Yeoun-Jae Kim1, Chang-Kyu Park2, Kwang Gi Kim3. 1. Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, National Cancer Center, 323 Ilsan-ro, Ilsandong-gu, Goyang-si, Gyeonggi-do, 10408, Korea. 2. Department of Ship and Ocean, Vision University of Jeonju, 1070, Hyoja-dong 2-ga, Wansan-gu, Jeonju, 560-760, Korea. 3. Department of Biomedical Engineering, Gil Medical Center, School of Medicine, Gachon University, 38 3-dockjeom ro, Namdong-gu, Incheon, 21565, Korea. kimkg@gachon.ac.kr.
Abstract
PURPOSE: The remote medical diagnosis system (RMDS) is for providing medical diagnosis to the patients located in remote sites. To apply to RMDS and medical automation, many master-slave type ultrasound scanning robots are being developed and researched. One of the important research issue of the master-slave type ultrasound scanning robot is to determine the gains of the feedback force. Therefore, in this study, we suggest a gain determination method of feedback force for a master-slave type ultrasound thyroid scanning robot using a genetic algorithm. METHOD: A master-slave type ultrasound thyroid scanning robot (NCCMSU) was constructed, and the optimal y and z direction feedback force gains were calculated for NCCMSU with genetic algorithm. The Hunt-Crossley model is used to model the elastic behavior of the thyroid phantom and the thyroid scanning procedure is embedded in genetic algorithm by modeling the procedure mathematically. The genetic algorithm solves the average feedback force-overall procedure time optimization problem to seek optimal y, z direction feedback gains candidates. RESULTS: The rating results show that although there are some deviations among the subjective ratings, the feedback force with the determined gain setting is within the appropriate range. By analyzing the subjective rating test, the optimal y, z direction feedback force gains were determined. The optimal gains were verified by thyroid phantom scanning test and the scanned ultrasound image analysis. CONCLUSION: With the proposed method, the y, z direction optimal feedback force gains of the master-slave type ultrasound scanning robots can be determined. The proposed methods were verified by thyroid phantom scanning test.
PURPOSE: The remote medical diagnosis system (RMDS) is for providing medical diagnosis to the patients located in remote sites. To apply to RMDS and medical automation, many master-slave type ultrasound scanning robots are being developed and researched. One of the important research issue of the master-slave type ultrasound scanning robot is to determine the gains of the feedback force. Therefore, in this study, we suggest a gain determination method of feedback force for a master-slave type ultrasound thyroid scanning robot using a genetic algorithm. METHOD: A master-slave type ultrasound thyroid scanning robot (NCCMSU) was constructed, and the optimal y and z direction feedback force gains were calculated for NCCMSU with genetic algorithm. The Hunt-Crossley model is used to model the elastic behavior of the thyroid phantom and the thyroid scanning procedure is embedded in genetic algorithm by modeling the procedure mathematically. The genetic algorithm solves the average feedback force-overall procedure time optimization problem to seek optimal y, z direction feedback gains candidates. RESULTS: The rating results show that although there are some deviations among the subjective ratings, the feedback force with the determined gain setting is within the appropriate range. By analyzing the subjective rating test, the optimal y, z direction feedback force gains were determined. The optimal gains were verified by thyroid phantom scanning test and the scanned ultrasound image analysis. CONCLUSION: With the proposed method, the y, z direction optimal feedback force gains of the master-slave type ultrasound scanning robots can be determined. The proposed methods were verified by thyroid phantom scanning test.
Authors: F Pierrot; E Dombre; E Dégoulange; L Urbain; P Caron; S Boudet; J Gariépy; J L Mégnien Journal: Med Image Anal Date: 1999-09 Impact factor: 8.545