Browsing by Author "Zwiggelaar, Reyer"

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    Groupwise Non-rigid Image Alignment With Graph-based Initialisation
    (The Eurographics Association, 2018) Aal-Yhia, Ahmad; Malcolm, Paul; Akanyeti, Otar; Zwiggelaar, Reyer; Tiddeman, Bernard; {Tam, Gary K. L. and Vidal, Franck
    Groupwise image alignment automatically provides non-rigid registration across a set of images. It has found applications in facial image analysis and medical image analysis by automatically generating statistical models of shape and appearance. The main approaches used previously include iterative and graph-based approaches. In iterative approaches, the registration of each image is iteratively updated to minimise an error measure across the set. Various metrics and optimisation strategies have been proposed to achieve this. Graph-based methods perform registration of each pair of images in the set, to form a weighted graph of the ''distance'' between all the images, and then finds the optimal paths between the most central image and every other image. In this paper, we use a graph-based approach to perform initialisation, which is then refined with an iterative approach. Pairwise registration is performed using demons registration, then shortest paths identified in the resulting graph are used to provide an initial warp for each image by concatenating warps along the path. The warps are refined using an iterative Levenberg-Marquardt minimisation to the mean, based on updating the locations of a small number of points and incorporating a stiffness constraint. This optimisation approach is efficient, has very few free parameters to tune and we show how to tune the few remaining parameters. We compare the combined approach to both the iterative and graph-based approaches used independently. Results demonstrate that the combined method improves the alignment of various datasets, including two face datasets and a difficult medical dataset of prostate MRI images.
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    SHREC 2021: Surface-based Protein Domains Retrieval
    (The Eurographics Association, 2021) Langenfeld, Florent; Aderinwale, Tunde; Christoffer, Charles; Shin, Woong-Hee; Terashi, Genki; Wang, Xiao; Kihara, Daisuke; Benhabiles, Halim; Hammoudi, Karim; Cabani, Adnane; Windal, Feryal; Melkemi, Mahmoud; Otu, Ekpo; Zwiggelaar, Reyer; Hunter, David; Liu, Yonghuai; Sirugue, Léa; Nguyen, Huu-Nghia H.; Nguyen, Tuan-Duy H.; Nguyen–Truong, Vinh-Thuyen; Le, Danh; Nguyen, Hai-Dang; Tran, Minh-Triet; Montès, Matthieu; Biasotti, Silvia and Dyke, Roberto M. and Lai, Yukun and Rosin, Paul L. and Veltkamp, Remco C.
    Proteins are essential to nearly all cellular mechanism, and often interact through their surface with other cell molecules, such as proteins and ligands. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence surface, which is therefore of primary importance for their activity. In the present work, we assess the ability of five methods to retrieve similar protein surfaces, using either their shape only (3D meshes), or their shape and the electrostatic potential at their surface, an important surface property. Five different groups participated in this challenge using the shape only, and one group extended its pre-existing algorithm to handle the electrostatic potential. The results reveal both the ability of the methods to detect related proteins and their difficulties to distinguish between topologically related proteins.
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    Topological Connected Chain Modelling for Classification of Mammographic Microcalcification
    (The Eurographics Association, 2018) George, Minu; Denton, Erika R. E.; Zwiggelaar, Reyer; {Tam, Gary K. L. and Vidal, Franck
    Breast cancer continues to be the most common type of cancer among women. Early detection of breast cancer is key to effective treatment. The presence of clusters of fine, granular microcalcifications in mammographic images can be a primary sign of breast cancer. The malignancy of any cluster of microcalcification cannot be reliably determined by radiologists from mammographic images and need to be assessed through histology images. In this paper, a novel method of mammographic microcalcification classification is described using the local topological structure of microcalcifications. Unlike the statistical and texture features of microcalcifications, the proposed method focuses on the number of microcalcifications in local clusters, the distance between them, and the number of clusters. The initial evaluation on the Digital Database for Screening Mammography (DDSM) database shows promising results with 86% accuracy and findings which are in line with clinical perception of benign and malignant morphological appearance of microcalcification clusters.