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Infrared (IR) thermography has evolved in recent years from being an emerging nondestructive testing (NDT) technique to a viable approach for both aerospace manufacturing and in-service inspections. One of the drawbacks of thermography techniques is that no standard signal processing has been universally adopted and that different algorithms yield different sizing results. Additionally, the data interpretation is not as simple as with other NDT techniques. In this paper the most common signal processing techniques applied to pulsed thermography, which include derivative processing, pulsed phase thermography and principal component analysis, are applied in an attempt to simplify the damage detection and sizing process. The pulsed thermography experiments were carried out on 25 impacted panels made of carbon fiber epoxy material. Despite using similar panels and the same experiment parameters, the damage detection and sizing processes are not straight forward. It is concluded that some algorithms provide easier detection capability than others. However, on their own, the different algorithms lack the robustness to make the damage detection and sizing processes reliable and fully automated. And Optimization of the similarity measure is an essential theme in medical image registration. In this paper, a novel continuous medical image registration approach (CMIR) is proposed. This is our extension work of the previous one where we did a segmentation part of any particular image with a custom algorithm .The CMIR, considering the feedback from users and their preferences on the trade-off between global registration and local registration, extracts the concerned region by user interaction and continuously optimizing the registration result. Experiment results show that CMIR is robust, and more effective compared with the basic optimization algorithm. Image registration, as a precondition of image fusion, has been a critical technique in clinical diagnosis. It can be classified into global registration and local registration. Global registration is used most frequently, which could give a good approximation in most cases and do not need to determine many parameters. Local registration can give detailed information about the concerned regions, which is the critical region in the image. Finding the maximum of the similarity measure is an essential problem in medical image registration. Our work is concentrating on that particular section with the synergy of Tpe-2 fuzzy logic invoked in it.

Image Processing for Automated Flaw Detection and CMYK model for Color Image Segmentation using Type 2 Fuzzy Sets