Automatically Gradient Threshold Estimation of Anisotropic Diffusion for Meyer’s Watershed Algorithm Based Optimal Segmentation

Abstract

Medical image segmentation is a fundamental task in the medical imaging field. Optimal segmentation is required for the accurate judgment or appropriate clinical diagnosis. In this paper, we proposed automatically gradient threshold estimator of anisotropic diffusion for Meyer's Watershed algorithm based optimal segmentation. The Meyer's Watershed algorithm is the most significant for a large number of regions separations but the over segmentation is the major drawback of the Meyer's Watershed algorithm. We are able to remove over segmentation after using anisotropic diffusion as a preprocessing step of segmentation in the Meyer's Watershed algorithm. We used a fixed window size for dynamically gradient threshold estimation. The gradient threshold is the most important parameter of the anisotropic diffusion for image smoothing. The proposed method is able to segment medical image accurately because of obtaining the enhancement image. The introducing method demonstrates better performance without loss of any clinical information while preserving edges. Our investigated method is more efficient and effective in order to segment the region of interests in the medical images indeed.

Direct image-based micro finite element modelling of bone tissue

“…Figure 2 illustrated options with the following criteria: if the center of a pixel inside FE it included in integration (orange squares) otherwise not (blue squares). The problem of the meshing area goes beyond the paper, but nowadays there are some methods to create mesh on CT data [30][31][32]. In the assumptions stress results in node are invalid.…”

The Image-Based Finite Element Evaluation of the Deformed State

“…где V -объем конечного элемента в пространстве сплошного материала Ω; V' -объем в дискретном пространстве компьютерной томографии Ω'. Для сегментации исследуемого объекта на изображении использовались методы, основанные на оптической плотности [30,31] с модификациями, описанными в [32,33]. Для автоматизации построения расчетной сетки использовался подход фильтрации сетки, описанный в [34].…”

Automatic processing and analysis of the structural properties of bone tissue

“…We used a specific algorithm to analyze the CT data. Generally, the algorithm of the analysis can be described as [25,29,31,32]: 1) calculating the binarization threshold; 2) meshing the sample;…”

“…The actual task in these problems is the automation of such a calculation. The obtained data can be useful in various biomechanical calculations, such as determination of bone defects before [22][23][24][25] and after [26][27][28] surgery.…”