New neutrosophic approach to image segmentation

Guo, Yanhui; Cheng, Heng-Da

doi:10.1016/j.patcog.2008.10.002

Cited by 208 publications

(167 citation statements)

References 12 publications

Supporting

Mentioning

167

Contrasting

Order By: Relevance

“…In neutrosophy theory, every event has not only a certain degree of the truth, but also a falsity degree and an indeterminacy degree that have to be considered independently from each other [21]. Thus, a theory, event, concept, or entity, { } Neutrosophic set had been applied to image thresholding, image segmentation and image denoising applications [22][23][24][25][26]. Cheng and Guo [22] proposed a thresholding algorithm based on neutrosophy, which could select the thresholds automatically and effectively.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Cheng and Guo [22] proposed a thresholding algorithm based on neutrosophy, which could select the thresholds automatically and effectively. The NS approach of image segmentation for real images [23] and for color texture image segmentation discussed in [24]. In [25], some concepts and operators were defined based on NS and applied for image denoising.…”

Section: Proposed Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Mohan¹,

Krishnaveni²,

Guo³

2013

Measurement Science Review

Self Cite

View full text Add to dashboard Cite

In this paper, a new filtering method based on neutrosophic set (NS) approach of wiener filter is presented to remove Rician noise from magnetic resonance image. A neutrosophic set, a part of neutrosophy theory, studies the origin, nature and scope of neutralities, as well as their interactions with different ideational spectra. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image is transformed into NS domain, described using three membership sets: True (T), Indeterminacy (I) and False (F). The entropy of the neutrosophic set is defined and employed to measure the indeterminacy. The ω-wiener filtering operation is used on T and F to decrease the set indeterminacy and remove noise. The experiments have conducted on simulated Magnetic Resonance images (MRI) from Brainweb database and clinical MR images corrupted by Rician noise. The results show that the NS wiener filter produces better denoising results in terms of visual perception, qualitative and quantitative measures compared with other denoising methods, such as classical wiener filter, the anisotropic diffusion filter, the total variation minimization scheme and non local means filter.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Mohan¹,

Krishnaveni²,

Guo³

2013

Measurement Science Review

Self Cite

View full text Add to dashboard Cite

show abstract

“…These three functions are independent completely. The neutrosophic set has vast applications in various fields (see [2][3][4][5][6]). …”

Section: Introductionmentioning

confidence: 99%

Neutrosophic Positive Implicative N -Ideals in BCK-Algebras

Jun

Smarandache

Song

et al. 2018

Axioms

View full text Add to dashboard Cite

Abstract:The notion of a neutrosophic positive implicative N -ideal in BCK-algebras is introduced, and several properties are investigated. Relations between a neutrosophic N -ideal and a neutrosophic positive implicative N -ideal are discussed. Characterizations of a neutrosophic positive implicative N -ideal are considered. Conditions for a neutrosophic N -ideal to be a neutrosophic positive implicative N -ideal are provided. An extension property of a neutrosophic positive implicative N -ideal based on the negative indeterminacy membership function is discussed.

show abstract

“…In NS theory, a member of a set has degrees to the truth, falsity, and indeterminacy, respectively [5]. Therefore, it has an ability to deal with the indeterminacy information and has attracted much attention in almost all engineering communities and subsequently a great number of works have been studied, such as NS-based color and texture segmentation [6][7][8][9][10][11][12][13][14], NS-based Ts(x, y) = g(x, y) − g min g max − g min (1) Is(x, y) = Gd(x, y) − Gd min Gd max − Gd min (2) where g(x,y) and Gd(x,y) are the intensity and gradient magnitude at the pixel of (x,y) on the image. We also compute the neutrosophic membership values based on the global intensity distribution which considers the indeterminacy on intensity between different groups.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut

et al. 2017

Self Cite

View full text Add to dashboard Cite

Segmentation is considered as an important step in image processing and computer vision applications, which divides an input image into various non-overlapping homogenous regions and helps to interpret the image more conveniently. This paper presents an efficient image segmentation algorithm using neutrosophic graph cut (NGC). An image is presented in neutrosophic set, and an indeterminacy filter is constructed using the indeterminacy value of the input image, which is defined by combining the spatial information and intensity information. The indeterminacy filter reduces the indeterminacy of the spatial and intensity information. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algorithm on the graph. Numerous experiments have been taken to test its performance, and it is compared with a neutrosophic similarity clustering (NSC) segmentation algorithm and a graph-cut-based algorithm. The results indicate that the proposed NGC approach obtains better performances, both quantitatively and qualitatively.

show abstract

New neutrosophic approach to image segmentation

Cited by 208 publications

References 12 publications

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Neutrosophic Positive Implicative N -Ideals in BCK-Algebras

An Efficient Image Segmentation Algorithm Using Neutrosophic Graph Cut

Contact Info

Product

Resources

About