Robust image segmentation using local robust statistics and correntropy-based K-means clustering

Huang, Chencheng; Zeng, Li

doi:10.1016/j.optlaseng.2014.09.005

Cited by 25 publications

(4 citation statements)

References 43 publications

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“…Our future work may focus on extracting the objects from the segmented results and/or describing the semantic meanings in the processed images. The proposed method may be extended by cooperating the local robust statistics [15] or the machine leaning methods based on the artificial neural network [16] and fuzzy theory [17]. The effectiveness of applying the proposed SUCIS method onto the medical image processing [16], [18] can be investigated as well.…”

Section: Discussionmentioning

confidence: 99%

Segmentation using Codebook Index Statistics for Vector Quantized Images

Chang¹,

Su²

2016

ijacsa

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Abstract-In this paper, the segmentation using codebook index statistics (SUCIS) method is proposed for vector-quantized images. Three different codebooks are constructed according to the statistical characteristics (mean, variance, and gradient) of the codewords. Then they are employed to generate three different index images, which can be used to analyze the image contents including the homogeneous, edge, and texture blocks. An adaptive thresholding method is proposed to assign all image blocks in the compressed image to several disjoint regions with different characteristics. In order to make the segmentation result more accurate, two post-processing methods: the region merging and boundary smoothing schemes, are proposed. Finally, the pixel-wise segmentation result can be obtained by partitioning the image blocks at the single-pixel level. Experimental results demonstrate the effectiveness of the proposed SUCIS method on image segmentation, especially for the applications on object extraction.

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Section: Discussionmentioning

confidence: 99%

Segmentation using Codebook Index Statistics for Vector Quantized Images

Chang¹,

Su²

2016

ijacsa

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“…A common approach for robustness assessment involves the use of synthetic images, where the addition of varying levels of noise can be used for the assessment [72,73]. By adding perturbations, such as additive, multiplicative and speckle noise to synthetic images using the script in reference [74], we investigated the error in the edge points sampled using different edge localisation methods in the presence of varying image degradation levels.…”

Section: Robustness and Computational Complexitymentioning

confidence: 99%

Vision-Based Detection and Coordinate Metrology of a Spatially Encoded Multi-Sphere Artefact

Isa

Leach

Branson

et al. 2023

Preprint

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“…Although K-means has the great advantage of being easy to implement, it has certain drawbacks such as sensitivity to noise [23]. The quality of the final clustering result as well depends on the arbitrary selection of initial centroid.…”

Section: Background Information K-means Algorithmmentioning

confidence: 99%

Brain MR Segmentation using a Fusion of K-Means and Spatial Fuzzy C-Means

Oluwasanmi¹,

Qin²,

Lan³

2018

dtcse

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This study presents an automatic segmentation of the brain tissues in Magnetic Resonance Image using a fusion of Spatial Fuzzy C-Means (sFCM) and K-Means Algorithms (sFCMKA). The segmentation of the standard FCM algorithm have been realized to be highly sensitive to noise and therefore fail in providing accurate results. The K-Means algorithm while having the advantage of simplified clustering results is disadvantaged for merely classifying pixels into just one cluster segment. In overcoming this problem, first, FCM algorithm incorporated with a spatial neighborhood function is presented to eliminate the effect of noise. Also, the K-Means algorithm is embedded to sort pixels in tissues which are not intertwined with another. Since they are nonoverlapping tissues, such pixels are grouped in a singular cluster without having membership to another cluster segment. sFCMKA achieves this by placing a threshold parameter on pixels having contrasting cluster result from both algorithms and then reprocessed. The efficiency of the proposed algorithm is demonstrated by evaluating the results with the input image's manual segmentation using the Jaccard similarity index, Dice coefficient and accuracy index. The results of the proposed algorithm depicts a far more admirable accuracy quality compared to existing clustering based segmentation methods.

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Robust image segmentation using local robust statistics and correntropy-based K-means clustering

Cited by 25 publications

References 43 publications

Segmentation using Codebook Index Statistics for Vector Quantized Images

Segmentation using Codebook Index Statistics for Vector Quantized Images

Vision-Based Detection and Coordinate Metrology of a Spatially Encoded Multi-Sphere Artefact

Brain MR Segmentation using a Fusion of K-Means and Spatial Fuzzy C-Means

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