It is difficult to use a single edge operator in image processing to extract continuous and accurate contours of a porous bioelastomer due to the fuzzy boundary and complex background in ultrasound images.To solve thi...It is difficult to use a single edge operator in image processing to extract continuous and accurate contours of a porous bioelastomer due to the fuzzy boundary and complex background in ultrasound images.To solve this problem,this paper proposes a joint algorithm for bioelastomer contour detection and a texture feature extraction method for monitoring the degradation performance of bioelastomers.First,the mean-shift clustering method is utilized to obtain the clustering feature information of bioelastomers and native tissue from manually segmented images,and this information is used as the initial information in the image binarization algorithm for image partitioning.Second,Otsu's thresholding method and mathematical morphology are applied in the process of image binarization.Finally,the Canny edge detector is employed to extract the complete bioelastomers contour from the binary image.To verify the robustness of the proposed joint algorithm,the results using the proposed joint algorithm,where mean-shift clustering is replaced with k-means clustering are also obtained.The proposed joint algorithm based on mean-shift clustering outperforms the joint algorithm based on k-means clustering,as well as algorithms that directly apply the Canny,Sobel and Laplacian methods.Texture feature extraction is based on the computer-aided recognition of bioelastomers.The region of interest(ROI)is set in the scaffold region,and the first-order statistical features and second-order statistical features of the greyscale values of the ROI are extracted and analysed.The proposed joint algorithm can not only extract ideal bioelastomers contours from ultrasound images but also provide valuable feedback on the degradation behaviour of bioelastomers at implant sites.展开更多
Hydrophilic photoluminescent CdTe/poly (1, 4-butanediol-citrate) (PBC) bioelastomer nanocomposite was successfully synthesized by a two-step method and characterized by X-ray diffraction (XRD), Fourier trans- fo...Hydrophilic photoluminescent CdTe/poly (1, 4-butanediol-citrate) (PBC) bioelastomer nanocomposite was successfully synthesized by a two-step method and characterized by X-ray diffraction (XRD), Fourier trans- form infrared (FT-IR) spectroscopy, Uv-vis spectroscopy, photoluminescence (PL) spectroscopy and scanning elec- tron microscope (SEM). The differential scanning calori- metry analysis shows that the bioelastomer nanocom- posites with different mass fractions of CdTe have low glass-transition temperature, which indicates that they possess elastic property in the range from room tempera- ture to the expected applied temperature (37℃). The measurements of the hydrophilicity, in vitro degradation and PL show that the nanocomposite has good hydro- philicity, degradation and high fluorescence properties.展开更多
基金supported by the National Natural Science Foundation of China(12074160)the Natural Science Foundation of Liaoning Province of China(2019-MS-219)Liaoning Revitalization Talents Program(XLYC1907034).
文摘It is difficult to use a single edge operator in image processing to extract continuous and accurate contours of a porous bioelastomer due to the fuzzy boundary and complex background in ultrasound images.To solve this problem,this paper proposes a joint algorithm for bioelastomer contour detection and a texture feature extraction method for monitoring the degradation performance of bioelastomers.First,the mean-shift clustering method is utilized to obtain the clustering feature information of bioelastomers and native tissue from manually segmented images,and this information is used as the initial information in the image binarization algorithm for image partitioning.Second,Otsu's thresholding method and mathematical morphology are applied in the process of image binarization.Finally,the Canny edge detector is employed to extract the complete bioelastomers contour from the binary image.To verify the robustness of the proposed joint algorithm,the results using the proposed joint algorithm,where mean-shift clustering is replaced with k-means clustering are also obtained.The proposed joint algorithm based on mean-shift clustering outperforms the joint algorithm based on k-means clustering,as well as algorithms that directly apply the Canny,Sobel and Laplacian methods.Texture feature extraction is based on the computer-aided recognition of bioelastomers.The region of interest(ROI)is set in the scaffold region,and the first-order statistical features and second-order statistical features of the greyscale values of the ROI are extracted and analysed.The proposed joint algorithm can not only extract ideal bioelastomers contours from ultrasound images but also provide valuable feedback on the degradation behaviour of bioelastomers at implant sites.
基金Acknowledgements This work was supported by the National Natural Science Foundation of China (Grant Nos. 21063005, 50968005 and 51163003), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No, 20091341).
文摘Hydrophilic photoluminescent CdTe/poly (1, 4-butanediol-citrate) (PBC) bioelastomer nanocomposite was successfully synthesized by a two-step method and characterized by X-ray diffraction (XRD), Fourier trans- form infrared (FT-IR) spectroscopy, Uv-vis spectroscopy, photoluminescence (PL) spectroscopy and scanning elec- tron microscope (SEM). The differential scanning calori- metry analysis shows that the bioelastomer nanocom- posites with different mass fractions of CdTe have low glass-transition temperature, which indicates that they possess elastic property in the range from room tempera- ture to the expected applied temperature (37℃). The measurements of the hydrophilicity, in vitro degradation and PL show that the nanocomposite has good hydro- philicity, degradation and high fluorescence properties.