Abstract

In this paper, an integrated underwater sonar image extraction strategy, which combines two improved methods, namely the level set method (LSM) and the Lattice Boltzmann Method (LBM), is proposed. First, sonar images are processed by a clustering method and a connected domain analysis to generate the target minimum rectangle frame. Next, the segmentation task is decomposed into two subtasks, namely a coarse segmentation task to obtain the initial contour and a fine segmentation task after embedding the initial contour. Finally, the improved LSM is used to obtain the target contour, and the coarse contour of the segment is embedded into the LBM to obtain the region segmentation of the target in the sonar images. The main contributions of the paper are as follows: (1) The contours and regions of the sonar images are extracted simultaneously. (2) The original LBM method is enhanced to solve the level set iteration problem. (3) The region segmentation with the original image background is extracted, and a more intuitive region segmentation result than that of directly extracting the contour of the level set is achieved. Experimental results based on four evaluation indices of image segmentation show that our method is effective, accurate, and superior to other existing methods.

Highlights

  • Image segmentation is the key step from image processing to image analysis

  • The evolution speed of the Distance Regularized Level Set Evolution (DRLSE) model in [11] has been greatly improved, we find that a direct application of this model to noisy images still has drawbacks, and most of the level set initialization of the manual or random mode will lead to inaccurate segmentation results

  • It can be seen that the segmentation results of our improved Lattice Boltzmann method (LBM) are significantly improved compared with the original segmentation of the second to the third rows

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Summary

Introduction

Image segmentation is the key step from image processing to image analysis. In general, image segmentation methods can be summarized as threshold-based, region-based, edge-based, and some specific theory-based segmentation methods. Compared with the geodesic active contour model, the LSM shows good performance, and some images that are not suitable for classical snake method (based on gradient) segmentation are successfully segmented [5,6,7]. The evolution speed of the Distance Regularized Level Set Evolution (DRLSE) model in [11] has been greatly improved, we find that a direct application of this model to noisy images still has drawbacks, and most of the level set initialization of the manual or random mode will lead to inaccurate segmentation results In this context, a novel method to segment real-world sonar images is proposed. We employed it in sonar image segmentation to observe the region segmentation of sonar images, embedded it in our level set initialization improvement algorithm, and obtained good results with a few iterations.

Traditional Level Set Model
DRLSE Model
Experimental Results and Analysis
Comparison of LBM Evolution
Performance Evaluation
Jaccard
Evaluation Indices
Conclusions
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