Medical Image Segmentation Using Multi-level Set Partitioning with Topological Graph Prior

Abstract

Abstract. In this paper, we propose an approach for multi-region segmentation based on a topological graph prior within a multi-level set (MLS) formulation. We consider topological graph prior information to evolve the contour based on a topological relationship presented via a graph relation. This novel method is capable of segmenting adjacent objects with very close gray level that would be difficult to segment correctly using standard methods. We describe our algorithm and show the graph prior technique to explain how it gives precise multi-region segmentation. We validate our algorithm with numerous abdominal and brain image databases and compare it to other multi-region segmentation methods to demonstrate its accuracy and computational efficiency.

Multi-region labeling and segmentation using a graph topology prior and atlas information in brain images

“…Fig. 5 shows the results of using the atlas information with the topological graph compared with the results explained in Section 3.1 for multi-region segmentation [8] as well as with the approaches proposed in [14,[20][21][22]. In Fig.…”

“…The results of the first part of our algorithm show the improvement in the segmentation by using the topological prior information [8]. This information help the contour, during the evolution process, to detect the ambiguous regions in the image.…”

“…To validate the accuracy of our algorithm, we compare our algorithm with other state-of-the-art methods [3,5,8,14,[20][21][22] using dice similarity coefficients (DSC) [31]. DSC is measured by computing the similarity between the ground truth segmentation and our algorithm as well as the methods proposed in [3,5,8,14,[20][21][22].…”

“…DSC is measured by computing the similarity between the ground truth segmentation and our algorithm as well as the methods proposed in [3,5,8,14,[20][21][22]. A large DSC indicates higher accuracy: We also employ the symmetric mean absolute distance (MAD) and Hausdorff distance (HD) [32] between the resulting segmentation and the corresponding reference segmentation as additional metrics to evaluate the segmentation results.…”

“…Therefore, the automatic labeling is necessary and to maintain it for brain tissue we consider a topological graph information and tissue labeling for the segmentation that give a precise knowledge about position, size and type of the brain tissue [8]. The topological graph prior gives an information about the organs in the brain images and the atlas gives useful information about the label of the brain regions.…”