Abstract
The main goal of this paper is to describe universal software framework and its improvements to represent region of interest (ROI) which can be used for precise medical image segmentation. Software reads different image modalities and later applies registration method to align two or more datasets. This article also presents extension for basic segmentation enriching it with Rough sets theory and c-means fuzzy logic application to manage uncertain and vague data. Rough sets method introduces two region of interest for current segmentation: positive one where voxels are certainly included in the ROI and boundary region in which voxels possibly belong to ROI. Such concept description is very valuable in early medical diagnosis especially in oncological treatment. Along with Rough sets algorithm c-means fuzzy logic algorithm has been implemented for clustering data in MRI images.
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Majak, M. (2014). Universal Segmentation Framework for Medical Imaging Using Rough Sets Theory and Fuzzy Logic Clustering. In: Piętka, E., Kawa, J., Wieclawek, W. (eds) Information Technologies in Biomedicine, Volume 3. Advances in Intelligent Systems and Computing, vol 283. Springer, Cham. https://doi.org/10.1007/978-3-319-06593-9_16
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DOI: https://doi.org/10.1007/978-3-319-06593-9_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06592-2
Online ISBN: 978-3-319-06593-9
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