  Biodegradable elastic scaffolds have attracted more and more attention in the
field of soft tissue repair and tissue engineering. These scaffolds made of
porous bioelastomers support tissue ingrowth along with their own degradation.
It is necessary to develop a computer-aided analyzing method based on
ultrasound images to identify the degradation performance of the scaffold, not
only to obviate the need to do destructive testing, but also to monitor the
scaffold's degradation and tissue ingrowth over time. It is difficult using a
single traditional image processing algorithm to extract continuous and
accurate contour of a porous bioelastomer. This paper proposes a joint
algorithm for the bioelastomer's contour detection and a texture feature
extraction method for monitoring the degradation behavior of the bioelastomer.
Mean-shift clustering method is used to obtain the bioelastomer's and native
tissue's clustering feature information. Then the OTSU image binarization
method automatically selects the optimal threshold value to convert the
grayscale ultrasound image into a binary image. The Canny edge detector is used
to extract the complete bioelastomer's contour. The first-order and
second-order statistical features of texture are extracted. The proposed joint
algorithm not only achieves the ideal extraction of the bioelastomer's contours
in ultrasound images, but also gives valuable feedback of the degradation
behavior of the bioelastomer at the implant site based on the changes of
texture characteristics and contour area. The preliminary results of this study
suggest that the proposed computer-aided image processing techniques have
values and potentials in the non-invasive analysis of tissue scaffolds in vivo
based on ultrasound images and may help tissue engineers evaluate the tissue
scaffold's degradation and cellular ingrowth progress and improve the scaffold
designs.
