Analysis of Featute Extraction Techniques for Medical Images

Author:

Shankara C 1,Latha D U 2,Dharini K R 2,Harsha Vardhini K 2,Jayashree K M 2,Varsha R 2

Affiliation:

1. Government Polytechnic, Nagamangala, Mandya, Karnataka, India

2. Vidya Vikas Institute of Engineering and Technology, Mysuru, Karnataka, India

Abstract

In the image downloading process, image processing method, data mining method, and computer scanning technique, feature removal is an important step. The process of extracting logical data from original data is known as feature extraction. However, many FE methods still struggle with the difficulty of extracting relevant features that can accurately capture the basic content of a piece of data or database. We provide a survey of existing methods of extracting features used in recent years in this work. Brightness, homogeneity, entropy, meaning, and strength were shown to be the most of the distinctive features that could be obtained when using global learning and development community features extraction method in the images in the study. In addition, it was found that the extraction methods are not specific to the application and can be used in a variety of situations.

Publisher

Naksh Solutions

Subject

General Medicine

Reference20 articles.

1. D. P. Tian, “A review on image feature extraction and representation techniques,” International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013.

2. N. Goel and P. Sehga, “A refined hybrid image retrieval system using text and color,” International Journal of Computer Science Issues, vol. 9, no. 1, pp. 48-56, 2012.

3. J. Tang, S. Alelyani and H. Liu, “Feature selection for classification: A review,” Data classification: Algorithms and applications, pp. 129, 2014.

4. T. K. Shih, J. Y. Huang and C. S. Wang, “An intelligent contentbased image retrieval system based on color, shape and spatial relations,” in:Proceedings of the National Science Council, R. O.C., Part A: Physical Science and Engineering, vol. 25, no. 4, pp. 232243, 2001.

5. P. L. Stanchev, D. Green, and B. Dimitrov, “High level colour similarity retrieval,” International Journal of Information Theories and Applications, vol. 10, no. 3, pp. 363-369, 2003.

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3