CT Image Segmentation Using Structural Analysis

Hishida, Hiroyuki; Michikawa, Takashi; Ohtake, Yutaka; Suzuki, Hiromasa; Oota, Satoshi

doi:10.1007/978-3-642-17277-9_5

Cited by 1 publication

(4 citation statements)

References 23 publications

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“…For this purpose, we need to set the loading condition of external forces . One method was to do it manually [1] , but this is not a realistic way particularly for complex structures. In this paper we propose a method to automatically find some appropriate by casting this problem to an optimization problem.…”

Section: Methodsmentioning

confidence: 99%

“…Accordingly, we previously proposed a method to segment CT images using structural analysis [1] . The technique is based on the assumption that the interference area (joint) between components (bones) is structurally weak.…”

Section: Introductionmentioning

confidence: 99%

“…We compute strain, which tends to be large in structurally weak areas and segment the image in the region of high strain. In previous work [1] , we used a commercial software VOXELCON [2] , which is an image-based structural analysis system. In the approach examined, we set physical properties for every pixel and create a stiffness matrix.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we expand our previous work [1] to set parameters and loading conditions in a semiautomatic way. We set regions of interest (ROIs) as candidates for weak areas, which include correct and erroneous segmentation targets.…”

Section: Introductionmentioning

confidence: 99%

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CT Image Segmentation Using FEM with Optimized Boundary Condition

et al. 2012

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The authors propose a CT image segmentation method using structural analysis that is useful for objects with structural dynamic characteristics. Motivation of our research is from the area of genetic activity. In order to reveal the roles of genes, it is necessary to create mutant mice and measure differences among them by scanning their skeletons with an X-ray CT scanner. The CT image needs to be manually segmented into pieces of the bones. It is a very time consuming to manually segment many mutant mouse models in order to reveal the roles of genes. It is desirable to make this segmentation procedure automatic. Although numerous papers in the past have proposed segmentation techniques, no general segmentation method for skeletons of living creatures has been established. Against this background, the authors propose a segmentation method based on the concept of destruction analogy. To realize this concept, structural analysis is performed using the finite element method (FEM), as structurally weak areas can be expected to break under conditions of stress. The contribution of the method is its novelty, as no studies have so far used structural analysis for image segmentation. The method's implementation involves three steps. First, finite elements are created directly from the pixels of a CT image, and then candidates are also selected in areas where segmentation is thought to be appropriate. The second step involves destruction analogy to find a single candidate with high strain chosen as the segmentation target. The boundary conditions for FEM are also set automatically. Then, destruction analogy is implemented by replacing pixels with high strain as background ones, and this process is iterated until object is decomposed into two parts. Here, CT image segmentation is demonstrated using various types of CT imagery.

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