Abstract
Anatomical structures and tissues are often hard to be segmented in medical images due to their poorly defined boundaries, i.e., low contrast in relation to other nearby false boundaries. The specification of the boundary polarity can help alleviate a part of this problem. In this work, we discuss how to incorporate this property in the relative fuzzy connectedness (RFC) framework. We include a theoretical proof of the optimality of the new algorithm, named oriented relative fuzzy connectedness (ORFC), in terms of an oriented energy function subject to the seed constraints, and show its usage to devise powerful hybrid image segmentation methods. The methods are evaluated using medical images of MRI and CT of the human brain and thoracic studies.
Highlights
Manipulating large amounts of data efficiently with high performance is today a complex task investigated by various scientific communities, as well by private sector corporations and government entities
5.2 oriented relative fuzzy connectedness (ORFC) as a directed cut in the digraph Given that the previous ORFC definition (Section 5.1) presents undesirable results, we present an alternative definition supported by a graph cut optimality criterion, which is motivated by the definitions from Section 3.2
We performed the segmentation of the bones calcaneus and talus for all the methods (IRFC [9], relative fuzzy connectedness (RFC) [26], oriented image foresting transform (OIFT) [23], RFC+graph cuts (GC) [29], oriented graph cut (OGC) [11] the graph cut with boundary polarity, ORFC, and the proposed hybrid method ORFC+GC), for different seed sets automatically obtained by eroding and dilating the ground truth at different radius values
Summary
Manipulating large amounts of data efficiently with high performance is today a complex task investigated by various scientific communities, as well by private sector corporations and government entities. The seed’s labels are propagated to all unlabeled regions by following some optimum criterion, such that a complete labeled image is constructed This class encloses many of the most prominent methods for general purpose segmentation, which are usually easier to extend to multi-dimensional images, including frameworks, such as watershed from markers [6,7], random walks [8], fuzzy connectedness [9,10], graph cuts (GC) [11], distance cut [12], image foresting transform [13], and grow cut [14].
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