Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing

Baâzaoui, Abir; Barhoumi, Walid; Ahmed, Amr; Zagrouba, Ezzeddine

doi:10.1016/j.irbm.2017.02.003

Cited by 51 publications

(17 citation statements)

References 9 publications

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“…It makes up for the lack of spatial relationship that threshold segmentation does not or seldom consider. For example, Baazaoui et al [21] proposed an entropy-based fuzzy region growing method to segment single or multiple liver cancer lesions. 2) The level set method has been gradually applied to liver segmentation due to its advantages of involving numerical calculation of curves and surfaces [22].…”

Section: Related Work a Hand-crafted Feature-based Methodsmentioning

confidence: 99%

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

et al. 2021

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Section: Related Work a Hand-crafted Feature-based Methodsmentioning

confidence: 99%

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

et al. 2021

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“…Based on the definition of Shannon entropy, the 3-D discrete entropy can be defined as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 4 ; 6 3 ; 4 3…”

Section: Three-dimensional Maximum Entropy Methodsmentioning

confidence: 99%

“…1 Recently, considerable research on image segmentation has been conducted, and various segmentation algorithms have been proposed. In general, these algorithms can be divided into: edge detection segmentation algorithms, 2 region segmentation algorithms, 3 threshold segmentation algorithms, 4 and fuzzy segmentation algorithms. 5 Among them, threshold segmentation algorithms have been widely studied and applied owing to their simplicity and effectiveness.…”

Section: Introductionmentioning

confidence: 99%

Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

Lei

Ouyang

2018

Opt. Eng.

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To improve the timeliness of the three-dimensional (3-D) maximum entropy method, an image segmentation method based on equivalent 3-D entropy and artificial fish swarm optimization algorithm is proposed. An equivalent 3-D entropy method without logarithmic operation is developed, and its equivalence is proved theoretically. The optimal threshold is determined based on the artificial fish swarm optimization algorithm so as to avoid exhaustive search and improve algorithm efficiency. The experimental results demonstrate that the proposed method is more time-efficient than the traditional 3-D entropy method and the equivalent 3-D entropy method without affecting segmentation. Compared with the one-dimensional entropy method and the two-dimensional entropy method, it is obviously superior in noise immunity and detail preservation.

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“…The content similarity is computed as a comparison of the two regions, whereas the border similarity is defined based on the similarity between the connected superpixels that form the border from each region. Thus the general form of the similarity measure between two regions, R i , R j ∈ Ψ r , can be expressed by Equation 5.…”

Section: Regions Similarity Measurementioning

confidence: 99%

Adaptive strategy for superpixel-based region-growing image segmentation

Chaibou

Conze

Kalti

et al. 2017

J. Electron. Imaging

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International audienceThis work presents a region-growing image segmentation approach based on superpixel decomposition. From an initial contour-constrained oversegmentation of the input image, the image segmentation is achieved by iteratively merging similar superpixels into regions. This approach raises two key issues: (1) how to compute the similarity between superpixels in order to perform accurate merging and (2) in which order those superpixels must be merged together. In this perspective, we first introduce a robust adaptive multiscale superpixel similarity in which region comparisons are made both at content and common border level. Second, we propose a global merging strategy to efficiently guide the region merging process. Such strategy uses an adaptive merging criterion to ensure that best region aggregations are given highest priorities. This allows the ability to reach a final segmentation into consistent regions with strong boundary adherence. We perform experiments on the BSDS500 image dataset to highlight to which extent our method compares favorably against other well-known image segmentation algorithms. The obtained results demonstrate the promising potential of the proposed approach

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Semi-Automated Segmentation of Single and Multiple Tumors in Liver CT Images Using Entropy-Based Fuzzy Region Growing

Cited by 51 publications

References 9 publications

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

Image segmentation based on equivalent three-dimensional entropy method and artificial fish swarm optimization algorithm

Adaptive strategy for superpixel-based region-growing image segmentation

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