Seturo Imawaki

A 3-DOF knee joint angle measurement system with inertial and magnetic sensors

Quantitative diagnosis of the knee joint dynamics is required to decrease the inner- and intra-observer variability. This paper proposes a noninvasive, unconstrained and free field of measurement system of 3 degree-of-freedom knee joint angles. The proposed system employs a compound sensor of inertial and magnetic sensors. Based on a rigid-body link model, the proposed method enables a measurement system. The experimental results showed that the proposed method estimated the flexion of knee joint angle with a mean displacement of 1.3 deg.

Ultrasonic large intestine thickness determination system for low anterior resection

We propose a thickness determination method of the intestine with the ultrasonic probe of 15 MHz. We determine the surface point by calculating correlation coefficient between surface echo and an acquisition waveform. Next, we determine the bottom point by calculating amplitude of bottom echo, correlation with surface echo and bottom echo, and interval distance between surface echo and bottom echo. Finally, we calculate the thickness between the surface and the bottom points. As the result, we have obtained the thickness within an error rate of 5.09%.

Trans-skull imaging system by ultrasonic array probe

In this paper, we propose a trans-skull imaging system of the human brain using an ultrasonic array probe. In it, we employ a cow scapula imitated to human skull and a steel with ditches imitated to cerebral sulci. We scan the phantom consisting of the bone and steel ditches by a 32channel array probe and obtain the B-mode image. From the B-mode image, we extracted the bone thickness by fuzzy inference, and visualize the ditches by filtering techniques. Experimental result shows that the mean error of bone thickness is less than 1mm and that the mean errors of the ditch width and depth are 6.9mm and 2.8mm, respectively.

Automated fuzzy logic based skull stripping in neonatal and infantile MR images

Automated morphometric analysis using human brain magnetic resonance (MR) images is an effective approach to investigate the morphological changes of the brain. However, even though many methods for adult brain have been studied, there are few studies for infantile brain. Same as the adult brain, it is effective to measure cerebral surface and for quantitative diagnosis of neonatal and infantile brain diseases. This article proposes a skull stripping method that can be applied to the neonatal and infantile brain. The proposed method can be applied to both of T1 weighted and T2 weighted MR images. First, the proposed method estimates intensity distribution of white matter, gray matter, cerebrospinal fluid, fat, and others using a priori knowledge based Bayesian classification with Gaussian mixture model. The priori knowledge is embedded by representing them with fuzzy membership functions. Second, the proposed method optimizes the whole brain by using fuzzy active surface model, which evaluates the deforming model with fuzzy rules. The proposed method was applied to 26 neonatal and infantile subjects between −4 weeks and 4 years 1 month old. The results showed that the proposed method stripped skull well from any neonatal and infantile MR images.

Unconstraint Knee Joint Dynamics Estimation System Using Inertial and Magnetic Composite MEMS Sensor

The estimation system of the knee joint angle that decreases the variability caused by the subjective diagnosis of the knee joint disorders has been attracting a considerable attention. This paper proposes an unconstraint knee joint angle measurement system using inertial and magnetic composite MEMS sensor. The proposed system estimated the posture difference between the thigh and shank region from the difference of the measurement vector between the compound sensors attached on the shank and the thigh, respectively, and 3-D knee joint angles can be calculated by applying Grood’s definition. Through experimented results, the knee flexion angle was obtained compared with the true value due to the measurement error of 1.3 deg in average.

Fuzzy Logic Assisted Quantification of Gyral Deformation Index Using Magnetic Resonance Images for the Infantile Brain

There are various cerebral diseases that deform the cerebral shape with region specificity. So it is effective to quantify the deformation change of cerebral gyri. This study introduces new index called gyral deformation index (GDI) that is defined as a ratio of area of gyrus of interest to area of cerebrum in the defined projection plane. To calculate the gyral areas, this paper proposes a gyral labeling method in the projection plane using magnetic resonance images. The new method finds the boundaries between the gyri by optimizing deformable boundary models aided by fuzzy logic. The proposed method was applied to quantify the cerebral deformation of infants on a plane which is perpendicular to the longitudinal fissure. The comparison results with the manual delineation showed that the method labels gyri with a mean sensitivity of 92.8% and a mean false positive rate of 0.1% for 14 infantile subjects (3 weeks – 4 years 3 months old).

Computer-aided diagnosis system of systems for neonatal and infantile brain using MR images

Computer aided diagnosis (CAD) system is an effective system of systems engineering (SoSE) in the medical field. The CAD system using medical images is constructed by integrating medical image acquisition system, medical image processing system, and clinical data analysis system, and it presents both of quantitative and qualitative data for physicians. This article proposes a CAD system for diagnosing neonatal and infantile brain disorders. The CAD system acquires intracranial images using MR scanner system, and segments the cerebral volume and surface using medical image processing systems, and produces cerebral volume and area, and 3-D rendering of cerebral surface. Especially, this article proposes medical image processing systems based on expert knowledge systems for neonatal/infantile brain MR images. To validate the proposed CAD system, it has been applied to three neonatal and five infantile MR images.

Fuzzy rule-based Interactive gyrus labeling for the infantile brain in magnetic resonance images

It is effective to measure the surface area of each gyrus for quantitative diagnosis of infantile brain diseases. This paper proposes an interactive gyrus labeling method for the infantile brain in magnetic resonance images. First, a user roughly gives guidelines of gyral boundaries on a 2-D projected image of the cerebral surface. The cerebral gyri are labeled by automatically determining gyral boundaries with respect to the user-given guidelines. The boundary deformation process is based on fuzzy rules. The automatically determined boundaries are validated by the user, and modified interactively. We applied the proposed method to 14 infantile subjects (3 weeks - 4 years 3 months old). The results showed that the cerebral gyri were successfully labeled with a mean sensitivity of 92.8% and a mean false positive rate of 0.1%.

2-D/3-D image registration of implanted knee DR images with Kalman filter

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. However, these methods are based on static image analysis although the knee joint continuously moves. This paper proposes an analysis method of the knee kinematics using digital radiography images with Kalman filter. Use of Kalman filter enables us to take into account the continuous knee movement. The experimental results showed that the proposed method estimated the knee joint angles within a mean error of 0.31 deg.

Brain shape homologous modeling using sulcal-distribution index in MR images

The brain shape is deformed regionally by kinds of cerebral diseases and the degree of progress. Therefore quantitative evaluation of the deformation using MR images is effective for diagnosis of cerebral diseases. To evaluate the cerebral deformation, almost conventional methods are based on normalization of the brain shape which deforms the evaluating brain into the standardized brain. Because the normalization process does not take into account anatomical features such as the cerebral sulci and gyri, in some cases the normalization process produces that one sulcus of the evaluating brain miss-corresponds to the other sulcus of the standardized brain. This paper proposes a homologous brain shape modeling method for quantitative evaluation of the brain shape in MR images. We define a new image feature called sulcal-distribution index (SDI) to represent the 3-D distribution of sulci, and the proposed method deforms a template brain model so that SDI of the deformed brain model calculated from the evaluating brain MR images is similar to SDI of the template brain model. By using SDI, the proposed method can take into account anatomical features of the cerebral sulci. The experimental results showed that the proposed method homologically modeled the brain shape with a mean displacement of 1.3 mm.