Objectives To study the reliability,validity and feasibility of the Chinese version of memory alteration test(M@T).Methods Cross-sectional survey with a convenience sample was employed to interview 220 elderly people over 60 years old,39 patients with mild cognitive impairment(MCI),20 with Alzheimer's disease(AD),and 161 normal cognitive elderly.The survey was,then evaluated with internal consistency,content validity,criterion validity,principal component/factor analysis and influencing factors.Results A Cronbach's α coefficient of 0.818 was obtained in M@T. The correlation coefficients which were the score of the subtest and the total were 0.5-0.9.The correlation coefficient of the scores of the Mini.mental State Examination(MMSE)and the M@T Was 0.933.The 5 factors were extracted with the factor analysis,which could explain the total variance of 69.449%,and the corresponding factors of the proieets have a satisfied amount of factor loading(≥0.4).There were significant diffeFences in the score of M@T among the different cognitive level groups with good discriminant validity(cognitive normal group:39.0±3.7,MCI group:29.0±3.7,AD group:16.9±3.7;F=498.419,P<0.05).There were no significant differences in the score of M@T among the different gender,age,occupation and education level groups.Conclusions The Chinese version of M@T has good reliability and validity and feasibility.The score of the M@T is not affected by gender,age,occupation,education level and other factors.

We propose an improved version of regional competition algorithm in this paper, and apply it to the automatic segmentation of medical image series, particularly in the segmentation and recognition of brain tumor. The traditional regional competition is enhanced by combining the attractive aspects of fuzzy segmentation, and thus it provides an efficient approach to segment the fuzzy and heterogeneous medical images. In order to perform regional competition on medical image series, we utilize the segmentation result of a slice to initiate the next segmented slice, while the first slice is initialized using regional growing algorithm. Moreover, we develop an algorithm to recognize the tumors automatically, taking into account its characters. Experimental results show that our algorithm performs well on the segmentation of magnetic resonance imaging (MRI) image series with high speed and precision.
Algorithms ; Brain Neoplasms ; diagnosis ; Fuzzy Logic ; Humans ; Imaging, Three-Dimensional ; methods ; Magnetic Resonance Imaging ; Pattern Recognition, Automated