images. Additionally, physicians may overlook the disease of the shoulder because the structure of the shoulder is very complicate. To resolve these problems, we propose a method that estimates the rotator cuff by combining T1 weighted and T2 weighted MR images using fuzzy inference techniques and Visualizes as 3-D image. Copyright - World Automation Congress (WAC) 2006,.

Classification of brain tissues assists for detecting brain tumors and for quantifying the cerebral atrophy. Almost of conventional methods assign the same class to voxels that have same MR signal independent of their locations. So, their methods are unsuitable for MR images with intensity nonuniformity (INU) artifact. This article proposes an automated method that locally Classifies the brain tissues by adapting a fuzzy model that represents transit of MR signals on a line that draws from the gray matter to the white matter. Also, this article evaluates and discusses the proposed method and compares with the conventional method. Copyright - World Automation Congress (WAC) 2006.

再生医療の一端として骨の欠損部分に幹細胞を注入した人工培養骨を補填し,治癒の補助を行うことが考えられている．しかし現在人工培養骨内の細胞量を測定するには培養骨を粉砕し電子顕微鏡で目視により確認するのみであるため,細胞量の測定を行った人工培養骨は臨床および実験に用いることは不可能である．そのため人工培養骨内の細胞量が生体内で与える影響についても不明となっている．そこで本研究では超音波ノギスプローブを用いて人工培養骨内の細胞量を非破壊で評価できる知的システムを提案する．波形を獲得した人工培養骨を粉砕し電子顕微鏡により確認した細胞量を真値とし,ファジィ推論による推定結果との比較を行ったところ,本手法により評価できることを示した．

A conventional ultrasonography system can noninvasively provide human tissue and blood flow velocity information with real-time processing. In general, since the human skull prevents the disclosure of brain anatomy, we usually placed the sensor at the anterior and superior attachment site of the upper ear (the posterior temporal window) in adults. Due to this limitation, the conventional system cannot obtain transcranial information from arbitrary places in the skull. This paper describes a transcranial sonography system that can visualize the shape of the skull and brain surface from any point to examine skull fracture and brain disease such as cerebral atrophy and epidural or subdural hematoma. In this system, we develop anatomical knowledge of the human head, and we employ fuzzy inference to determine the skull and brain surface. To evaluate our method, three models are applied: the phantom model, the animal model with soft tissue, and the animal model with brain tissue. In all models, the shapes of the skull and the brain tissue surface are successfully determined. Next, the method is applied to two adults. As a result, we have determined the skin surface, skull surface, skull bottom, and brain tissue surface for the subjects' foreheads. Consequently, our system can provide the skull and brain surface information using three-dimensional shapes. © 2005 IEEE.

本報告では, 略等速で移動する物体に設けられた筒状の穴に棒状のピンを挿入する自動システムを提案する.穴の位置と高さに加え, 穴の傾きを実時間で推定する.物体表面に付した複数個のマーカを観測することにより, モデルデータは用いずに推定する.マーカの3次元位置は, 時系列の単眼画像からカルマンフィルタにより推定される.また穴の傾き(面の傾き)は推定された複数マーカの3次元位置より求められる.本手法によるピン挿入の自動システムを用いて実験を行い, 略等速で移動する任意姿勢の物体に対して3次元位置・姿勢を良好に追跡できることを示す.

視覚機能を持ったロボットや, 複合・強調現実感に関する研究が盛んである.これらの技術の多くは, 人間やロボットの自己位置姿勢情報が必要である.本報告では時系列画像中のエッジ情報を用いて, カメラ位置を推定する方法を提案する.本方法は屋内の環境に適用され, 既知環境中でのカメラの軌道情報に基づき次フレームでのカメラ位置姿勢を予測する.予測した位置情報に基づき, 環境フレームモデルをエッジ抽出した入力画像へ投影し, その投影モデルを修正しながら, 抽出エッジとのマッチングを行い, カメラの3次元位置を推定する.

視覚機能を持ったロボットや, 複合・強調現実感に関する研究が盛んである.これらの技術の多くは, 人間やロボットの自己位置姿勢情報が必要である.本報告では時系列画像中のエッジ情報を用いて, カメラ位置を推定する方法を提案する.本方法は屋内の環境に適用され, 既知環境中でのカメラの軌道情報に基づき次フレームでのカメラ位置姿勢を予測する.予測した位置情報に基づき, 環境フレームモデルをエッジ抽出した入力画像へ投影し, その投影モデルを修正しながら, 抽出エッジとのマッチングを行い, カメラの3次元位置を推定する.

ロボットが物体に対し作業を行うとき, その3次元形状情報が必要となるが, 一般に物体の3次元形状復元は物体全周からの撮影により達成され, ターンテーブルを用いる方法や, 複数のカメラを全周に設置する方法等がある.しかしこれらの方法は, 特別の環境が必要なため日常生活場面での使用には適しているとはいえない.そこで本報告では, 日常生活場面での使用を目指し, 特に人をシステムに取り込み, すなわち人手を借りて物体の3次元形状を復元するシステムを提案する.物体を人手により回転させることで物体の形状情報を得る.人手により発生するズレをハンドアイカメラ画像から推定し吸収する.

視覚機能を持った移動型ロボットや, 複合現実感に関する研究が盛んである.これらの技術においてロボットや人の自己位置情報が必要である.本報告では, 壁等背景情報が既知である屋内環境でハンディ型単眼カメラを用いて, その撮像画像列の特徴点からカメラの自己位置姿勢を推定する方法を提案する.本手法は, カメラ軌道情報に基づき次フレームでのカメラ位置姿勢を予測して, 特徴点の誤検出による影響を少なくし, またその予測値に対して次フレームによる修正を行い, 効率良く3次元位置とパンチルト角を推定する.

Various methods for generating arbitrary view images from input images of cameras have been proposed These conventional methods use multiple cameras. In this paper, we propose a method to generate arbitrary view images using a single camera in known room. Especially, we perform basic experiments to apply our method to surveillance. We use one pan tilt camera. Unknown objects in known room are replaced by simple shapes such as rectangular parallelepiped, triangle pole, circular cylinder, etc. Then, by using camera parameters and 3 dimensional (3D) spatial information of the room, the 3D position and model of unknown objects are estimated, and arbitrary view images are generated. Since we use a single camera, we can easily set it in secluded spot. In experimental results, we show the effectiveness of the proposed method.

Support implant has been used to reconstruct an artificial hip joint on the acetabulum in total hip arthroplasty (TRA). After TRA, we should diagnose periodically state of the support implant because the implants may be distorted or broken. This paper proposes an in vivo evaluation method using multidetector-row computed tomography (MDCT) images. The proposed method estimates the distortion degree of the support implant by comparing the 3-D geometric model of the support implant with the support implant region segmented from the MDCT images. The support implant region is segmented from the MDCT images using a fuzzy object model which can express knowledge about the shape of objects. The distortion degree is estimated based on a multiscale matching algorithm. The performance of estimating the distortion degree was validated through computer simulation experiments, phantom experiments in vitro, and subject experiments.