Using Fuzzy C-means Cluster for Histogram-Based Color Image Segmentation

Huang, Zhi-Kai; Xie, Yun-Ming; Liu, De-Hui; Hou, Ling-Ying

doi:10.1109/itcs.2009.130

Cited by 15 publications

(3 citation statements)

References 11 publications

(4 reference statements)

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“…Color feature is a global feature that describes the surface properties of the scene corresponding to the image or image region. The color feature extraction has color histogram, including quantized color histogram and cluster color histogram [34], [35]. Texture feature is also a global feature that also describes the surface properties of a scene corresponding to an image or image region.…”

Section: B Image Feature Extraction Methodsmentioning

confidence: 99%

Channel State Discrimination Method Based on Multi-Structure Color Difference Histogram

2020

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The determination of channel characteristics has always been a research hotspot of wireless communication systems. It directly affects the correctness of channel state analysis. Channel feature extraction methods are endless, but most of the existing extraction methods are limited by specific environments. The methods are complex, the system analysis is not comprehensive enough, the applicability is not extensive, and it is affected by external interference. This paper avoids the disadvantages of direct extraction and innovatively introduces an image construction method that can analyze the channel state in real time. In addition, an improved image feature extraction algorithm is proposed for the state images proposed in this paper, which verifies high robust performance of the algorithm. In the end, a model capable of efficiently and accurately predicting real-time conditions of shortwave fading channel states is constructed. First, channel state images are constructed, including time domain image, frequency domain image, and related domain image. The three state images reflect the channel state in all aspects. Then, the multi-texton image feature extraction method is optimized and improved, and the multi-structure color difference (MSC) extraction method is constructed to extract the channel state image information. Then, through the experimental simulation, the method of channel state division is specified, and the performance advantage of the improved algorithm MSC relative to the original algorithm is verified. Finally, discriminating channel state based on SVM algorithm. A series of experiments on rotation invariance test, immunity from interference test and illumination variation test verify that the proposed method has high robustness and the recognition accuracy is between 80% and 100%, which can effectively identify Instantaneous channel advantages and disadvantages. INDEX TERMS Channel state information, channel state discrimination, channel state images, multi-structure color difference (MSC), robustness, shortwave fading channel. YIFAN CHEN received the B.S. degree in electronic information engineering from Harbin Engineering University, Harbin, China, in 2018, where she is currently pursuing the M.S. degree in information and communication engineering. Her research interests are in the areas of channel model establishment, intelligent channel feature extraction, and channel state feature optimization.

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Section: B Image Feature Extraction Methodsmentioning

confidence: 99%

Channel State Discrimination Method Based on Multi-Structure Color Difference Histogram

2020

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“…• Fuzzy techniques: Fuzzy C-means is widely used unsupervised image segmentation. It considers only the intensity of image but still some noise is present in the image which is the disadvantage [4,12]. •  Neural Network based methods: A hybrid approach of using support vector machine (SVM) combined with wavelets is a two step algorithm which is superior than the kohonen neural network or Self Organized Map (SOM)is a single layer feed forward network.SVM approach cannot be implemented successfully if the data is too large, training of such a large data becomes complicated.…”

Section: Segmentation Methodsmentioning

confidence: 99%

A review on noise reduction methods for brain MRI images

Vaishali

Rao

2015

2015 International Conference on Signal Processing and Communication Engineering Systems

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Segmentation plays a vital role in extracting information from medical images .Segmentation is the process of partitioning the image into distinct regions. Magnetic resonance imaging is used to extract images of soft tissues of human body. It is used to analyze the human organs without the need for surgery. Generally MRI images contain a significant amount of noise caused by operator performance, equipment and the environment, which leads to serious inaccuracies MRI seems to be efficient in providing information regarding the location of tumors and even the volume.The noise present in the MRI image can be removed by using various de-noising techniques whichever is best suited depending upon the image acquired and then can be processed by any of the segmentation methods. The noise in MRI images may be due to field strength, RF pulses, RF coil, voxel volume, or receiver bandwidth. A review of various de-noising methods are presented.

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“…The most convenient and widely used technique is histogram thresholding that is based on the shape properties of the histogram. The image histogram has distinct peaks, with each peak corresponding to one distinct region, and the valleys as the threshold values for separating these regions [ 4 , 5 ]. Thresholding-based segmentation algorithms are then generally efficient in terms of computational complexity when compared to other segmentation methods, and Otsu's clustering-based thresholding [ 6 ] is being one of the most representative methods for image segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Technique for Medical Image Segmentation

Nyma

Kang²,

Kwon³

et al. 2012

Journal of Biomedicine and Biotechnology

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Medical image segmentation is an essential and challenging aspect in computer-aided diagnosis and also in pattern recognition research. This paper proposes a hybrid method for magnetic resonance (MR) image segmentation. We first remove impulsive noise inherent in MR images by utilizing a vector median filter. Subsequently, Otsu thresholding is used as an initial coarse segmentation method that finds the homogeneous regions of the input image. Finally, an enhanced suppressed fuzzy c-means is used to partition brain MR images into multiple segments, which employs an optimal suppression factor for the perfect clustering in the given data set. To evaluate the robustness of the proposed approach in noisy environment, we add different types of noise and different amount of noise to T1-weighted brain MR images. Experimental results show that the proposed algorithm outperforms other FCM based algorithms in terms of segmentation accuracy for both noise-free and noise-inserted MR images.

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Using Fuzzy C-means Cluster for Histogram-Based Color Image Segmentation

Cited by 15 publications

References 11 publications

Channel State Discrimination Method Based on Multi-Structure Color Difference Histogram

Channel State Discrimination Method Based on Multi-Structure Color Difference Histogram

A review on noise reduction methods for brain MRI images

A Hybrid Technique for Medical Image Segmentation

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