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Optimisation and Performance Evaluation in image registration technique

Abstract : D’Tech Thesis SummaryThe importance of medical imaging as a core component of several medical application and healthcare diagnosis cannot be over emphasised. Integration of useful data acquired from different images is vital for proper analysis of information contained in the images under observation. For the integration process to be successful, a procedure referred to as image registration is necessary.The purpose of image registration is to align two images in order to find a geometric transformation that brings one image into the best possible spatial correspondence with another image by optimising a registration criterion. The two images are known as the target image and the source image. Image registration methods consist of having the two images referenced with control points. This is followed by a registration transformation that relates the two images and a similarity metric function that aims to measure the qualitative value of closeness or degree of fitness between the target image and the source image. Finally, an optimiser which seeks an optimal transformation inside the defined solution search space is performed.This research presents an automated image registration algorithm for solving multimodal image registration on lung Computer Tomography (CT) scan pairs, where a comparison between regular step gradient descent optimisation technique and evolutionary optimisation was investigated. The aim of this research is to carry out optimisation and performance evaluation of image registration techniques in order to provide medical specialists with estimation on how accurate and robust the registration process is. Lung CT scan pairs are registered using mutual information as a similarity measure, affine transformation and linear interpolation. In order to minimise the cost function, an optimiser, which seeks the optimal transformation inside the defined search space is applied.Determination of a transformation model that depends on transformation parameters and identification of similarity metric based on voxel intensity were carried out. By fitting transformation to control points, three transformation models were compared. Affine transformation produced the best recovered image when compared to non-reflective similarity and projective transformations. The results of this research compares well with documented results from EMPIRE 10 Challenge research and conforms to both theoretical principles as well as practical applications.The contribution of this research is its potential to increase the scientific understanding of image registration of anatomical body organs. It lays a basis for further research in performance evaluation of registration techniques and validation of procedures to other types of algorithms and image registration application areas, such as remote sensing, satellite communication, biomedical engineering, robotics, geographical information systems and mapping, among others
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Shadrack Mambo. Optimisation and Performance Evaluation in image registration technique. Signal and Image Processing. Université Paris-Est; Tshwane University of Technology, 2018. English. ⟨NNT : 2018PESC1125⟩. ⟨tel-02127712⟩

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