Image registration is an essential step in many image processing applications that need visual information from multiple images for comparison, integration or analysis. Recently researchers have introduced image registration techniques using the log-polar transform (LPT) for its rotation and scale invariant properties. However, it suffers from nonuniform sampling which makes it not suitable for applications in which the registered images are altered or occluded. Inspired by LPT, this paper presents a new registration algorithm that addresses the problems of the conventional LPT while maintaining the robustness to scale and rotation. We introduce a novel Adaptive Polar Transform (APT) technique that evenly and effectively samples the image in the Cartesian coordinates. Combining APT with an innovative projection transform along with a matching mechanism, the proposed method yields less computational load and more accurate registration than that of the conventional LPT. Translation between the registered images is recovered with the new search scheme using Gabor feature extraction to accelerate the localization procedure. Moreover an image comparison scheme is proposed for locating the area where the image pairs differ. Experiments on real images demonstrate the effectiveness and robustness of the proposed approach for registering images that are subjected to occlusion and alteration in addition to scale, rotation and translation.

Abstract. This paper is a review of works about the use of the log-polar image model for pattern recognition purposes. Particular attention is paid to the rotation- and scale-invariant pattern recognition problem, which is simplified by the log-polar mapping. In spite of this advantage, ordinary translations become a complicated image transform in the log-polar domain. Two approaches addressing the estimation of translation, rotation and scaling are compared. One of them, developed by the au-thors, takes advantage of the principles of the active vision paradigm. 1

Abstract: Image registration has great significance in medicine, with a lot of techniques anticipated in it. This research work implies an approach for medical image registration that registers images of the mono modalities for CT or MRI images using Modified Adaptive Polar Transform (MAPT). The performance of the Adaptive Polar Transform (APT) with the proposed technique is examined. The results prove that MAPT performs better than APT technique. The proposed scheme not only reduces the source of errors and also reduces the elapsed time for registration of brain images. An analysis is presented for the medical image processing on mutual- information-based registration.

Abstract: Image registration has great significance in medicine, astrophotography and satellite imaging, so a lot of techniques have been developed within its perspective of one or the other discipline. This paper presents a method for medical image registration of multimodal brain images using Modified Adaptive Polar Transform (MAPT). This algorithm can be used to register images of the same or different modalities. The performance of the Adaptive Polar Transform (APT) with the proposed technique is examined. The proposed scheme not only reduces the source of errors and also reduces the time for registration of brain images. The analysis presented for the medical image registration using Modified Adaptive Polar Transform (MAPT) and APT proves that MAPT performs better than APT technique.

Abstract: Image registration has great significance in medicine, with a lot of techniques anticipated in it. This research work implies an approach for medical image registration that registers images of the mono modalities for CT or MRI images using Modified Adaptive Polar Transform (MAPT). The performance of the Adaptive Polar Transform (APT) with the proposed technique is examined. The results prove that MAPT performs better than APT technique. The proposed scheme not only reduces the source of errors and also reduces the elapsed time for registration of brain images. An analysis is presented for the medical image processing on mutual- information-based registration.

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