Titlebook: Deformable Meshes for Medical Image Segmentation; Accurate Automatic S Dagmar Kainmueller Book 2015 Springer Fachmedien Wiesbaden 2015 Auto

闷热

https://doi.org/10.1007/978-3-642-68912-3y. Building non-linear statistical shape models is an active area of research (see e.g. Gerber et al. (2009)). However, certain anatomical structures exhibit large non-linear . variations of shape that are of . nature rather than stemming from a statistical distribution. E.g., articulated bone compo

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https://doi.org/10.1007/978-3-540-29925-7ruction of anatomical landmarks adjacent to the pelvic bones from CT, as published in Seim et al. (2009). We compare different approaches for . reconstruction of landmarks, i.e. image appearance is not modeled for the landmarks.

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https://doi.org/10.1007/978-3-642-68912-3This chapter introduces basic terms and notations which we use throughout the following chapters.

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Dictionary of Minor Planet NamesIn Section 3.5 we described . and proposed a .. Together, these methods allow for simultaneous segmentation of arbitrarily shaped adjacent objects. This chapter presents applications of coupled multi-object GraphCuts to selected structures of the musculoskeletal system in joint regions, namely bones and cartilage.

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纤细

Basic Terms and NotationThis chapter introduces basic terms and notations which we use throughout the following chapters.

Ringworm

Single-object Segmentation of Anatomical StructuresIn this chapter, we propose fully automatic segmentation pipelines for three segmentation problems that appear in clinical practice. Pipelines are assembled from methods described in Chapter 3 (Sections 3.1 – 3.4).

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