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Paper Citation Record · LEDGER

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation

As of 13 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2607.22255.

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pith.paper-citation-record.v1
2607.22255 v1

Coverage vector

measured 33 of 33 reference resolution

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measured 33 of 33 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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Reference resolution

33 of 33 outbound references displayed

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Outbound references

Observation 63cce0ab-d2bf-4512-b950-2670ebdfaa05 · outbound

This paper cites Gore, and Zhaohua Ding.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Gore, and Zhaohua Ding

Reference 1

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Observation 3b963d6e-f734-4f63-94fe-f44543934590 · outbound

This paper cites Chris Gatenby, Dimitris N.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Chris Gatenby, Dimitris N

Reference 2

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Observation 351e588b-d113-4804-9c0c-5d6fe57ad27b · outbound

This paper cites Deep convolutional neural networks with spatial regularization, volume and star-shape priors for image segmentation.Journal of Mathematical Imaging and Vision, 64(6): 625–645, 2022.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Deep convolutional neural networks with spatial regularization, volume and star-shape priors for image segmentation.Journal of Mathematical Imaging and Vision, 64(6): 625–645, 2022

Reference 3

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Observation 786c869e-ef2b-4310-a117-a1dd830abd41 · outbound

This paper cites Convex shape prior for deep convolution neural network-based image segmentation.Journal of Mathematical Imaging and Vision, 67(6):61, 2025.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Convex shape prior for deep convolution neural network-based image segmentation.Journal of Mathematical Imaging and Vision, 67(6):61, 2025

Reference 4

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Observation 6259e2db-928f-4b06-987a-61106323fa46 · outbound

This paper cites Additive-bias-correction variational model for noisy and intensity-inhomogeneous image segmentation.SIAM Journal on Imaging Sciences, 18(2):1235–1259, 2025.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Additive-bias-correction variational model for noisy and intensity-inhomogeneous image segmentation.SIAM Journal on Imaging Sciences, 18(2):1235–1259, 2025

Reference 5

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Observation d6f1a0a5-2c82-4e7d-b891-a98cce84d172 · outbound

This paper cites Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations.Journal of Computational Physics, 79(1):12–49, 1988.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations.Journal of Computational Physics, 79(1):12–49, 1988

Reference 6

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Observation 6cedc2dc-baa9-4ea9-aa1f-642736e7f5a2 · outbound

This paper cites Snakes: Active contour models.International Journal of Computer Vision, 1(4):321–331, 1988.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Snakes: Active contour models.International Journal of Computer Vision, 1(4):321–331, 1988

Reference 7

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Observation a836c1f9-c6b1-4753-8460-bf45db581286 · outbound

This paper cites Caselles, R.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Caselles, R

Reference 8

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Observation 97c5765c-d206-4f08-ba2a-9030778cc59d · outbound

This paper cites Optimal approximations by piecewise smooth functions and associated variational problems.Communications on Pure & Applied Mathematics, 42(5):577–685, 1989.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Optimal approximations by piecewise smooth functions and associated variational problems.Communications on Pure & Applied Mathematics, 42(5):577–685, 1989

Reference 9

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Observation 6ee168f0-337d-478d-baff-9ea1f7a50a16 · outbound

This paper cites Chan and L.A.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Chan and L.A

Reference 10

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From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Unresolved cited work

Reference 11

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Observation 41b3547f-6a06-4a38-a60e-ecbbfcdb5a1b · outbound

This paper cites A modified level set algorithm based on point dis- tance shape constraint for lesion and organ segmentation.Physica Medica, 57:123–136, 2019.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation A modified level set algorithm based on point dis- tance shape constraint for lesion and organ segmentation.Physica Medica, 57:123–136, 2019

Reference 12

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Observation b96b84a2-f540-44be-8efe-89eb5c05cb13 · outbound

This paper cites Convexity shape prior for level set-based image segmen- tation method.IEEE Transactions on Image Processing, 29:7141–7152, 2020.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Convexity shape prior for level set-based image segmen- tation method.IEEE Transactions on Image Processing, 29:7141–7152, 2020

Reference 13

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Observation bf587d89-4bd3-4314-a563-4dc79eae82b9 · outbound

This paper cites Image segmentation for intensity inhomogeneity in presence of high noise.IEEE Transactions on Image Processing, 27(8):3729–3738, 2018.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Image segmentation for intensity inhomogeneity in presence of high noise.IEEE Transactions on Image Processing, 27(8):3729–3738, 2018

Reference 14

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Observation af9c38a3-cd45-49f8-93c9-c991b11c3643 · outbound

This paper cites A variational image segmentation model with intensity correction in the presence of high level multiplicative noise.Inverse Problems and Imaging, 19(5):877–902, 2025.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation A variational image segmentation model with intensity correction in the presence of high level multiplicative noise.Inverse Problems and Imaging, 19(5):877–902, 2025

Reference 15

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Observation 15bffe6f-5186-406f-89dd-477c1ba7b49b · outbound

This paper cites A three-stage variational image segmentation framework incor- porating intensity inhomogeneity information.SIAM Journal on Imaging Sciences, 13(3):1692–1715, 2020.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation A three-stage variational image segmentation framework incor- porating intensity inhomogeneity information.SIAM Journal on Imaging Sciences, 13(3):1692–1715, 2020

Reference 16

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Observation b3d188f5-1028-4ad3-b3ef-09147a1a1be8 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation U-net: Convolutional networks for biomedical image segmentation

Reference 17

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Observation 28d274ac-30f8-4389-8b5c-f4866bac7f72 · outbound

This paper cites Swin-unet: Unet-like pure transformer for medical image segmentation.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Swin-unet: Unet-like pure transformer for medical image segmentation

Reference 18

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Observation f922d93d-bbf6-4301-adfa-0722757ce398 · outbound

This paper cites Lungren, Shaoting Zhang, Lei Xing, Le Lu, Alan Yuille, and Yuyin.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Lungren, Shaoting Zhang, Lei Xing, Le Lu, Alan Yuille, and Yuyin

Reference 19

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Observation 6a2c4f0e-bd3e-47f5-9a91-e048cc4588bf · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

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Observation 2e02c768-41b4-46d5-bfd3-362d8e625ca0 · outbound

This paper cites Berg, and Wan Yen Lo.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Berg, and Wan Yen Lo

Reference 21

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This paper cites Rudin, Stanley Osher, and Emad Fatemi.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Rudin, Stanley Osher, and Emad Fatemi

Reference 22

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From level set evolution to threshold optimization: A grayscale level set framework for image segmentation He and Hao Liu

Reference 23

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From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Unresolved cited work

Reference 24

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This paper cites Image denoising by sparse 3-d transform-domain collaborative filtering.IEEE Transactions on Image Processing, 16(8):2080–2095, 2007.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Image denoising by sparse 3-d transform-domain collaborative filtering.IEEE Transactions on Image Processing, 16(8):2080–2095, 2007

Reference 25

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This paper cites Hybrid bm3d and pde filter- ing for non-parametric single image denoising.Signal Processing, 184:108049, 2021.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Hybrid bm3d and pde filter- ing for non-parametric single image denoising.Signal Processing, 184:108049, 2021

Reference 26

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This paper cites Tustison, Brian B.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Tustison, Brian B

Reference 27

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This paper cites Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.IEEE Transactions on Image Processing, 26(7):3142–3155, 2016.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising.IEEE Transactions on Image Processing, 26(7):3142–3155, 2016

Reference 28

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This paper cites A variational model to remove multiplicative noise based on sar image feature preservation.Inverse Problems and Imaging, 19(2):253–281, 2025.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation A variational model to remove multiplicative noise based on sar image feature preservation.Inverse Problems and Imaging, 19(2):253–281, 2025

Reference 29

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This paper cites URL https://www.sciencedirect.com/science/ article/pii/S0165168421000888.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation URL https://www.sciencedirect.com/science/ article/pii/S0165168421000888

Reference 1684

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This paper cites URL https://www.sciencedirect.com/science/ article/pii/S1120179718313681.

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation URL https://www.sciencedirect.com/science/ article/pii/S1120179718313681

Reference 1797

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From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Unresolved cited work

Reference 2001

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From level set evolution to threshold optimization: A grayscale level set framework for image segmentation Unresolved cited work

Reference 2010

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