To realize a quantitative diagnosis method of liver fibrosis, we have been developing a modeling method for the probability density function of the echo amplitude. In our previous model, the approximation accuracy is insufficient in regions with hypoechoic tissue such as a nodule or a blood vessel. In this study, we examined a multi-Rayleigh model with three Rayleigh distributions, corresponding to the distribution of the echo amplitude from hypoechoic, normal, and fibrous tissue. We showed quantitatively that the proposed model can model the amplitude distribution of liver fibrosis echo data with hypoechoic tissue adequately using Kullback–Leibler (KL) divergence, which is an index of the difference between two probability distributions. We also found that fibrous indices can be estimated stably using the proposed model even if hypoechoic tissue is included in the region of interest. We conclude that the multi-Rayleigh model with three components can be used to evaluate the progress of liver fibrosis quantitatively.

In our previous study, we proposed the multi-Rayleigh model as an amplitude distribution model of fibrotic liver, and succeeded in the quantitative evaluation of liver fibrosis in the region of interest. In this paper, to evaluate liver fibrosis more accurately, the amplitude of each pixel in a clinical echo image was converted to hypoechoic and fibrotic probabilities using the multi-Rayleigh model. Clinical echo images of liver fibrosis were analyzed and the relationship between these probabilities and the stage of liver fibrosis were discussed. We also showed that the information on fibrotic tissue can be extracted more accurately using the fibrotic probability than using the conventional method based on the constant false alarm rate (CFAR) processing. We conclude that the proposed method is valid for the quantitative diagnosis of liver fibrosis.

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We propose a novel intelligent wheelchair based on the passive robotics. Our proposed assistive wheelchair consists of a frame, casters, wheels and servo brakes. Our wheelchair system estimates the trajectory its user wants using the characteristic of the row motion and realizes the estimated tracks by controlling a torque of its servo brake. Our system requires no actuators, and its mechanism is simple and low cost. There is no risk by malfunction of servomotors and patients can use it intuitively because they use our wheelchair passively with their own intentional force. Our key ideas are two topics. One is the development of a passive-type assistive wheelchair which is suitable for practical use. The other key topic is a novel driving assistance algorithm with estimation of its user's intention. For realizing this estimation, we use a minimum jerk trajectory model, which expresses a typical human movement. Our proposed system compares a beginning part of row motion by the user and this trajectory model, and estimates a whole row motion which will be operated. Using our proposed system, the user can drive our wheelchair with a natural feeling. We test our proposed assistance system by the experiments with our prototype and verify its effectiveness.