A Review on Rough Set Theory in Medical Images

被引：0

作者：

Keerthana, R. Madhu ^{[1
]}

Jinila, Y. Bevish ^{[1
]}

机构：

[1] Sathyabama Univ, Dept Informat Technol, Madras, Tamil Nadu, India

来源：

RESEARCH JOURNAL OF PHARMACEUTICAL BIOLOGICAL AND CHEMICAL SCIENCES | 2016年 / 7卷 / 01期

关键词：

Rough set theory; Medical images; feature identification; dimensionality reduction and image segmentation;

D O I：

暂无

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The accurate representation of sets in crisp set is known as rough set. The knowledge is obtained by considering every data as objects with its discourse to extract associated information. There are many applications that used the advent of traditional rough set approach. A rough set handles the uncertainties in the medical images. It monitors tasks such as feature identification, dimensionality reduction, pattern classification and image segmentation. It provides various algorithms to discover the knowledge from finding patterns in data, data reduction, validating the data significance and framing the rules from the known information. This paper intends to help the study on Rough Set Theory (RST) especially focused in medical images.

引用

页码：815 / 822

页数：8

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