Showing papers by "Mohamed Abdel-Nasser published in 2013"

A High Performance Point Based Image Alignment Approach Using an Artificial Immune System

Abstract: In this paper, a high performance image alignment approach is presented. This approach is classified as a point based alignment approach. An artificial immune system (AIS) with a modified mutation formula is used to find the correspondence points between the reference and the input images. After the correspondence is found, the least mean squares technique (LMS) is used to determine the transformation which is used to align the two images. This approach doesn't require any additional refinement or features detector as some others approaches required. To demonstrate the effectiveness of proposed algorithm, it compared with two state-of-the-art algorithms for different data sets. formula based on an uniform distribution was used. F. Ye et. al (7) proposed two step image registration by artificial immune system and chamfer matching, in this paper the artificial immune system has been used to find an initial transformation where the edge distance used as a fitness function then an area based method has been used to refine the transformation estimation. The artificial immune systems are used for function optimization, the clonal selection and affinity mutation principles are used to explain how the immune systems perform the optimization process. There are many artificial immune systems were published in the context. An immune algorithm, named CLONALG, was developed to perform pattern recognition and optimization. De Castro and J. Timmis proposed opti-aiNet for Multimodal Function Optimization (8). This algorithm is used in our registration approach. The mutation formula proposed at (8) was:

C16. Multimodal Medical Image Registration Approach Using an Artificial Immune System for Noisy and Partial Data

Abstract: Improvement of medical diagnosis, aided computer surgeries and tumor identification requires an accurate image registration approaches. The registration of multimodal medical images is more complicated than the registration of unimodal medical images due to the variation in luminance between the images. In this paper, an accurate multimodal image registration approach using artificial immune system (AIS) is proposed and the affine transformation model is used in contrast to the most of the related works which assumed rigid transformation model or similarity transformation model. In the proposed approach the LL bands of the discrete wavelet transform (DWT) for the images are used and the normalized mutual information (NMI) is used as a fitness function. The proposed approach achieves good result in the case of noiseless images, noisy images and partial data loss from one of the images. Moreover, the proposed approach does not need any feature extraction or refinement step. To demonstrate the robustness of the proposed approach, it has been compared with two multimodal medical image registration approaches.

Region based medical image registration approach using a modified artificial immune system

Abstract: A high accuracy medical image registration is needed to achieve an efficient medical diagnosis and computer aided surgeries, so that a novel region based medical image registration approach is presented in this paper. This approach consists of four sequential steps, image segmentation, point correspondence using a modified artificial immune system, point selection and finally LMS technique is used to estimate the warp parameters. The proposed approach provides a high accuracy and it doesn’t require any additional treatment or feature extraction as some other methods do. To demonstrate the effectiveness of the proposed approach it tested for many image pairs and it is compared with many registration algorithms such aspoint-based image registration using an artificial immune system (AIS),ICP and RANSAC algorithms.

Satellite images registration approach using Lorentzian norm with an artificial immune system

Abstract: Image registration is an important operation in many aerospace imaging applications, so that, in this paper a promising approach is proposed for satellite images registration. In this approach, we propose to use the Lorentzian norm as a fitness function with an artificial immune system optimization (AIS) technique to reduce the registration outlier and avoid trapping into local minima. To demonstrate the effectiveness of the proposed approach it is compared with the state-of-the-art Lukas-Kanade registration approach for many data sets.