Read e-book online Deformable meshes for medical image segmentation : accurate PDF

By Dagmar Kainmueller

ISBN-10: 3658070145

ISBN-13: 9783658070144

ISBN-10: 3658070153

ISBN-13: 9783658070151

​ Segmentation of anatomical buildings in clinical photo info is a vital job in medical perform. Dagmar Kainmueller introduces equipment for actual totally automated segmentation of anatomical constructions in 3D scientific photograph information. The author’s middle methodological contribution is a singular deformation version that overcomes boundaries of cutting-edge Deformable floor techniques, as a result taking into consideration exact segmentation of tip- and ridge-shaped beneficial properties of anatomical constructions. As for functional contributions, she proposes application-specific segmentation pipelines for more than a few anatomical constructions, including thorough reviews of segmentation accuracy on scientific picture information. in comparison to comparable paintings, those absolutely computerized pipelines permit for hugely actual segmentation of benchmark photo data.​

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Extra info for Deformable meshes for medical image segmentation : accurate automatic segmentation of anatomical structures

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However, they do not deal with the problem that overlapping segmentation results might occur if the shared displacement directions do not fulfill certain conditions. Liu et al. (2009) have proposed an approach which is similar to ours in that it makes use of medial axes of an initial segmentation to define displacement directions with an appropriate length while avoiding self-intersections. However, the displacement directions are specifically designed for tree-like structures with multiple walls, like airway trees and lung vascular trees.

Application-specific modifications of the above are described in the respective Application sections. 1 Appearance Cost Function For a range of applications discussed in this thesis, we define vertex-individual appearance costs by means of an appearance cost function φ : V × S → R+ 0 . The function φ reflects the “match” between appearance model and image I : Ω → R at vertex candidate locations v + s as follows: If the image intensity I(v + s) lies within a window [t1 , t2 ], and the directional image derivative ∇nv I(v + s) along the surface normal nv at vertex v exceeds a threshold g > 0, the cost φ(v, s) is g .

Sec. e. the ranges of motion of vertices, are not closely related. 4 and Chapter 4. g. e. a rigid transformation T ), an appearance cost is determined by comparing image gradient ∇I(v) directions |∇I(v)| and surface normals nv at each vertex v of the transformed surface mesh. If the angle between image gradient and vertex normal is smaller than a threshold, the individual vertex is assigned zero cost, else one. , φGHT : V → {0, 1} , v → arccos( π ∇I(v) · nv )< |∇I(v)| a , where πa defines an angle threshold.

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Deformable meshes for medical image segmentation : accurate automatic segmentation of anatomical structures by Dagmar Kainmueller


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