Syoji Kobashi

Automated segmentation of human brain MR images aided by fuzzy information granulation and fuzzy inference

This paper proposes an automated procedure for segmenting an magnetic resonance (MR) image of a human brain based on fuzzy logic. An MR volumetric image composed of many slice images consists of several parts: gray matter, white matter, cerebrospinal fluid, and others. Generally, the histogram shapes of MR volumetric images are different from person to person. Fuzzy information granulation of the histograms can lead to a series of histogram peaks. The intensity thresholds for segmenting the whole brain of a subject are automatically determined by finding the peaks of the intensity histogram obtained from the MR images. After these thresholds are evaluated by a procedure called region growing, the whole brain can be identified. A segmentation experiment was done on 50 human brain MR volumes. A statistical analysis showed that the automated segmented volumes were similar to the volumes manually segmented by a physician. Next, we describe a procedure for decomposing the obtained whole brain into the left and right cerebral hemispheres, the cerebellum and the brain stem. Fuzzy if-then rules can represent information on the anatomical locations, segmentation boundaries as well as intensities. Evaluation of the inferred result using the region growing method can then lead to the decomposition of the whole brain. We applied this method to 44 MR volumes. The decomposed portions were statistically compared with those manually decomposed by a physician. Consequently, our method can identify the whole brain, the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem with high accuracy and therefore can provide the three dimensional shapes of these regions.

Human Health Care System of Systems

In this paper, we describe a human health management system scheme and its practical applications. Specifically, it focuses on health management, medical diagnosis, and surgical support system of systems engineering (SoSE). The application domains discussed here are broad and essential in health management and clinical practice. Firstly, we describe a system of systems (SoS) in human health management. Within it, a notion of health management is introduced and discussed from the viewpoint of SoS. Human health management is the first level of daily monitoring for a healthy human. Sensing and control technology during sleep are espectially focused on because the quality and quantity of sleep has considerable impact on health. Secondly, an SoS in medical diagnostic imaging is discussed. This section introduces a clinical usage of a magnetic resonance imaging (MRI) scanner for the diagnosis of certain diseases. In it, there is a new system that consists of image processing system and expert medical knowledge system described by fuzzy logic. To demonstrate the effectiveness of the new system, applications to human brain magnetic resonance images and orthopedic kinematic analyses are introduced. Thirdly, we describe an SoS in medical ultrasonic surgery support device. This section introduces a novel ultrasonic support system for supporting crash bone orthopedic surgery.

Volume-quantization-based neural network approach to 3D MR angiography image segmentation

Abstract Volume visualization of cerebral blood vessels is highly significant for diagnosis of the cerebral diseases. It is because the automated segmentation of the blood vessels from an MR angiography (MRA) image is a knotty problem that there are few works on it. This paper proposes an automated method to segment the blood vessels from 3D time of flight (TOF) MRA volume data. The method consists of: (1) removing the background, (2) volume quantization by watershed segmentation, and (3) classification of primitives by using an artificial neural network (NN). In the proposed method, the NN classifies each primitive, which is a clump of voxels, by evaluating the intensity and the 3D shape. The method was applied to seven MRA data sets. The evaluation was done by comparing with the manual classification results. The average classification accuracy was 80.8%. The method also showed the volume visualizations using target maximum intensity projection (target MIP) and surface shaded display (SSD). The evaluation by a physician showed that unclear regions on the conventional image were clearly depicted on applying the method, and that the produced images were quite interesting for diagnosis of cerebral diseases such as aneurysm and encephaloma. The quantitative and qualitative evaluations showed that the method was appropriate for blood vessel segmentation.

Computer-Aided Diagnosis of Intracranial Aneurysms in MRA Images with Case-Based Reasoning

Finding intracranial aneurysms plays a key role in preventing serious cerebral diseases such as subarachnoid hemorrhage. For detection of aneurysms, magnetic resonance angiography (MRA) can provide detailed images of arteries non-invasively. However, because over 100 MRA images per subject are required to cover the entire cerebrum, image diagnosis using MRA is very time-consuming and labor-intensive. This article presents a computer-aided diagnosis (CAD) system for finding aneurysms with MRA images. The principal components are identification of aneurysm candidates (= ROIs; regions of interest) from MRA images and estimation of a fuzzy degree for each aneurysm candidate based on a case-based reasoning (CBR). The fuzzy degree indicates whether a candidate is true aneurysm. Our system presents users with a limited number of ROIs that have been sorted in order of fuzzy degree. Thus, this system can decrease the time and the labor required for detecting aneurysms. Experimental results using phantoms indicate that the system can detect all aneurysms at branches of arteries and all saccular aneurysms produced by dilation of a straight artery in 1 direction perpendicular to the principal axis. In a clinical evaluation, performance in finding aneurysms and estimating the fuzzy degree was examined by applying the system to 16 subjects with a total of 19 aneurysms. The experimental results indicate that this CAD system detected all aneurysms except a fusiform aneurysm, and gave high fuzzy degrees and high priorities for the detected aneurysms.

Real-time position and pose tracking method of moving object using visual servo system

This paper proposes a real-time position and pose tracking method of a moving object. The conventional methods need a three-dimensional (3D) model data of the target object. By the proposed method, the 3D position and pose tracking can be achieved without the 3D model data. We set some landmarks to estimate the 3D position and pose of the object by a single camera. In addition, the trajectory of moving landmarks is estimated by a Kalman filter. The experimental results show that a stick with a diameter of 5.0 mm can be inserted in a moving pipe with a diameter of 30.0 mm in real-time.

Interactive segmentation of the cerebral lobes with fuzzy inference in 3T MR images

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.

Fuzzy Logic Approach to 3D Magnetic Resonance Image Segmentation

This paper proposes an approach of fuzzy logic to 3D MR image segmentation. We show a fuzzy knowledge representation method to represent the knowledge needed to segment the target portions, and apply our method to 3D MR human brain image segmentation. In it we consider position knowledge, boundary surface knowledge and intensity knowledge. They are expressed by fuzzy if-then rules and compiled to a total degree as the measure of segmentation. The degree is evaluated in region growing technique and which segments the whole brain region into the left cerebral hemisphere, the right cerebral hemisphere, the cerebellum and the brain stem. The experimental result on 36 MR voxel data shows that our method extracted the portions precisely.

From cartoon to real time MRI: in vivo monitoring of phagocyte migration in mouse brain

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.

Fuzzy ultrasonic array system for locating screw holes of intramedullary nail

In this paper, we describe an ultrasonography system for locating screw holes of intramedullary nail by one-direction freehand scanning using an ultrasonic array probe. Although conventional X-ray method can visualize the nail in the femur, it has serious problem of X-ray exposure. We propose a locating method of the nail screw holes by an ultrasonic array probe. We extract screw hole regions by calculating two fuzzy degrees: average of the intensity and variance of the intensity using fuzzy inference. Next, we do a registration between the obtained image with the true image, where the true image is the nail image obtained by scanning an array probe to the nail exactly. As the result, we could calculate the center distance of two screw holes within an error of 1.0 mm.

Fuzzy Detection System of Behavior before Getting Out of Bed by Air Pressure and Ultrasonic Sensors

In this paper, we introduce a health monitoring system by both air pressure and ultrasonic sensors. The system of these sensors can complementary detect a behavior before getting out of bed with high accuracy aided by fuzzy membership functions. In this system, the ultrasonic sensor can obtain vibration information of human by setting it the under a bed frame. The air pressure sensor can also detect a pressure change of movement of human by setting it into the mattress on the bed. By using these sensors, we construct a fuzzy system to detect a behavior before getting out of bed.