Propagated image Segmentation Using Edge-Weighted Centroidal Voronoi Tessellation based Methods

Propagated image segmentation is the problem of utilizing the existing segmentation of an image for obtaining a new segmentation of, either a neighboring image in a sequence, or the same image but in different scales. We name these two cases as the inter-image propagation and the intra-image propagation respectively. The inter-image propagation is particularly important to material science, where efficient and accurate segmentation of a sequence of 2D serial-sectioned images of 3D material samples is an essential step to understand the underlying micro-structure and related physical properties. For natural images with objects in different scales, the intra-image propagation, where segmentations are propagated from the finest scale to coarser scales, is able to better capture object boundaries than single-shot segmentations on a fixed image scale. In this work, we first propose an inter-image propagation method named Edge- Weighted Centroid Voronoi Tessellation with Propagation of Consistency Constraint (CCEWCVT) to effectively segment material images. CCEWCVT segments an image sequence by repeatedly propagating a 2D segmentation from one slice to another, and in each step of this propagation, we apply the proposed consistency constraint in the pixel clustering process such that stable structures identified from the previous slice can be well-preserved. We further propose a non-rigid transformation based association method to find the correspondence of propagated stable structures in the next slice when the inter-image distance becomes large. We justify the effectiveness of the proposed CCEWCVT method on 3D material image sequences, and we compare its performance against several state-of-the-art 2D, 3D, propagated segmentation methods. Then for the intra-image propagation, we propose a superpixel construction method named Hierarchical Edge-Weighted Centroidal Voronoi Tessellation (HEWCVT) to accurately capture object boundaries in natural images. We model the problem as a multilevel clustering process: superpixels in one level are clustered to obtain larger size superpixels in the next level. The clustering energy involves both color similarities and the proposed boundary smoothness of superpixels. We further extend HEWCVT to obtain supervoxels on 3D images or videos. Both quantitative and qualitative evaluation results on several standard datasets show that the proposed HEWCVT method achieves superior or comparable performances to other state-of-the-art methods. v

Multi-Label Segmentation Propagation for Materials Science Images Incorporating Topology and Interactivity

Segmentation propagation is the problem of transferring the segmentation of an image to a neighboring image in a sequence. This problem is of particular importance to materials science, where the accurate segmentation of a series of 2D serial-sectioned images of multiple, contiguous 3D structures has important applications. Such structures may have prior-known shape, appearance, and/or topology among the underlying structures which can be considered to improve segmentation accuracy. For example, some materials images may have structures with a specific shape or appearance in each serial section slice, which only changes minimally from slice to slice; and some materials may exhibit specific topology which constrains their structure or neighboring relations. In this work, we develop a framework for materials image segmentation that segments a variety of materials image types by repeatedly propagating a 2D segmentation from one slice to another, and we formulate each step of this propagation as an optimal labeling problem that can be efficiently solved using the graph-cut algorithm. During this propagation, we propose novel strategies to enforce the shape, appearance, and topology of the segmented structures, as well as handling local topology inconsistency. Most recent works on topology-constrained image segmentation focus on binary segmentation, where the topology is often described by the connectivity of both foreground and background. We develop a new multi-labeling approach to enforce topology in multiple-label image segmentation. In this case, we not only require each segment to be a connected region (intra-segment topology), but also require specific adjacency relations between each pair of segments (inter-segment topology). Finally, we integrate an interactive approach into the proposed framework that improves the segmentation by allowing minimal and simplistic human annotations. We justify the effectiveness of the proposed framework by testing it on various 3D materials images, and we compare its performance against several existing image segmentation methods

A Multichannel Edge-Weighted Centroidal Voronoi Tessellation Algorithm for 3D Super-alloy Image Segmentation

In material science and engineering, the grain structure inside a super-alloy sample determines its mechanical and physical properties. In this paper, we develop a new Multichannel Edge-Weighted Centroidal Voronoi Tessellation (MCEWCVT) algorithm to automatically segment all the 3D grains from microscopic images of a super-alloy sample. Built upon the classical k-means/CVT algorithm, the proposed algorithm considers both the voxel-intensity similarity within each cluster and the compactness of each cluster. In addition, the same slice of a super-alloy sample can produce multiple images with different grain appearances using different settings of the microscope. We call this multichannel imaging and in this paper, we further adapt the proposed segmentation algorithm to handle such multichannel images to achieve higher grain-segmentation accuracy. We test the proposed MCEWCVT algorithm on a 4-channel Nibased 3D super-alloy image consisting of 170 slices. The segmentation performance is evaluated against the manually annotated ground-truth segmentation and quantitatively compared with other six image segmentation/edgedetection methods. The experimental results demonstrate the higher accuracy of the proposed algorithm than the comparison methods. 1