Spectral Embedding and Min Cut for Image Segmentation

Image segmentation based on improved continuous max-flow/min-cut approach

Information Management and Management Engineering ◽

10.2495/imme140121 ◽

2014 ◽

Author(s):

Lin He ◽

Juncheng Lv

Keyword(s):

Image Segmentation ◽

Min Cut

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A Graph-Based Image Segmentation Alorithm Using Heirarchical Social Metaheuristic

Advances in Image and Video Segmentation ◽

10.4018/978-1-59140-753-9.ch004 ◽

2006 ◽

pp. 72-92

Author(s):

Abraham Duarte ◽

Angel Sanchez ◽

Felipe Fernandez ◽

Antonio S. Montemayor

Keyword(s):

Image Segmentation ◽

Weighted Graph ◽

Standard Test ◽

Second Phase ◽

Improve Quality ◽

Splitting Scheme ◽

Segmentation Method ◽

Normalized Cut ◽

Segmentation Quality ◽

Min Cut

This chapter proposes a new evolutionary graph-based image segmentation method to improve quality results. Our approach is quite general and can be considered as a pixel- or region-based segmentation technique. What is more important is that they (pixels or regions) are not necessarily adjacent. We start from an image described by a simplified undirected weighted graph where nodes represent either pixels or regions (obtained after an oversegmentation process) and weighted edges measure the dissimilarity between pairs of pixels or regions. As a second phase, the resulting graph is successively partitioned into two subgraphs in a hierarchical fashion, corresponding to the two most significant components of the actual image, until a termination condition is met. This graph-partitioning task is solved as a variant of the min-cut problem (normalized cut) using a hierarchical social (HS) metaheuristic. As a consequence of this iterative graph bipartition stage, pixels or regions are initially merged into the two most coherent components, which are successively bipartitioned according to this graph-splitting scheme. We applied the proposed approach to brightness segmentation on different standard test images, with good visual and objective segmentation quality results.

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Direct 3D Organ Extraction Method in Virtual Human-Body Color Volume Image

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3209 ◽

2020 ◽

Vol 10 (11) ◽

pp. 2739-2744

Author(s):

Bin Liu ◽

Yanjie Chen ◽

Shujun Liu ◽

Qifeng Wang ◽

Xiaolei Niu ◽

...

Keyword(s):

Image Segmentation ◽

Connected Graph ◽

Human Error ◽

Segmentation Method ◽

Color Range ◽

Color Similarity ◽

Different Color ◽

Segmentation Task ◽

3D Volume ◽

Min Cut

Extracting 3D structures from voxel based images can make doctors more directly observe the situation of the target in the clinic, making it easier for doctors to diagnose the condition and make the medicine teaching more directly and easier to understand. For this purpose, we propose a 3D volume image segmentation method based on the max-flow/min-cut algorithm. Our segmentation method can be applied directly to 3D volume image. After users marking small amount tags (foreground and background pixels), we put forward a method to use a directed connected graph structure to represent the volume image. In the directed connected graph, in order to speed up the efficiency of the segmentation in subsequent steps, we divide each voxel node in the graph into different color ranges, and each color range match up with an auxiliary node. In order to divide the color range more finely, we propose a method to calculate the color similarity. We then use the max-flow/min-cut algorithm to segment the directed connected graph. The result of experiments performed in multiple sets of slice images shows that our proposed method improves the efficiency, reduces human error on the 3D volume image segmentation task, and the result is complete and accurate.

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