Multi-class Image Segmentation Using Theory of Weak String Energy and Fuzzy Set

The Fuzzy C-Means algorithm with spatial informations and membership constrains is a very effective and efficient image segmentation method. However£¬it is founded with Type-1 fuzzy sets, which can not handle the uncertainties existing in liver image well.The type-2 fuzzy sets have better performance on handling uncertainties than Type-1 fuzzy set. In this paper, a new robust Type-2 FCM image segmentation algorithm is proposed aiming to improve the segmentation precision and robustness of liver image by introducing the type-2 fuzzy set into FCM with spatial information and membership constrains. We extend the type-1 fuzzy set of membership to interval type-2 fuzzy set using two fuzzifiers and which create a footprint of uncertainty (FOU). The experimental results show that the target area of the liver in CT images can be segmented well by the proposed method.

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Intuitionistic Fuzzy Set: Application to Medical Image Segmentation

Computational Intelligence in Medical Informatics - Studies in Computational Intelligence ◽

10.1007/978-3-540-75767-2_3 ◽

2008 ◽

pp. 51-68 ◽

Cited By ~ 3

Author(s):

Tamalika Chaira ◽

Tridib Chaira

Keyword(s):

Image Segmentation ◽

Fuzzy Set ◽

Medical Image ◽

Intuitionistic Fuzzy Set ◽

Medical Image Segmentation ◽

Intuitionistic Fuzzy

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Intuitionistic fuzzy set approach to multi-objective evolutionary clustering with multiple spatial information for image segmentation

Neurocomputing ◽

10.1016/j.neucom.2018.05.116 ◽

2018 ◽

Vol 312 ◽

pp. 296-309 ◽

Cited By ~ 13

Author(s):

Feng Zhao ◽

Hanqiang Liu ◽

Jiulun Fan ◽

Chang Wen Chen ◽

Rong Lan ◽

...

Keyword(s):

Image Segmentation ◽

Fuzzy Set ◽

Spatial Information ◽

Intuitionistic Fuzzy Set ◽

Multi Objective ◽

Evolutionary Clustering ◽

Intuitionistic Fuzzy

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Hesitant fuzzy C-means algorithm and its application in image segmentation†

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-191973 ◽

2020 ◽

Vol 39 (3) ◽

pp. 3681-3695

Author(s):

Wenyi Zeng ◽

Rong Ma ◽

Qian Yin ◽

Xin Zheng ◽

Zeshui Xu

Keyword(s):

Image Segmentation ◽

Fuzzy Set ◽

Distance Measure ◽

Hesitant Fuzzy Set ◽

Natural Image ◽

Membership Degree ◽

Fuzzy C Means ◽

Comprehensive Performance ◽

Multi Attribute Decision Making ◽

Fuzzy C Means Algorithm

Image segmentation plays an important role in many fields such as computer vision, pattern recognition, machine learning and so on. In recent years, many variants of standard fuzzy C-means (FCM) algorithm have been proposed to explore how to remove noise and reduce uncertainty. In fact, there are uncertainty on the boundary between different patches in images. Considering that hesitant fuzzy set is a useful tool to deal with uncertainty, in this paper, we merge hesitant fuzzy set with fuzzy C-means algorithm, introduce a new kind of method of fuzzification and defuzzification of image and the distance measure between hesitant fuzzy elements of pixels, present a method to establish hesitant membership degree of hesitant fuzzy element, and propose hesitant fuzzy C-means (HFCM) algorithm. Finally, we compare our proposed HFCM algorithm with some existing fuzzy C-means (FCM) algorithms, and apply HFCM algorithm in natural image, BSDS dataset image, different size images and multi-attribute decision making. These numerical examples illustrate the validity and applicability of our proposed algorithm including its comprehensive performance, reducing running time and almost without loss of accuracy.

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Key Algorithms for Segmentation of Copperplate Printing Image Based on Deep Learning

Mobile Information Systems ◽

10.1155/2021/9940801 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Ye Zhang ◽

Qiu Xie ◽

Canlin Zhang

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Fuzzy Set ◽

Image Synthesis ◽

Original Data ◽

Synthesis Process ◽

Learning Technology ◽

The Neural Network ◽

Functional Learning ◽

Main Color

As a branch of the field of machine learning, deep learning technology is abrupt in various computer vision tasks with its powerful functional learning functions. The deep learning method can extract the required features from the original data and dynamically adjust and update the parameters of the neural network through the backpropagation algorithm so as to achieve the purpose of automatically learning features. Compared with the method of extracting features manually, the recognition accuracy is improved, and it can be used for the segmentation of copperplate printing images. This article mainly introduces the research on the key algorithm of the copperplate printing image segmentation based on deep learning and intends to provide some ideas and directions for improving the copperplate printing image segmentation technology. This paper introduces the related principles, watershed algorithm, and guided filtering algorithm of copperplate printing image synthesis process and establishes an image segmentation model. As a result, a deep learning-based optimization algorithm mechanism for the segmentation of copper engraving printing images is proposed, and experimental steps such as main color extraction in the segmentation of copper engraving printing images, adaptive main color extraction based on fuzzy set 2, and main color extraction based on fuzzy set 2 are proposed. Experimental results show that the average processing time of each image segmentation model in this paper is 0.39 seconds, which is relatively short.

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Image segmentation using trainable fuzzy set classifiers

10.1117/12.354700 ◽

1999 ◽

Author(s):

Robert J. Schalkoff ◽

Albrecht E. Carver ◽

Sabri Gurbuz

Keyword(s):

Image Segmentation ◽

Fuzzy Set

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