Abstract
This paper presents an approach to medical image registration using a segmentation step based on Fuzzy C-Means (FCM) clustering and the Scale Invariant Feature Transform (SIFT) for matching keypoints in segmented regions. To obtain robust segmentation, FCM is applied on feature vectors composed by local information invariant to image scaling and rotation, and to change in illumination. SIFT is then applied to corresponding regions in reference and target images, after the application of an alpha-cut. The proposed registration method is more robust to noise artifacts than standard SIFT. The paper shows also a method for FCM clustering speeding-up based on a dynamic pyramid approach using low resolution images of increasing size.
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Mahmoud, H., Masulli, F., Rovetta, S. (2013). Feature-Based Medical Image Registration Using a Fuzzy Clustering Segmentation Approach. In: Peterson, L.E., Masulli, F., Russo, G. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2012. Lecture Notes in Computer Science(), vol 7845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38342-7_4
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DOI: https://doi.org/10.1007/978-3-642-38342-7_4
Publisher Name: Springer, Berlin, Heidelberg
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