Convex and Compact Superpixels by Edge- Constrained Centroidal Power Diagram

Ma, Dongyang; Zhou, Yuanfeng; Xin, Shiqing; Wang, Wenping

doi:10.1109/tip.2020.3045640

Cited by 14 publications

(8 citation statements)

References 46 publications

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“…where 𝑧 𝑖 is the number of generated superpixels for the image 𝑖, ẑ is the average number of superpixels, and 𝑀 is the number of images. Some papers Wu et al (2021); Tu et al (2018); Xu et al (2022); Ye et al (2019); Ma, Zhou, Xin and Wang (2021) evaluate the performance of superpixel methods with respect to the required superpixel number, which is suitable for evaluating superpixel methods in which the generated superpixel number is close to the required one. However, some superpixel methods, such as LNS-Net and SSN, generate far more superpixels than required.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A survey of superpixel methods and their applications

Wu,

Yan

2024

Preprint

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Superpixels can preserve the structure and reduce the redundancy of the original image. Because of these advantages, superpixel generation or superpixel segmentation is widely used as a pre-processing step in many image processing tasks. Although superpixels can be employed to reduce computational complexity, some challenges, such as the non-Euclidean feature learning problem introduced by superpixels, still exist. This survey provides a comprehensive overview of the state-of-the-art superpixel methods, major challenges, commonly used evaluation metrics, applications of superpixels, and potential future directions for the study of superpixels. We first give a review of the state-of-the-art superpixel methods. Next, we use different evaluation metrics to evaluate the performance of 25 up-to-date superpixel methods on different datasets in different noisy environments. After that, we introduce several up-to-date applications of superpixels. Finally, we give several possible future directions for addressing the challenges of superpixels.

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Section: Evaluation Metricsmentioning

confidence: 99%

A survey of superpixel methods and their applications

Wu,

Yan

2024

Preprint

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“…As we all know, almost all images have high information redundancy either in the form of low rank or sparse representation [11,12]: many pixels share similar features. Based on a low-rank prior or sparse representation, images can be denoised [13][14][15][16][17]. For medical images with limited intensity levels, the phenomenon of low rank is particularly obvious.…”

Section: Motivation and Contributionmentioning

confidence: 99%

Improved fuzzy clustering for image segmentation based on a low-rank prior

et al. 2021

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Image segmentation is a basic problem in medical image analysis and useful for disease diagnosis. However, the complexity of medical images makes image segmentation difficult. In recent decades, fuzzy clustering algorithms have been preferred due to their simplicity and efficiency. However, they are sensitive to noise. To solve this problem, many algorithms using non-local information have been proposed, which perform well but are inefficient. This paper proposes an improved fuzzy clustering algorithm utilizing nonlocal self-similarity and a low-rank prior for image segmentation. Firstly, cluster centers are initialized based on peak detection. Then, a pixel correlation model between corresponding pixels is constructed, and similar pixel sets are retrieved. To improve efficiency and robustness, the proposed algorithm uses a novel objective function combining non-local information and a low-rank prior. Experiments on synthetic images and medical images illustrate that the algorithm can improve efficiency greatly while achieving satisfactory results.

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“…There are several methods considering generating superpixels with controllable size, MSHIFT, FH, [25][26][27][28]. Achanta et al [25] proposed a scale adaptive superpixel based on the local texture and scale of an image (Adaptel).…”

Section: Introductionmentioning

confidence: 99%

“…[25,26] are designed for natural images, without considering the characteristics of medical images. In [27], Bauchet and Lafarge can generate polygons with different sizes, but the size of the superpixels are not controllable. In [28], Ma et al can generate convex polygons with controllable size by using features such as image boundary.…”

Section: Introductionmentioning

confidence: 99%

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Scale‐adaptive superpixels for medical images

Sun

Zhou

2022

Quant. Biol.

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Background: Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing. Traditionally, size uniformity is one of the significant features of superpixels. However, in medical images, in which subjects scale varies greatly and background areas are often flat, size uniformity rarely conforms to the varying content. To obtain the fewest superpixels with retaining important details, the size of superpixel should be chosen carefully. Methods: We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images, especially pathological images. A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content, that is smaller (larger) superpixels in color-riching areas (flat areas). Results: The proposed superpixel algorithm can generate superpixels with boundary adherence, insensitive to noise, and with extremely big sizes and extremely small sizes on one image. The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images. Conclusion:With the proposed algorithm, the choice of superpixel size is automatic, which frees the user from the predicament of setting suitable superpixel size for a given application. The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons.

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Convex and Compact Superpixels by Edge- Constrained Centroidal Power Diagram

Cited by 14 publications

References 46 publications

A survey of superpixel methods and their applications

A survey of superpixel methods and their applications

Improved fuzzy clustering for image segmentation based on a low-rank prior

Scale‐adaptive superpixels for medical images

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