Foundations of Information Technologies

- FIT 2009 -

June 14-27, 2009, Novi Sad

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New objective analysis techniques for quantification of tissue regeneration around medical devices


 Joakim Lindblad



Digital Image Analysis is the extraction of meaningful information from images by the means of computers. This is a rapidly growing area of research within the field of Information Science, and the applications of Digital Image Analysis are continuously expanding through all areas of science and industry, ranging from the analysis of virus particles in electron microscopy images to the study of astronomical images aimed at understanding the origins of the universe. In modern medicine, digital image analysis has become an indispensable tool and is present in almost every situation. This lecture will present one application of digital image analysis, ultimately aimed at improving the quality of life for patients requiring any type of bone implants.

With an ageing and increasingly osteoporotic population, bone implants are becoming more important in the modern society. Today medical implants is a large industry, and new models and new materials are presented at an ever increasing rate. For proper functionality of bone implants, a good integration of the device in the surrounding bone tissue is of highest importance. To evaluate how the tissue reacts on different types of implants and implant materials, samples are retrieved from animal testing and analyzed in detail. Today, this analysis is done manually in a microscope, which is a tedious, costly, and subjective procedure. With the aid of Digital Image Analysis, we aim to provide a fast, cheap, and objective alternative to this manual investigation. In my talk I will present ongoing research in this area, including two approaches for quantifying the integration of bone implants, one based on 2D histological imaging and one based on 3D images acquired using Synchrotron X-ray microtomorgraphy. Processing steps that will be mentioned include image segmentation based on supervised colour classification, iterative relative fuzzy connectedness, high precision feature estimates utilizing a pixel coverage model.