We describe a new ultrasonography system, which can identify an implant position in bone. Although conventional X-ray fluoroscopy can visualize implants, it has the serious disadvantage of X-ray exposure. Therefore, we developed a system for orthopedic surgery that involves no X-ray exposure. Barriers to the development of the system were overcome using an ultrasonic instrument and fuzzy logic techniques. We located distal transverse screw holes in an intramedullary nail during surgery for femur fracture. The screw hole positions are identified by calculating two fuzzy degrees of intensity and the variance. Results allow this system to identify the screw hole positions within an error of 1.43 mm, an error ratio adequate for clinical surgical practice.
This paper proposes an automated procedure for segmenting menisci in MR images of a human knee aided by fuzzy expert system. A three-dimensional (3D) MR volumetric images composed of many slice images consists of several parts: bone marrow, meniscus, periarticular liquor, cartilage and others. We employ both T1-weighted and T2-weighted MR images to identify the menisci with high accuracy. After a registration between these images is manually done on a computer display, our procedure aided by fuzzy expert system can automatically segment meniscal regions from 3D MR images. Physicians can observe the 3D shapes of meniscal tears from any point of view on the display. We examined five subjects including a normal knee and three injured knees. The all meniscal regions were significantly identified, and these 3D shapes were displayed. The patterns of meniscal tears were identified on the display for all subjects. In a subject, since the preoperative and postoperative 3D meniscal shapes were clearly viewed, we easily recognized the operated meniscal regions. Thus, the system can provide useful information for diagnosing meniscal tears.
Rupture of anterior cruciate ligament (ACL) is a serious problem for playing sports, which causes in functional stability of the knee joint. To restore this problem, various operation techniques of ACL reconstruction are proposed. Thus, it is important to numerically characterize the knee kinematics after ACL reconstruction. Then, we proposed an analysis method to estimate the three-dimensional (3-D) knee kinematics. However, the estimation accuracy was not enough. Because the target image did not have high contrast, for example, at the boundary between the femoral bone and the tibial bone. Then, born regions can not be extracted preciously because the target image has low contrast. In this paper, we propose a fuzzy ROI (region of interests) based image registration. This method attend the region where has clear contour of bone region and ignore the region where has murky contour of bone region, by using fuzzy degree map which is assigned by the fuzzy region of interests (ROI).
The pivot shift test has been performed to assess the instability of the knee caused by anterior cruciate ligament (ACL) injuries. However, the test depends on the clinician's subjective feeling. In this study, inertial and magnetic sensors have been introduced into quantitative evaluation of the pivot shift test. The analysis method extracts the knee movement of the pivot shift by using wavelet transformation. In the result of applying the proposed method to the ACL injured subject, pivot shift phenomenon was detected correctly in comparison with reference video images taken simultaneously, the mean of the maximum accelerations of the pivot shift was 2.19±0.69 m/s2, and the maximum accelerations were correlated with grade scores based on the clinician's subjective feeling.
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%
This paper describes a testicular tubules evaluation using 1.0MHz ultrasonic array probe. In this system, we evaluate a diameter of testicular tubules. We employ an ultrasonic array probe with the center frequency of 1.0MHz. We employ evaluation index that cumulative relative frequency of amplitude values. In the experiment, we employ 24 nylon lines as the testicular tubules. Amplitude of large nylon line echo is larger than that of small nylon echo. For the evaluation, we calculate cumulative relative frequency amplitude of acquisition data. Fuzzy if-then rules are made by the cumulative relative frequency of large and small lines. We evaluate a rate of large lines among all lines by using the fuzzy MIN-MAX center-of gravity method. In this experiment, the proposed method successfully evaluated the rate of the large lines. We changed the rate of large lines in 24 nylon lines, and tested our method 20 times for each rate. We evaluated the rate with 5.77% in mean absolute error.
Three-Dimensional (3-D) shape reconstruction of total knee arthroplasty (TKA) implants in vivo plays a key role to investigate implanted knee kinematics. TKA implants typically consist of metal femoral and tibial components and a polyethylene tibial insert. X-ray computed tomography (CT) causes severe metal artifacts, making the 3-D shape in reconstructed images extremely difficult to understand. This article proposes a new method of 3-D reconstruction from X-ray cone-beam images. Called a fuzzy visual hull, it introduces fuzzy logic in recognizing X-ray images. X-ray cone-beam images are fuzzified and back-projected into a fuzzy voxel space. Defuzzifying the fuzzy voxel space enables the 3-D TKA implant shape to be reconstructed. The results of evaluation using TKA implants in vitro and computer-synthesized images demonstrated that the fuzzy visual hull provides high robustness against noise added to X-ray cone-beam images. The new approach also reconstructed the 3-D polyethylene insert despite the difficulty of recognizing the region in conventional X-ray CT.
This paper describes a method for a respiratory rate monitoring system by an air pressure sensor. By using this sensor, we propose a detection method of a respiratory rate for human in bed by fuzzy logic. Our method was examined on four healthy volunteers. We successfully detected the respiratory rate and the time of apnea state. In our method, fuzzy logic plays a primary role in the detection of respiratory points. The experimental results showed that the error ratio of respiratory rate was 1.3% and the error of time of apnea state was 1.1 seconds. Consequently, this system can noninvasively detect the respiratory rate and the time of apnea state by using an unconstrained device.
