When it comes to feature-based image fusion, we make use of Mean-Shift to extract appropriate features according to the characteristics of Radarsat and Landsat images, then apply the Bayes theory to feature level fusion classification.

In the parameter-basedfusion methods, we analyze the applicability of the Bayes theory and Neyman-Pearson rule when they are applied to identity verification systems;

Effectively corrected short disadvantages of dynamic load balance algorithm based on Bayes theory,provide the working mechanism and the realization method of the improved algorithm.

Moreover this solution coincides with the one obtained from Bayes theory with a β prior.

The Bayes theory is used to find a quasi-optimal algorithm for filtering of a Markov sequence with a gamma distribution in the class of Y2-minimax rules based on non-Gaussian approximations for a posteriori distributions.

We used the Fourier transform to reveal important periods and chose the two strongest periods to serve as parameters to a classification system based on Bayes' theory.

Confidence measures for the class and pose estimates, derived using Bayes theory, determine when additional observations are required, as well as where the sensor should be positioned to provide the most useful information.

We begin with an outline of Bayes theory, using it to discuss well-known quantities such as priors, likelihood and posteriors, and we provide the basic Bayesian fusion equation.

Based on our ten years' experience in diagnosis and differential diagnosis of primary liver cancer (PLC), positivities of 18 parameters including history, symptoms, signs, alpha fetoprotein (AFP) level, laboratory findings, ultrasound, liversoan, etc. have been selected for differential diagnosis of the following diseases; namely subclini-cal PLC, clinical PLC, AFP negative PLC or secondary liver cancer, liver cirrhosis in active stage, liver cirrhosis with atrophy, chronic hepatitis in active stage, liver hemangioma,...

Based on our ten years' experience in diagnosis and differential diagnosis of primary liver cancer (PLC), positivities of 18 parameters including history, symptoms, signs, alpha fetoprotein (AFP) level, laboratory findings, ultrasound, liversoan, etc. have been selected for differential diagnosis of the following diseases; namely subclini-cal PLC, clinical PLC, AFP negative PLC or secondary liver cancer, liver cirrhosis in active stage, liver cirrhosis with atrophy, chronic hepatitis in active stage, liver hemangioma, hepatic cyst and liver abscess. The probability of the diagnosis was calculated using Bayes Theory and employing APPLESOFT BASIC as programming language.237 pathologically verified oases covering all of the diseases mentioned above had been testified by a 48K APPLE TYPE II microcomputer. The total accuracy of computer-aided diagnosis of 237 oases of 9 types of disorders was 91.6% as compared with pathological diagnosis. Among them 155 cases of AFP positive PLC gained the highest accuracy 99.4%, while AFP negative PLC 89.2%. The overall accuracy rata in different disorders was: liver cancer 97.4% (187/192), hepatic cyst 85.7% (12/ 14), liver hemangioma 75.0% (9/12) and liver abscess 60% (3/5). It was rather difficult to differentiate cirrhosis and chronic hepatitis from liver cancer that the accuracy was reduced to 43% (6/14) only, according to the false negative (2.6%) and false positive (4.6%) were insignificant. The overall accuracy of computer-aided diagnosis might be comparable with clinical diagnostic accuracy by high level specialists of liver caneer (90.7%). It seems that computer-aided certainly has the definite advanges in differential diagnosis, confirming the diagnosis and suggesting the proper treatment during the early stage of liver cancer.

Fourteen factors affecting survival after resection of primary hepatic cancer(PHC)were selected,including AFP levels before and after resection,type of resection,encap-sulation of tumor,presence of tumor emboli,the size,site and number of tumor,age,coexisting cirrhosis,etc.The importance of each of the 14 parameters in relation topostoperative survival was calculated by using Bayes theory,based upon the clinical dataand follow-up results of 134 cases.The prediction of survival time was programmedinto an...

Fourteen factors affecting survival after resection of primary hepatic cancer(PHC)were selected,including AFP levels before and after resection,type of resection,encap-sulation of tumor,presence of tumor emboli,the size,site and number of tumor,age,coexisting cirrhosis,etc.The importance of each of the 14 parameters in relation topostoperative survival was calculated by using Bayes theory,based upon the clinical dataand follow-up results of 134 cases.The prediction of survival time was programmedinto an APPLE (?) PLUS type microcomputer.52 cases of PHC resected during 1971-1976with complete clinical and follow-up data had been tested by the computer.Incomparison with the actual surviving time,the accuracy of computer prediction was84.6%(44/52).

It is a preliminary trial to introduce the computing technique into X-ray diagnostic work on diffuse pulmonary disease. According to the Bayes theory and multiple discriminant analysis, the authors have calculated and made the indexes table for differential diagnosis of silicosis(50), miliary tuberculosis (43) and miliary metastatic cancer (50). The diagnostic accuracy is 95.8%, which was higher than the conventional X-ray diagnosis (73.2%). The authors also used it to check up other 162 caces collected...

It is a preliminary trial to introduce the computing technique into X-ray diagnostic work on diffuse pulmonary disease. According to the Bayes theory and multiple discriminant analysis, the authors have calculated and made the indexes table for differential diagnosis of silicosis(50), miliary tuberculosis (43) and miliary metastatic cancer (50). The diagnostic accuracy is 95.8%, which was higher than the conventional X-ray diagnosis (73.2%). The authors also used it to check up other 162 caces collected later and found the diagnostic accuracy is 93.2%. Due to the simplicity of the calculations, it can be used by any medicical clinic or hospital.