Improving image segmentation based on patch-weighted distance and fuzzy clustering

Zhang, Xiaofeng; Jian, Muwei; Sun, Yu; Wang, Hua; Zhang, Caiming

doi:10.1007/s11042-019-08041-x

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Adaptive histogram equalization (AHE) causes noise amplification, whereas CLAHE is different from AHE. In CLAHE, The noise amplification can be reduced by clipping limit, i.e., the histogram is clipped at predefined value before calculating cumulative distributed function [21]. Block Size (BS) and Clip Limit (CL) are two main parameters in CLAHE.…”

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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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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“…During fault location, firstly traverse the cluster center node of each cluster. When the information is obtained from the cluster center node, there is abnormal node state in the cluster; then, traverse all nodes in the cluster, and then identify the location of the fault node and isolate the fault area, and synchronously transfer the fault information to other cluster center nodes [29][30][31][32]. When the clustering center satisfies the convergence condition of the semi-supervised learning, the detection statistic of the characteristic information of the large-data value of the communication satisfies the clustering convergence condition, and the implementation process of the large-data fuzzy weighted clustering algorithm designed in this paper is obtained, as shown in Figure 6.…”

Section: Big Data Fuzzy Weighted Clustering Optimizationmentioning

confidence: 99%

Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing

2020

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It is necessary to optimize clustering processing of communication big data numerical attribute feature information in order to improve the ability of numerical attribute mining of communication big data, and thus a big data clustering algorithm based on cloud computing was proposed. The cloud extended distributed feature fitting method was used to process the numerical attribute linear programming of communication big data, and the mutual information feature quantity of communication big data numerical attribute was extracted. Combined with fuzzy C-means clustering and linear regression analysis, the statistical analysis of big data numerical attribute feature information was carried out, and the associated attribute sample set of communication big data numerical attribute cloud grid distribution was constructed. Cloud computing and adaptive quantitative recurrent classifiers were used for data classification, and block template matching and multi-sensor information fusion were combined to search the clustering center automatically to improve the convergence of clustering. The simulation results show that, after the application of this method, the information fusion performance of the clustering process was better, the automatic searching ability of the data clustering center was stronger, the frequency domain equalization control effect was good, the bit error rate was low, the energy consumption was small, and the ability of fuzzy weighted clustering retrieval of numerical attributes of communication big data was effectively improved.

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