In the present paper, we propose a method for reconstructing the surfaces of objects from stereo data. Both the fitness of stereo data to surfaces and interrelation between the surfaces are defined in the framework of a three-dimensional (3-D) Markov Random Field (MRF) model. The surface reconstruction is accomplished by searching for the most likely state of the MRF model. Three experimental results are shown for synthetic and real stereo data.

We have proposed a recognition method for pulmonary nodules based on experimentally selected feature values (such as contrast, circularity, etc.) of pathologic candidate regions detected by our Variable N-Quoit (VNQ) filter. In this paper, we propose a new recognition method for pulmonary nodules by use of not experimentally selected feature values, but each CT value itself in a region of interest (ROI) as a feature value. The proposed method has 2 phases: learning and recognition. In the learning phase, first, the pathologic candidate regions are classified into several clusters based on a principal component score. This score is calculated from a set of CT values in the ROI that are regarded as a feature vector, and then eigen vectors and eigen values are calculated for each cluster by application of principal component analysis to the cluster. The eigen vectors (we call them "eigen-images") corresponding to the S-th largest eigen values are utilized as base vectors for subspaces of the clusters in a feature space. In the recognition phase, correlations are measured between the feature vector derived from testing data and the subspace which is spanned by the eigen-images. If the correlation with the nodule subspace is large, the pathologic candidate region is determined to be a nodule, otherwise, it is determined to be a normal organ. In the experiment, first, we decide on the optimal number of subspace dimensions. Then, we demonstrated the robustness of our algorithm by using simulated nodule images.

This paper describes a novel discrimination method of pulmonary nodules based on statistical analysis of thoracic computed tomography (CT) scans. Our previous Computer-Aided Diagnosis (CAD) system can detect pulmonary nodules from CT scans, but, at the same time, yields many false positives. In order to reduce the false positives, the method proposed in the present paper uses a relationship between pulmonary nodules, false positives and image features in CT scans. The trend of variation of the relationships is acquired through statistical analysis of a set of CT scans prepared for training. In testing, by use of the trend, the method predicts the appearances of pulmonary nodules and false positives in a CT scan, and improves the accuracy of the previous CAD system by modifying the system's output based on the prediction. The method is applied to 218 actual thoracic CT scans with 386 actual pulmonary nodules. The receiver operating characteristic (ROC) analysis is used to evaluate the results. The area under the ROC curve (Az) is statistically significantly improved from 0.918 to 0.931.

Low-power technology for front-end GaAs ICs and hybrid IC (HIC) for a mobile communication equipment will be presented. For low-power operation of GaAs front-end ICs, new techniques of the intermediate tuned circuits, the single-ended mixer, dualgate MESFETs, and the asymmetric self-aligned LDD process were investigated. The designed down-converter IC showed conversion gain of 21 dB, noise figure of 3.5 dB, 3rd-order intercept point in output level (IP3out) of 4.0 dBm, image-rejection ratio of 20 dB at 880 MHz, operating at 3.0 V of supply voltage and 5.0 mA of dissipation current. The down-converter IC was also designed for 1.9 GHz to obtain conversion gain of 20 dB, noise figure of 4.0 dB, IP3out of 4.0 dBm, image-rejection ratio of 20 dB at 3.0 V, 5.0 mA. The up-converter IC was designed for 1.9 GHz using the same topology of circuit and showed conversion gain of 15 dB, IP3out of 7.5 dBm, and 1 dB compression level of -8 dBm with -20 dBm of LO input power, operating at 3.0 V, 8.0 mA. Another approach to the low-power operation was carried out by HIC using the GaAs down-converter IC chip. The HIC was designed for 880 MHz to show conversion gain of 27 dB, noise figure of 3.3 dB, IP3out of 3.0 dBm, image-rejection ratio of 12 dB, at 2.7 V, 4.5 mA. The HIC measures only 8.0 mm6.0 mm1.2 mm.

In this paper, we propose a construction method of three-dimensional deformable models that represent tree-shaped human organs, such as bronchial tubes, based on results obtained by statistically analyzing the distributions of bifurcation points in the tree-shaped organs. The models are made to be used as standard templates of tree-shaped organs in medical image recognition, and are formed by control points that can be uniquely identified as structural elements of organs such as bifurcation tracheae in bronchial tubes. They can be transfigured based on the statistical validity of relationships between the control points. The optimal state of that transfiguration is determined within the framework of energy minimization. Experimental results from bronchial tubes are shown on actual CT images.

A MMIC power amplifier operating with a single-supply (3.0 V) has been developed for 5.8 GHz Japanese Electronic Toll Collection (ETC) System. The present MMIC contains two FETs, matching circuits (input, intermediate and output matching circuits), and two drain bias circuits. High dielectric constant material SrTiO3 (STO) is used for by-pass and input coupling capacitors. Very small die size of 0.77 mm2 has been realized by using the STO capacitors and negative feedback circuit technology. High 1 dB output gain compression point (P1dB) of 13 dBm, high gain of 21.4 dB and low dissipation current of 41.3 mA have been achieved under 3.0 V single-supply condition.