In vitro recognition of HIFU-induced biological tissue degeneration based on ultrasound / 中国医学影像技术

ABSTRACT

Objective: To explore a method to improve the identification rate of tissue degeneration caused by high intensity focused ultrasound (HIFU) based on ultrasound combining with generalized regression neural network (GRNN). Methods: Totally 300 fresh isolated pork tissue samples were selected and irradiated at different HIFU doses, then 150 denatured and 150 undenatured samples were obtained. Ultrasonic images of the samples were collected before and after irradiation, then ultrasonic subtraction images were obtained. A total of 18 characteristic parameters of ultrasonic subtractive images were extracted using gray-gradient co-occurrence matrix and gray difference statistical methods, and the best characteristic vectors were obtained with P-value significance detection method and Euclidean distance method. Among 300 samples, 198 were taken as training samples and 102 as test samples. After recognition of training samples, the feature vectors eliminated with P-value significance detection method and 2 feature vectors with the smallest Euclidean distance were taken as control group of the best feature vectors, and then were input into GRNN respectively for recognition of tissue denaturation. Correct recognition rate and total recognition rate of test samples were calculated using combining feature vectors with GRNN. Results: The best feature vectors were non-uniformity of gray distribution and non-uniformity of gradient distribution, and the total recognition rate was 90.20% and 91.18% combining with GRNN, respectively, which increased to 98.04% when both 2 best characteristic parameters combined GRNN. The feature vectors eliminated using P-value significance detection method were average value and contrast, and the total recognition rate combining with GRNN was 48.04% and 75.49%, respectively, which became 79.41% when both 2 best characteristic parameters combined GRNN. The feature vectors with the smallest euclide distance were energy and small gradient, and the total recognition rate combining with GRNN was 88.24% and 89.22%, respectively, which remained 89.22% when both 2 of them combined with GRNN. The recognition rate of the optimal feature vectors combined with GRNN for tissue denaturation was significantly higher than that of control group. Conclusion: Based on ultrasonic subtraction images, of pork tissue irradiated with HIFU, non-uniformity of gray distribution and non-uniformity of gradient distribution combined with GRNN can both improve the recognition rate of tissue denaturation, while the combination of them and GRNN is more effective in identifying tissue denaturation induced by HIFU.