Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of Clusters

This study focuses on the threshold segmentation algorithm to obtain the real microstructure of asphalt concrete based on digital image technique, the perlite powder which was a kind of low-density material was put in the asphalt concrete to enhance the density contrast, three different specimens in which added different contents of perlite powder were compacted, and then the asphalt concrete specimens were scanned using x-ray CT to capture the gray images that reflect the density differences of the three constituents such as aggregates, mastic and voids, the CT images were converted to be the histograms. Furthermore, the FCM (Fuzzy C-Means) was demonstrated that it could be utilized to choose proper threshold values and segment images exactly, according to the double peak conditions of the three different histograms, the double peak condition for AC-13 is the best among the three types, a similar double peak features between AK13 and SMA-13 were observed. The results shows that the different contents of perlite powder added in the asphalt concrete can form different double peaks. This is another new method to segment the three constituents of the asphalt concrete exactly.

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Liver CT image segmentation by local entropy method

2010 International Conference on Computer Application and System Modeling (ICCASM 2010) ◽

10.1109/iccasm.2010.5623136 ◽

2010 ◽

Cited By ~ 1

Author(s):

Yi Xu ◽

Zhon Liu ◽

Ji Lin

Keyword(s):

Image Segmentation ◽

Entropy Method ◽

Ct Image ◽

Local Entropy ◽

Liver Ct

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Liver CT image segmentation algorithm research based on CV model

2017 3rd IEEE International Conference on Computer and Communications (ICCC) ◽

10.1109/compcomm.2017.8322866 ◽

2017 ◽

Author(s):

Xiangxin Shao ◽

Xiaomei Lin ◽

Tingting Shang

Keyword(s):

Image Segmentation ◽

Segmentation Algorithm ◽

Ct Image ◽

Liver Ct ◽

Cv Model ◽

Image Segmentation Algorithm

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PENERAPAN FUZZY C-MEANS KLUSTER UNTUK SEGMENTASI PELANGGAN E-COMMERCE DENGAN METODE RECENCY FREQUENCY MONETARY (RFM)

Jurnal Gaussian ◽

10.14710/j.gauss.v9i4.29445 ◽

2020 ◽

Vol 9 (4) ◽

pp. 421-433

Author(s):

Stevanus Sandy Prasetyo ◽

Mustafid Mustafid ◽

Arief Rachman Hakim

Keyword(s):

Marketing Strategy ◽

Customer Segmentation ◽

Optimum Number ◽

Number Of Clusters ◽

Business World ◽

The Public ◽

Fuzzy C Means ◽

Internet Users ◽

Ease Of Access ◽

The Right

E-commerce has become a medium for online shopping which is growing and popular among the public. Due to the ease of access for all internet users and the completeness of the products offered, e-commerce has become a new alternative in meeting people's needs. Currently, the competition in the business world is very fierce, any e-commerce company needs to be able to carry out the right marketing strategy to compete in acquiring, retaining, and partnering with customers. In this research, the segmentation of e-commerce customers was carried out using the Fuzzy C-Means cluster and the RFM method. The clustering process is carried out six times with the number of clusters starts from two to seven clusters. The results showed that the optimum number of clusters formed according to the Xie-Beni validity index was four clusters. The cluster becomes customer segments that have the characteristics of each customer based on their recency, frequency, and monetary value. The best segment is segment 4 which has very loyal customers in shopping on tumbas.in e-commerce. From the segments that have been formed, they can be used as a consideration in implementing the right marketing strategy for each customer. Keywords : E-commerce, customer segmentation, Fuzzy C-Means Cluster, RFM, Xie-Beni Index

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