摘要
分类是医学图像在计算机辅助诊断和模式识别领域的一个研究热点。精确地对人体解剖结构和病变区域进行分类能够最大程度辅助医生更精确、更快速地诊断病情。针对医学图像的特殊性,首先从图像预处理、图像分割、特征提取及分类方法4个方面对典型医学图像分类进行总结分析;然后介绍分析了深度学习理论在医学图像分类中的应用;最后提出现有的医学图像分类研究方法的不足,展望了深度学习领域的最新理论在医学图像分类领域的发展趋势。
The classification of medical images is a research hotspot in the field of computer-aided diagnosis and pattern recognition.Accurately categorizing human anatomical structure and lesion areas can maximally assist doctors in diagnosing diseases more accurately and quickly.Herein,for the particularity of medical images,typical medical image classification is first summarized and then described according to four aspects:image preprocessing, image segmentation,feature extraction,and classification.Next,the application of deep learning theory to medical image classification is introduced and discussed.Finally,the shortage of the existing medical image classification methods is addressed,and the development trend of the latest theories of deep learning in the field of medical image classification is discussed.
作者
张薇
吕晓琪
吴凉
张明
李菁
Zhang Wei;Lu Xiaoqi;Wu Liang;Zhang Ming;Li Jing(School of Information Engineering,Inner Mongolia University of Science and Technology Baotou,Inner Mongolia 014010,China;Inner Mongolia University of Technology,Hohhot,lnner Mongolia 010051,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第12期90-99,共10页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61771266
61179019)
包头市科技计划项目(2015C2006-14)
关键词
医学图像
分类
解剖结构
病变区域
深度学习
medical images
classification
anatomical structure
lesion area
deep learning
