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Biodegradable elastic scaffolds have attracted more and more attention in thefield of soft tissue repair and tissue engineering. These scaffolds made ofporous bioelastomers support tissue ingrowth along with their own degradation.It is necessary to develop a computer-aided analyzing method based onultrasound images to identify the degradation performance of the scaffold, notonly to obviate the need to do destructive testing, but also to monitor thescaffold's degradation and tissue ingrowth over time. It is difficult using asingle traditional image processing algorithm to extract continuous andaccurate contour of a porous bioelastomer. This paper proposes a jointalgorithm for the bioelastomer's contour detection and a texture featureextraction method for monitoring the degradation behavior of the bioelastomer.Mean-shift clustering method is used to obtain the bioelastomer's and nativetissue's clustering feature information. Then the OTSU image binarizationmethod automatically selects the optimal threshold value to convert thegrayscale ultrasound image into a binary image. The Canny edge detector is usedto extract the complete bioelastomer's contour. The first-order andsecond-order statistical features of texture are extracted. The proposed jointalgorithm not only achieves the ideal extraction of the bioelastomer's contoursin ultrasound images, but also gives valuable feedback of the degradationbehavior of the bioelastomer at the implant site based on the changes oftexture characteristics and contour area. The preliminary results of this studysuggest that the proposed computer-aided image processing techniques havevalues and potentials in the non-invasive analysis of tissue scaffolds in vivobased on ultrasound images and may help tissue engineers evaluate the tissuescaffold's degradation and cellular ingrowth progress and improve the scaffolddesigns.

Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications