Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation

Li, Xuanping; Wang, Xue; Dai, Yixiang

doi:10.1007/s11265-017-1257-3

Cited by 11 publications

(8 citation statements)

References 27 publications

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“…Table 2 describes the statistical measures such as VOE, RVD, ASD, MSD, and RMSD of the proposed method compared with the conventional methods by referring to [24, 27–31], which evaluates the performance, i.e.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Li et al [24] proposed the robust active contour model, using optimum energy weight parameter values, is described with adaptive energy weight functions to dynamically adjust the contribution of each external energy term. With the coarse results obtained by the fuzzy C‐means clustering process, the initial level set functions are optimised.…”

Section: Literature Surveymentioning

confidence: 99%

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Empirical greedy machine‐based automatic liver segmentation in CT images

2020

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Empirical greedy machine‐based automatic liver segmentation in CT images

2020

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“…Furthermore, such estimation allows to make the use of AC models completely automatic without human interaction. Many ideas have been proposed to deal with this issue (Hu et al, 2017;Li et al, 2018;Hoogi et al, 2017). Our main contribution consists of proposing an automatic estimation of the optimum weights that define the contribution of each color component to the active contour energy functional.…”

Section: Introductionmentioning

confidence: 99%

A Histogram-Based Heuristic for an Adaptive Active Contours Color Image Segmentation

Boutiche,

Abdesselam,

Ramou

et al. 2024

Image Anal Stereol

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The fidelity to data (external energy) term in energy-based segmentation of scalar (single channel) images requires setting scalar values defining the weights assigned to the inside and outside energy functional. These values are often determined empirically, which is a tedious and time consuming task. When it comes to color images (multi-channel), the weights become vectors, which further complicates the process of identifying the appropriate weights. In this work, a new interpretation of the weight vector is introduced. It is seen as representing the contribution of each channel in the energy functional, that is equivalent to search an optimum color space. We propose a heuristic formula for estimating the values of the weight vector. It is based on the ratio of the height to the width of the color components histograms. We have applied the proposed formulation to Piecewise Constant Vector Valued (PCVV) model of Chan and Vese in both biphase and multiphase frameworks. Results of the experiments demonstrate the advantages of the proposed model over the commonly used trial and error setting of weights and the model based on color spaces mixing.

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“…Balsiger et al ( 16 ) proposed to improve traditional CNN-based volumetric image segmentation through the point-wise classification of point clouds, where the threshold to balance false positives and false negatives is manually selected. Li et al ( 17 ) proposed an active contour model based on adaptive energy weight functions for medical image segmentation with high accuracy. However, the results of the proposed model are affected by the noise and boundary leakage.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression

Peng

Tang

et al. 2022

Front. Oncol.

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Accurate prostate segmentation in transrectal ultrasound (TRUS) is a challenging problem due to the low contrast of TRUS images and the presence of imaging artifacts such as speckle and shadow regions. To address this issue, we propose a semi-automatic model termed Hybrid Segmentation Model (H-SegMod) for prostate Region of Interest (ROI) segmentation in TRUS images. H-SegMod contains two cascaded stages. The first stage is to obtain the vertices sequences based on an improved principal curve-based model, where a few radiologist-selected seed points are used as prior. The second stage is to find a map function for describing the smooth prostate contour based on an improved machine learning model. Experimental results show that our proposed model achieved superior segmentation results compared with several other state-of-the-art models, achieving an average Dice Similarity Coefficient (DSC), Jaccard Similarity Coefficient (Ω), and Accuracy (ACC) of 96.5%, 95.2%, and 96.3%, respectively.

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Adaptive Energy Weight Based Active Contour Model for Robust Medical Image Segmentation

Cited by 11 publications

References 27 publications

Empirical greedy machine‐based automatic liver segmentation in CT images

Empirical greedy machine‐based automatic liver segmentation in CT images

A Histogram-Based Heuristic for an Adaptive Active Contours Color Image Segmentation

Semi-Automatic Prostate Segmentation From Ultrasound Images Using Machine Learning and Principal Curve Based on Interpretable Mathematical Model Expression

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