Deep Learning and Unsupervised Fuzzy C-Means Based Level-Set Segmentation for Liver Tumor

Zhang, Yue; Wu, Jiong; Jiang, Benxiang; Ji, Dongcen; Chen, Yifan; Wu, Ed X.; Tang, Xiaoying

doi:10.1109/isbi45749.2020.9098701

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…20 Another crucial factor is that the FCM's results are affected by the starting values of the parameters. In their work on possibilistic CM (PCM) clustering, Ramakrishnan et al and Zhang et al 21,22 suggested a new method called PCM. However, PCM almost eliminates the interdependence of the data points, which causes an increase in the parameters' complexity in determining which values to use.…”

Section: R E T R a C T E Dmentioning

confidence: 99%

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

et al. 2023

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The Editor-in-Chief and the publisher have retracted this article, which was submitted as part of a guest-edited special section. An investigation uncovered evidence of systematic manipulation of the publication process, including compromised peer review. The Editor and publisher no longer have confidence in the results and conclusions of the article. RD, KKH, BPD, and AKV either did not respond directly or could not be reached.

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Section: R E T R a C T E Dmentioning

confidence: 99%

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

et al. 2023

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“…The redistributed histogram is dissimilar from original histogram, because CL is utilized to restrict the pixel intensity [22]. Generally Medical images are low contrast, CLAHE is applied for enhancing medical images [23][24][25].…”

Section: Contrast Limited Adaptive Histogram Equalization (Clahe)mentioning

confidence: 99%

Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering

Rela¹,

Nagaraja²,

Ramana³

2020

IJACSA

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In computer aided diagnosis of liver tumor detection, tumor segmentation from the CT image is an important step. The majority of methods are not able to give an integrated structure for finding fast and effective tumor segmentation. Hence segmentation of tumor is most difficult task in diagnosing. In this paper, CT abdominal image is segmented using Superpixel-based fast Fuzzy C Means clustering algorithm to decrease the time needed for computation and eradicate the manual interface. In this algorithm, a superpixel image with perfect contour can be obtain using a Multiscale morphological gradient reconstruction operation. Superpixel is presegmentation algorithm and is employed to obtain segmentation accuracy. FCM with modified object is used to obtain the color segmentation. This method is examined on 20 CT images gathered from liveratlas database, results shows that this approach is fast and accurate compared to most of segmentation algorithms. Statistical parameters which include accuracy, precision, sensitivity, specificity, dice, rfn and rfp are calculated for segmented image. The results shows that this algorithm gives high accuracy of 99.58% and improved rfn value of 8.34% compared with methods discussed in the literature.

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“…In [11], a deep learning method is employed to acquire the initial contour, then a level-set method is used to evolve that initial contour and then segment medical images. In [12], at first, a deep learning method is employed for tumor segmentation in medical images, and then a level-set method improves the segmentation results. In [13], a level-set model with a deep prior method is proposed for image segmentation based on the priors learned by fully convolutional networks.…”

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

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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.

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Deep Learning and Unsupervised Fuzzy C-Means Based Level-Set Segmentation for Liver Tumor

Cited by 11 publications

References 10 publications

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

(Retracted) Deep belief network-based image processing for local directional segmentation in brain tumor detection

Liver Tumor Segmentation using Superpixel based Fast Fuzzy C Means Clustering

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

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