17 March 2017 Comparison of k-means related clustering methods for nuclear medicine images segmentation
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Proceedings Volume 10341, Ninth International Conference on Machine Vision (ICMV 2016); 1034118 (2017) https://doi.org/10.1117/12.2268825
Event: Ninth International Conference on Machine Vision, 2016, Nice, France
Image segmentation is often used in medical image processing. This crucial task can affect all results obtained from the further steps of image analysis. In nuclear medicine emission tomography imaging, where acquired and reconstructed images contain large noise and high blurring level, segmentation and tumour boundaries delineation can be very challenging task. Many from already existing image segmentation methods are based on clustering. In this work, we have tested and implemented a few clustering based methods. We have mainly focused on k-means related algorithms to evaluate and compare their accuracy. In this group we have chosen k-means algorithm, k-medoid clustering, and fuzzy C-means (FCM) method. Results for all methods were verified using the gold standard obtained from anatomical image co-registered and emission tomography dataset. Numerical values of both datasets matching were calculated using the Jaccard index. Results were compared with standard segmentation algorithm based on fixed threshold (standardized uptake value - SUV with threshold 2.5), which is a commonly used standard in clinical practice and also with previously implemented and verified methods (including game theoretical algorithm). For all tested methods we have obtained very similar results, comparable to SUV 2.5 threshold method but worse than the game theoretic method.
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Damian Borys, Damian Borys, Pawel Bzowski, Pawel Bzowski, Marta Danch-Wierzchowska, Marta Danch-Wierzchowska, Krzysztof Psiuk-Maksymowicz, Krzysztof Psiuk-Maksymowicz, } "Comparison of k-means related clustering methods for nuclear medicine images segmentation", Proc. SPIE 10341, Ninth International Conference on Machine Vision (ICMV 2016), 1034118 (17 March 2017); doi: 10.1117/12.2268825; https://doi.org/10.1117/12.2268825

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