Multi-region level set image segmentation based on image energy separation model

Xiao, Yin; Yan, Hong; Yao, Y. H.; Guo, Jianping; Zhong, Chong-Fa; Zhang, Zhe; Wei, Yi

doi:10.1117/12.2030891

Cited by 1 publication

(3 citation statements)

References 25 publications

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“…Region based segmentation models [5,[13][14][15][16][17][18] use image statistics to partition the image into regions. Ideally, the object of interest will have different statistics in comparison to surrounding areas and can then be quickly segmented.…”

Section: Active Contour Modelsmentioning

confidence: 99%

“…From (5) we can solve the mean curvature motion by means of the operator. However, since the operator generates new level set values after a single iteration, it ceases to be morphological.…”

Section: Morphological Active Contoursmentioning

confidence: 99%

“…The difficulty in computational processing of natural images can be attributed to inherent statistical complexities of the image and a lack of homogeneity or saliency of local features at the same spatial or quantization scale [2]. As a result, segmentation of natural images remains a challenging task in image processing and computer vision.In recent literature, methods for segmentation of natural images include active contours [3][4][5], clustering methods [6][7][8][9],lossy data compression [10,11] and graph cuts [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Hybrid Morphological Active Contour for Natural Images

Fox¹,

Milanova²,

Al-Ali³

2013

IJCSEA

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Morphological active contours for image segmentation have become popular due to their low computational complexity coupled with their accurate approximation of the partial differential equations involved in the energy minimization of the segmentation process. In this paper, a morphological active contour which mimics the energy minimization of the popular Chan-Vese Active Contour without Edges is coupled with a morphological edge-driven segmentation term to accurately segment natural images. By using morphological approximations of the energy minimization steps, the algorithm has a low computational complexity. Additionally, the coupling of the edge-based and region-based segmentation techniques allows the proposed method to be robust and accurate. We will demonstrate the accuracy and robustness of the algorithm using images from the Weizmann Segmentation Evaluation Database and report on the segmentation results using the Sorensen-Dice similarity coefficient.

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