An efficient level set model with self-similarity for texture segmentation

Liu, Lixiong; Fan, Shengming; Ning, Xiaodong; Liao, Lejian

doi:10.1016/j.neucom.2017.05.028

Cited by 14 publications

(6 citation statements)

References 29 publications

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“…[ 52 ]. Figure 6 shows the comparison results of FCM, LINC [ 38 ], MICO [ 39 ], ARKFCM [ 52 ] LIC [ 37 ] and WLSM on brain MRI images with 5% noise. FCM is very sensitive to noise, and the segmentation effect is poor.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…This method is particularly effective for the image with clear boundaries. However, for the image with weak boundaries, the level set function is usually difficult to converge to the correct boundaries due to the destruction of noise and intensity inhomogeneity [ 37 ]. Li et al designed a distance regularized level set evolution (DRLSE) scheme [ 38 ], in which the image gradient information is used as driving force and regularization term is introduced into energy function to adjust the deviation between signed distance function and level set function to eliminate the defect of continuous re-initialization.…”

Section: Introductionmentioning

confidence: 99%

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Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Song

Zhang

2021

Entropy

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Magnetic resonance imaging (MRI) segmentation is a fundamental and significant task since it can guide subsequent clinic diagnosis and treatment. However, images are often corrupted by defects such as low-contrast, noise, intensity inhomogeneity, and so on. Therefore, a weighted level set model (WLSM) is proposed in this study to segment inhomogeneous intensity MRI destroyed by noise and weak boundaries. First, in order to segment the intertwined regions of brain tissue accurately, a weighted neighborhood information measure scheme based on local multi information and kernel function is designed. Then, the membership function of fuzzy c-means clustering is used as the spatial constraint of level set model to overcome the sensitivity of level set to initialization, and the evolution of level set function can be adaptively changed according to different tissue information. Finally, the distance regularization term in level set function is replaced by a double potential function to ensure the stability of the energy function in the evolution process. Both real and synthetic MRI images can show the effectiveness and performance of WLSM. In addition, compared with several state-of-the-art models, segmentation accuracy and Jaccard similarity coefficient obtained by WLSM are increased by 0.0586, 0.0362 and 0.1087, 0.0703, respectively.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Song

Zhang

2021

Entropy

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“…Recently, CNN-based methods with orderless feature pooling (Gong et al, 2014;Cimpoi et al, 2015;Zhang et al, 2017) have shown good performance on texture recognition, which was later proved to be beneficial for semantic segmentation (Zhang et al, 2018). In terms of texture segmentation, many approaches are based on active contours and integrate different texture features (Wu et al, 2015;Reska et al, 2015;Varnosfaderani and Moallem, 2017;Liu et al, 2017a;Yuan et al, 2015;Gao et al, 2016). Cimpoi et al (2015) proposed to use object detection-like region proposal classification to assign the texture/object labels to each pixel.…”

Section: Texture Recognition and Segmentationmentioning

confidence: 99%

Two-stage human hair segmentation in the wild using deep shape prior

Yan

Duffner

Naturel

et al. 2020

Pattern Recognition Letters

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Human hair is a crucial biometric characteristic with rich color and texture information. In this paper, we propose a novel hair segmentation approach integrating a deep shape prior into a carefully designed two-stage Fully Convolutional Neural Network (FCNN) pipeline. First, we utilize a FCNN with an Atrous Spatial Pyramid Pooling (ASPP) module to train a human hair shape prior based on a specific distance transform. In the second stage, we combine the hair shape prior and the original image to form the input of a symmetric encoder-decoder FCNN with a border refinement module to get the final hair segmentation output. Both quantitative and qualitative results show that our method achieves state-of-the-art performance on the LFW-Part and Figaro1k datasets.

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“…The major advantage of this approach is not limited to color. Liu et al [28] proposed a new segmenting method for texture image by using local Gaussian distribution fitting and local self-similarity that has a relatively low complexity. Subudhi et al [29] proposed a hybrid structural energy based on co-occurrence features of the image to evolve contour curves toward the desired texture boundary.…”

Section: Introductionmentioning

confidence: 99%

An Improved Matrix Factorization Based Active Contours Combining Edge Preservation for Image Segmentation

Jiang

2020

IEEE Access

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An efficient level set model with self-similarity for texture segmentation

Cited by 14 publications

References 29 publications

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Magnetic Resonance Imaging Segmentation via Weighted Level Set Model Based on Local Kernel Metric and Spatial Constraint

Two-stage human hair segmentation in the wild using deep shape prior

An Improved Matrix Factorization Based Active Contours Combining Edge Preservation for Image Segmentation

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