The paper presents a new method of characterisation of texture changes in foot sole soft tissue ultrasound (US) images, as observed to occur in diabetic subjects, using wavelet transforms. US images of the soft tissue subcutaneous layer were taken with a 7.5 MHz linear transducer probe placed parallel to the skin surface. The foot sole hardness was characterised by Shore level. A 2D discrete wavelet transform was performed on the US images to extract features that encode the internal state of the foot sole soft tissue. The global energy feature computed at the output of each wavelet channel was found to achieve excellent delineation between the normal and the diabetic groups. An important finding was a strong correlation, in the order of 0.84 and above, between the feature values that reflect changes in the internal arrangement of the tissue, and the externally measurable hardening of the skin, characterised by the Shore levels, with the latter known to be high for diabetics. A comparison drawn between diabetic ulcer and non-ulcer groups established a change in the order of 122-311% in the textural parameter, as influenced by a corresponding 66.7-200% change in the respective Shore values. Thus US examination of foot sole soft tissue and its texture analysis may serve as sources of valuable information regarding the internal changes taking place with progressive hardening of the soft tissue and thereby help the clinician in taking appropriate preventive measures.

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Biological tissues for clinical use typically require gamma irradiation to achieve targeted sterility assurance level (SAL). Gamma radiation produces deleterious changes to physical and surface properties of tissues. In this study, we evaluate the requirement of gamma irradiation as a secondary sterilization procedure by comparing it with non-irradiated chemically treated xenograft tissues. Sixty four bovine pericardia (BP) were decellularized and subjected to nonconventional (glutaraldehyde free) cross-linking. Xenograft samples were screened for bacterial and fungal contaminations both at pre-and post-processing stages, after cross-linking and preservation. Microbial evaluations performed revealed that the xenografts were rendered 'microbe free' by subjecting to a new multistaged decellularization technique and cross-linking. Five of these cross-linked tissues were subjected to gamma irradiation as recommended by IAEC and were tested for surface and mechanical properties to understand the ultrastructure, surface and bulk properties. Surface tension and thrombogenicity parameters were also evaluated. Gamma-irradiated specimen showed reduced physical and mechanical properties of these xenogenic tissues significantly along with biological property. Validation and analysis led us to conclude that this microbe-free decellularization method and subsequent processing for xenogenic tissues is a viable alternative for clinical usage without the deleterious secondary sterilization using gamma irradiation.

Typically, the region of interest ͑ROI͒, in the JPEG2000 standard, is manually defined, and then wavelets are used to compress the ROI at a higher bitrate than the rest of the image. The wavelet decomposition in JPEG2000 also lends itself to texture and edge extraction for segmentation and classification purposes. In this paper, a semiautomatic ROI generation algorithm for images is presented, where the texture and edge information provided by the first level of the wavelet decomposition is used to segment the wavelet coefficients. This firstlevel decomposition provides enough edge and texture information for image segmentation, allowing computational savings. A mask that outlines the ROI is determined based on the entropy calculation of the segmented regions. The advantage of this method is that the segmentation process is entirely performed in the wavelet and not in the pixel domain, therefore offering additional computational efficiency. The resulting ROI is coded using the MAXSHIFT method. The algorithm was applied and successfully demonstrated in several images. IntroductionRegion of interest ͑ROI͒ coding is one of the features of the JPEG2000 standard. 1 This feature allows users to define regions within an image to be coded and transmitted in better quality and with less distortion than the rest of the image. Typically, ROIs are manually defined, then wavelets are used to compress the image, and the ROI wavelet coefficients are upshifted before the actual transmission occurs. ROI is especially useful when using progressive transmission of the image; in such a scenario, the ROI is transmitted first and the background information is transmitted later. The receiver progressively reconstructs the image and can interrupt the transmission at any time; yet, the ROI will still have the highest quality of all the regions in the image.A number of ROI coding methods have been proposed, such as the ROI coding in JPEG2000, 1 and ROI coding based on SPIHT. 2 Although ROI coding techniques are used extensively, most of the ROI identification itself is done manually; i.e., with human intervention. In this paper, a semi-automatic ROI identification technique for images is developed, whereby the segmentation step is performed in the wavelet domain; as opposed to other algorithms where segmentation is performed in the pixel domain. 3,4 Note that in this work, and for computational savings, image segmentation is performed in the wavelet domain and only using the first-level wavelet decomposition, which gives enough edge and texture information for the segmentation process. In, 5 we presented preliminary experimental results. In this paper, the algorithm is applied to other images to verify the