A local mean and variance active contour model for biomedical image segmentation

Peng, Yali; Liu, Shigang; Qiang, Yan; Wu, Xiaojun; Hong, Ling

doi:10.1016/j.jocs.2019.03.003

Cited by 23 publications

(8 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Biomedical images analysis [1–4] is a useful tool for researchers to quickly and accurately process a great mount of image data. There are a wide variety of applications for biomedical images, some of which include being able to distinguish between different neurons to identify specific pathogens or disease.…”

Section: Introductionmentioning

confidence: 99%

DenseUNet: densely connected UNet for electron microscopy image segmentation

et al. 2020

Self Cite

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

DenseUNet: densely connected UNet for electron microscopy image segmentation

et al. 2020

Self Cite

View full text Add to dashboard Cite

“…In addition, based on the third-party experiments [74], the excellent performance of our HCRF model is also verified. In their experiments, the HCRF and other state-of-the-art methods (BFC [75], SAM [76], FRFCM [77], MDRAN [78], LVMAC [79], PABVS [80], FCMRG [81]) are used for nuclei segmentation, and our HCRF model perform well, second only to the method proposed for their task in this experiment.…”

Section: Evaluation Of Am Modulementioning

confidence: 90%

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

Li¹,

Wu²,

Li³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…However, this model is sensitive to the initial contour placement and cannot effectively deal with severe intensity inhomogeneity. Peng et al [32] recently proposed a local mean and variance (LMV)-based ACM to segment medical images with inhomogeneity. The LMV model considers the distribution of intensity belonging to foreground and background regions as Gaussian distributions with varying means and variances.…”

Section: Related Workmentioning

confidence: 99%

A level‐set method for inhomogeneous image segmentation with application to breast thermography images

Koshki

Ahmadzadeh

Zekri

et al. 2020

IET Image Processing

View full text Add to dashboard Cite

Various level‐set methods have been suggested for segmenting images with intensity inhomogeneity as local region‐based models. The challenge in these methods is segmenting the inhomogeneous images with smooth edges. These methods cannot properly segment regions with smooth edges in inhomogeneous images. This paper presents a new local region‐based active contour model called local self‐weighted active contour model. In the proposed method, a novel different weighting technique is applied. In this model, the weight of each neighbour pixel in the energy function is set by a function of its intensity and not its geometrical distance regarding the central pixel as previous methods. Considering this, the presented approach can segment regions with smooth edges in the presence of inhomogeneity as breast thermography images. The experimental results of applying the model on heterogeneous images containing synthetic images and medical images, especially breast thermography images, are compared with well‐known local level‐set methods which show the perfect capability of the model. The segmentation results were evaluated using the F‐score, accuracy, precision and recall criteria. The results show values of 0.8, 0.62, 0.73 and 0.82 for the average accuracy, F‐score, precision and recall criteria on the segmentation of breast thermography images, respectively.

show abstract

A local mean and variance active contour model for biomedical image segmentation

Cited by 23 publications

References 50 publications

DenseUNet: densely connected UNet for electron microscopy image segmentation

DenseUNet: densely connected UNet for electron microscopy image segmentation

A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification

A level‐set method for inhomogeneous image segmentation with application to breast thermography images

Contact Info

Product

Resources

About