Enhanced interval type-2 fuzzy c-means algorithm with improved initial center

Qiu, Cunyong; Xiao, Jian; Han, Lu; Iqbal, Muhammad Naveed

doi:10.1016/j.patrec.2013.11.011

Cited by 23 publications

(14 citation statements)

References 18 publications

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“…Among these papers [7,16,17,[23][24][25][26]32], they were all validated using databases of real images from varied sources and in combination, used several types of validation techniques, such as a visual appreciation of the end result, recognition rate, error rate, and number of produced segments. This leads to a highly-varied collection of experimental tests performed among these papers.…”

Section: Discussionmentioning

confidence: 99%

“…In Cunyong Qiu et al (2014) [16], an enhanced IT2 FCM clustering algorithm was proposed which reduces the common shortfalls of uncertainty handling from the normal FCM; this is done by focusing on the cluster center initialization as well as optimizing the type-reduction. This approach was validated through image segmentation datasets.…”

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 99%

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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“…Here, ‘ N ’ is the total quantity of pixels present in the input MR slice image. Assigning

ε = 10^{- 5}

as per the suggestions given by Qiu et al [26] provide the degree of optimality. Step 2 : the SOM map mentioned as

ω_{i} ()(, t + 1)

is given as input to the IT2FLS methodology. The values

μ

and

σ^{2}

, the mean and variance used for limiting the over‐fitting problem of SOM prototype are used once again for the formulation of cluster boundaries for the IT2FLS algorithm.…”

Section: Materials and Methodologymentioning

confidence: 99%

“…

μ + σ^{2} / 2

), the breadth and depth of the Gaussian membership function can be considered. This helps the user to include all the pixels for segmentation without any omission or negligence of pixel values (motivated by Qiu et al [26], Vishnuvarthanan et al [2], and Alagarsamy et al [27]). The scalar unit used for membership formulation is named for convenience as AV parameter, and this parameter is used to define lower membership and higher membership functions of the IT2FLS algorithm.…”

Section: Materials and Methodologymentioning

confidence: 99%

Automated unsupervised learning‐based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI)

et al. 2020

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“…To overcome previous limitations in the segmentation technique of the nuclear chromatin, this study employs Fuzzy C-Means (FCM) clustering technique due to its simplicity and effectiveness in yielding promising results [ 25 , 26 ]. It is an unsupervised algorithm, in addition to its robustness for ambiguity and its ability which always converges.…”

Section: Introductionmentioning

confidence: 99%

A Fuzzy-C-Means-Clustering Approach: Quantifying Chromatin Pattern of Non-Neoplastic Cervical Squamous Cells

Tang¹,

Isa²,

Ch’ng³

2015

PLoS ONE

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Despite the effectiveness of Pap-smear test in reducing the mortality rate due to cervical cancer, the criteria of the reporting standard of the Pap-smear test are mostly qualitative in nature. This study addresses the issue on how to define the criteria in a more quantitative and definite term. A negative Pap-smear test result, i.e. negative for intraepithelial lesion or malignancy (NILM), is qualitatively defined to have evenly distributed, finely granular chromatin in the nuclei of cervical squamous cells. To quantify this chromatin pattern, this study employed Fuzzy C-Means clustering as the segmentation technique, enabling different degrees of chromatin segmentation to be performed on sample images of non-neoplastic squamous cells. From the simulation results, a model representing the chromatin distribution of non-neoplastic cervical squamous cell is constructed with the following quantitative characteristics: at the best representative sensitivity level 4 based on statistical analysis and human experts’ feedbacks, a nucleus of non-neoplastic squamous cell has an average of 67 chromatins with a total area of 10.827μm 2; the average distance between the nearest chromatin pair is 0.508μm and the average eccentricity of the chromatin is 0.47.

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Enhanced interval type-2 fuzzy c-means algorithm with improved initial center

Cited by 23 publications

References 18 publications

Review of Recent Type-2 Fuzzy Image Processing Applications

Review of Recent Type-2 Fuzzy Image Processing Applications

Automated unsupervised learning‐based clustering approach for effective anomaly detection in brain magnetic resonance imaging (MRI)

A Fuzzy-C-Means-Clustering Approach: Quantifying Chromatin Pattern of Non-Neoplastic Cervical Squamous Cells

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