Dental plaque quantification using fcm clustering combined with histogram analysis

doi:10.3778/j.issn.1002-8331.2010.20.058

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (20): 213-215.DOI: 10.3778/j.issn.1002-8331.2010.20.058

• 人工智能 • Previous Articles Next Articles

Dental plaque quantification using fcm clustering combined with histogram analysis

KANG Jia-yin^1，2，JI Zhi-cheng²

1.School of Electronics Engineering，Huaihai Institute of Technology，Lianyungang，Jiangsu 222005，China
2.School of Communication and Control Engineering，Jiangnan University，Wuxi，Jiangsu 214122，China

Received:2010-04-15 Revised:2010-05-19 Online:2010-07-11 Published:2010-07-11
Contact: KANG Jia-yin

利用FCM聚类结合直方图分析进行牙菌斑量化

康家银^1，2，纪志成²

1.淮海工学院电子工程学院，江苏连云港 222005
2.江南大学通信与控制工程学院，江苏无锡 214122

通讯作者: 康家银

Abstract

Abstract: Dental plaque quantification is very important to patients，dentists as well as researchers.This paper presents a method to automatically quantify the dental plaque using Fuzzy C-Means（FCM） clustering algorithm associated with images’ histogram analysis，and applies the proposed approach to clinical dental plaque quantification.The experimental results indicate that the quantification results based on proposed method are fully quantitative，repeatable，relatively accurate and automatic compared with those of quantification levels using plaque indices adopted in clinic.

Key words: dental plaque, quantification, Fuzzy C-Means（FCM）, segmentation, histogram

摘要： 牙菌斑的量化对于患者、牙科医生、以及研究者都很重要。提出了一种利用模糊C均值（FCM）聚类算法结合直方图分析的自动量化牙菌斑的方法，并将该方法应用于牙菌斑的量化中。实验结果表明，相对于临床中采用的菌斑指数的量化等级，基于该文提出的方法的量化结果具有完全定量、可重复、相对准确和自动等特点。

关键词: 牙菌斑, 量化, 模糊C均值, 分割, 直方图

CLC Number:

TP391.4

KANG Jia-yin^1，2，JI Zhi-cheng². Dental plaque quantification using fcm clustering combined with histogram analysis[J]. Computer Engineering and Applications, 2010, 46(20): 213-215.

康家银^1，2，纪志成². 利用FCM聚类结合直方图分析进行牙菌斑量化[J]. 计算机工程与应用, 2010, 46(20): 213-215.

[1]	LI Xiaoxiao, HU Xiaoguang, WANG Ziqiang, DU Zhuoqun. Survey of Instance Segmentation Based on Deep Learning [J]. Computer Engineering and Applications, 2021, 57(9): 60-67.
[2]	LAN Hong, HUANG Min. Fusion of KNN Optimized Density Peaks and FCM Clustering Algorithm [J]. Computer Engineering and Applications, 2021, 57(9): 81-88.
[3]	XU Shaojie, CAO Chuqing, WANG Yongjuan. Application Research of Visual SLAM in Indoor Dynamic Scenes [J]. Computer Engineering and Applications, 2021, 57(8): 175-179.
[4]	YAN Xiaoshen, GAO Qiang, ZHU Simeng, XI Xuecheng, ZHAO Wansheng. Study on Character Segmentation Algorithm of Pressed Character on Uneven Brightness Low Quality Images [J]. Computer Engineering and Applications, 2021, 57(8): 185-191.
[5]	ZHAO Yang, ZHANG Junhua. Multi-scale Feature Fusion Method for Spinal X-Ray Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 214-219.
[6]	PAN Peixin, PAN Zhongliang. Active Contour Image Segmentation Combined with Saliency [J]. Computer Engineering and Applications, 2021, 57(8): 225-230.
[7]	DONG Peng, ZHOU Feng, ZHAO Congcong, WANG Yafei, MI Zetian, FU Xianping. Automatic Measurement of Underwater Sea Cucumber Size Based on Binocular Vision [J]. Computer Engineering and Applications, 2021, 57(8): 271-278.
[8]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[9]	YANG Bo, TAO Qingchuan, DONG Peijun. Surgical Instrument Segmentation Method Based on Improved Deeplab v3+ Network [J]. Computer Engineering and Applications, 2021, 57(7): 222-227.
[10]	HU Yangtao, PEI Yang, LIN Chuan, LI Shicheng, YI Yugen. Atrous Residual U-Net for Retinal Vessel Segmentation [J]. Computer Engineering and Applications, 2021, 57(7): 185-191.
[11]	ZHANG Zhongmin, LIU Jinxin, XI Zhihong. Disparity Optimization Algorithm Using Entropy Rate Super-Pixel Segmentation Consistency Check [J]. Computer Engineering and Applications, 2021, 57(5): 204-209.
[12]	YUAN Mingyang, HUANG Hongbo, ZHOU Changsheng. Research Progress of Image Semantic Segmentation Based on Fully Supervised Learning [J]. Computer Engineering and Applications, 2021, 57(4): 43-54.
[13]	WANG Peng, YE Xueyi, WANG Tao, QIAN Dingwei. Face Recognition Based on Double Variation and Double Space Local Directional Pattern [J]. Computer Engineering and Applications, 2021, 57(4): 91-99.
[14]	YANG Yanan, ZHANG Hongming, LI Hanghao, YANG Jiangtao, QUAN Kai. Research on UAV Terrace Recognition Method Based on FCN and DenseCRF Model [J]. Computer Engineering and Applications, 2021, 57(3): 222-230.
[15]	PENG Jing, LUO Haoyu, ZHAO Gansen, LIN Chengchuang, YI Xusheng, CHEN Shaojie. Survey of Medical Image Segmentation Algorithm in Deep Learning [J]. Computer Engineering and Applications, 2021, 57(3): 44-57.

Dental plaque quantification using fcm clustering combined with histogram analysis

利用FCM聚类结合直方图分析进行牙菌斑量化

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics