ABSTRACT
A Pythagorean fuzzy set is the successful ?eld which includes membership and non membership functions. It has been extended from intuitionistic fuzzy set. It reaches many application with the support of score, Accuracy, Distance and Similarity measures. In this paper, cosine similarity measure is used with Pythagorean fuzzy set. An algorithm is developed for proposed method. An illustrative example is included. Comparison is also made with Score, Accuracy and Similarity measure function.
ABSTRACT
Objective:To propose a method of image similarity measurement based on structure information and intuitionistic fuzzy set and measure the similarity between CT image and CBCT image of radiotherapy plan positioning, aiming to objectively measure the setup errors.Methods:A total of four pre-registration images of a nasopharyngeal carcinoma patient on the cross-sectional and sagittal planes and a pelvic tumor patient on the cross-sectional and coronal planes were randomly selected. Five methods were used to quantify the setup errors, including correlation coefficient, mean square error, image joint entropy, mutual information and similarity measure method.Results:All five methods could describe the deviation to a certain extent. Compared with other methods, the similarity measure method showed a stronger upward trend with the increase of errors. After normalization, the results of five types of error increase on the cross-sectional plane of the nasopharyngeal carcinoma patient were 0.553, 0.683, 1.055, 1.995, 5.151, and 1.171, 1.618, 1.962, 1.790, 3.572 on the sagittal plane, respectively. The results of other methods were between 0 and 2 after normalization, and the results of different errors of the same method slightly changed. In addition, the method was more sensitive to the soft tissue errors.Conclusions:The image similarity measurement method based on structure information and intuitionistic fuzzy set is more consistent with human eye perception than the existing evaluation methods. The errors between bone markers and soft tissues can be objectively quantified to certain extent. The soft tissue deviation reflected by the setup errors is of significance for individualized precision radiotherapy.
ABSTRACT
Metabolic network alignments enable comparison of the similarities and differences between pathways in two metabolic networks and help to uncover the conserved sub-blocks therein. Such analysis is important in the understanding of metabolic networks and species evolution. The fundamental parts of metabolic network alignment algorithms all involve comparisons of the similarity between two enzymes as a similarity measure of network nodes. As a result, the study of methods for measuring enzyme similarity becomes highly relevant. Currently, two approaches are mainly used to measure enzyme similarity. One of the methods is based on similarity measures of gene or protein sequences; the other is based on enzyme classification. In this study, multiple metabolic network alignments were performed using both the methods. The results showed that, in general, the sequence similarity method yielded higher accuracy, especially with respect to reflecting evolutionary distances.