Active Contours Driven by Visual Saliency Fitting Energy for Image Segmentation in SAR Images

Yang, Linna; Xin, Dan; Zhai, Liang; Fang, Yuan; Li, Xiaopeng

doi:10.1109/icccbda.2019.8725646

Cited by 12 publications

(8 citation statements)

References 13 publications

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“…In this model, a saliency map of the original image is found using the approach in [40], and then it is applied with the CV model to compute the final energy functional. An edge term is also used to enhance the segmentation results.…”

Section: Sdrelmentioning

confidence: 99%

“…As discussed above, there are many methods to compute the saliency map of an input image; however, in this study, we compute the saliency map using the approach in [40]. Fig.…”

Section: A Saliency Mapmentioning

confidence: 99%

“…SDREL uses a saliency map with a global image information based active contour model, and will therefore not produce prominent results for images with intensity inhomogeneity. Similarly, Nguyen et al [39] and Yang et al [40] also used visual saliency with active contour models to enhance the segmentation results. All such saliency-based active contour models can segment images that have strong boundaries and intensity homogeneity but fail to segment images with corrupted intensity and intensity inhomogeneity.…”

Section: Introductionmentioning

confidence: 99%

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Saliency-Driven Active Contour Model for Image Segmentation

et al. 2020

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Active contour models have achieved prominent success in the area of image segmentation, allowing complex objects to be segmented from the background for further analysis. Existing models can be divided into region-based active contour models and edge-based active contour models. However, both models use direct image data to achieve segmentation and face many challenging problems in terms of the initial contour position, noise sensitivity, local minima and inefficiency owing to the in-homogeneity of image intensities. The saliency map of an image changes the image representation, making it more visual and meaningful. In this study, we propose a novel model that uses the advantages of a saliency map with local image information (LIF) and overcomes the drawbacks of previous models. The proposed model is driven by a saliency map of an image and the local image information to enhance the progress of the active contour models. In this model, the saliency map of an image is first computed to find the saliency driven local fitting energy. Then, the saliency-driven local fitting energy is combined with the LIF model, resulting in a final novel energy functional. This final energy functional is formulated through a level set formulation, and regulation terms are added to evolve the contour more precisely across the object boundaries. The quality of the proposed method was verified on different synthetic images, real images and publicly available datasets, including medical images. The image segmentation results, and quantitative comparisons confirmed the contour initialization independence, noise insensitivity, and superior segmentation accuracy of the proposed model in comparison to the other segmentation models.

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Section: Sdrelmentioning

confidence: 99%

“…As discussed above, there are many methods to compute the saliency map of an input image; however, in this study, we compute the saliency map using the approach in [40]. Fig.…”

Section: A Saliency Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Saliency-Driven Active Contour Model for Image Segmentation

et al. 2020

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“…Image analysis and processing is a complex process. As the basic work of computer vision preprocessing, image segmentation plays an important role in image analysis and understanding [8].…”

Section: Introductionmentioning

confidence: 99%

SAR Image Segmentation Using Region Smoothing and Label Correction

et al. 2020

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The traditional unsupervised image segmentation methods are widely used in synthetic aperture radar (SAR) image segmentation due to the simple and convenient application process. In order to solve the time-consuming problem of the common methods, an SAR image segmentation method using region smoothing and label correction (RSLC) is proposed. In this algorithm, the image smoothing results are used to approximate the results of the spatial information polynomials of the image. Thus, the segmentation process can be realized quickly and effectively. Firstly, direction templates are used to detect the directions at different coordinates of the image, and smoothing templates are used to smooth the edge regions according to the directions. It achieves the smoothing of the edge regions and the retention of the edge information. Then the homogeneous regions are presented indirectly according to the difference of directions. The homogeneous regions are smoothed by using isotropic operators. Finally, the two regions are fused for K-means clustering. The majority voting algorithm is used to modify the clustering results, and the final segmentation results are obtained. Experimental results on simulated SAR images and real SAR images show that the proposed algorithm outperforms the other five state-of-the-art algorithms in segmentation speed and accuracy.

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“…Further, using affinity propagation clustering algorithm, [32] combines regional saliency and uses the random walks method for segmentation. Moreover, visual saliency with ACMs are proposed in [33] , [34] to enhance the segmentation results. However, these saliency-based models cannot accurately segment images with weaker edges due to inhomogeneity.…”

Section: Introductionmentioning

confidence: 99%

SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases

et al. 2020

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Noise or artifacts in an image, such as shadow artifacts, deteriorate the performance of stateof-the-art models for the segmentation of an image. In this study, a novel saliency-based region detection and image segmentation (SRIS) model is proposed to overcome the problem of image segmentation in the existence of noise and intensity inhomogeneity. Herein, a novel adaptive level-set evolution protocol based on the internal and external functions is designed to eliminate the initialization sensitivity, thereby making the proposed SRIS model robust to contour initialization. In the level-set energy function, an adaptive weight function is formulated to adaptively alter the intensities of the internal and external energy functions based on image information. In addition, the sign of energy function is modulated depending on the internal and external regions to eliminate the effects of noise in an image. Finally, the performance of the proposed SRIS model is illustrated on complex real and synthetic images and compared with that of the previously reported state-of-the-art models. Moreover, statistical analysis has been performed on coronavirus disease (COVID-19) computed tomography images and THUS10000 real image datasets to confirm the superior performance of the SRIS model from the viewpoint of both segmentation accuracy and time efficiency. Results suggest that SRIS is a promising approach for early screening of COVID-19.

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Active Contours Driven by Visual Saliency Fitting Energy for Image Segmentation in SAR Images

Cited by 12 publications

References 13 publications

Saliency-Driven Active Contour Model for Image Segmentation

Saliency-Driven Active Contour Model for Image Segmentation

SAR Image Segmentation Using Region Smoothing and Label Correction

SRIS: Saliency-Based Region Detection and Image Segmentation of COVID-19 Infected Cases

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