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Abstract:Image registration is an essential step in a large number of processing chains for medical images. It is used to align two images taken at different times and from different sensors as well. In this paper, we are interested in the rigid registration and similarity measures. We describe a new registration approach, based on the normalized dissimilarity index that results from the local dissimilarity map (LDP). This LDP is obtained from distance transform applied to gray-scale images, to register, undergoing a binarization. We evaluate the performance of our method compared to the classical registration measurements such as correlation and mutual information, on a medical images database. We show that the mean squared error of our approach is more accurate in comparison to the classical registration methods to which researchers still adhere. The robustness of our proposed index is validated regarding the luminance variation and the presence of "the Pepper and Salt" as much as "the Gaussian" noise.

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Abstract:In medical image processing, evaluating the variations of lesion volume plays a major role in many medical applications. It helps radiologists to follow-up with patients and examine the effects of therapy. Several approaches have been proposed to meet with medical expectations. The present work comes within this context. We present a new approach based on the local dissimilarity volume (LDV) that is a 3D representation of the local dissimilarity map (LDM). This map presents a useful means to compare two images, offering a localization of information. We proved the effectiveness of this method (LDV) compared to medical techniques used by radiologists. The result of simulations shows that we can quantify lesion volume by using the LDV method, which is an efficient way to calculate and localize the volume variation of anomalies. It allowed a time savings with the compete satisfaction of an expert during the medical treatment.