中国组织工程研究

中国组织工程研究 ›› 2011, Vol. 15 ›› Issue (39): 7340-7342.doi: 10.3969/j.issn.1673-8225.2011.39.030

• 数字化骨科 digital orthopedics • 上一篇    下一篇

基于孤立点分析的医学图像处理

冯  敏1，阴珊珊2   

  1. 1泰山医学院信息工程学院，山东省泰安市271016
    2泰安市疾病预防控制中心，山东省泰安市  271000
  • 收稿日期:2011-05-06 修回日期:2011-06-19 出版日期:2011-09-24 发布日期:2011-09-24
  • 作者简介:冯敏★，男，1980年生，山东省泰安市人，2009年山东师范大学毕业，硕士，讲师，主要从事计算机辅助医学技术的研究。 fmxxsc@126.com

Medical image processing based on outlier analysis

Feng Min1, Yin Shan-shan2   

  1. 1College of Information Engineering, Taishan Medical University, Taian  271016, Shandong Province, China
    2Taian Center for Disease Control and Prevention, Taian  271000, Shandong Province, China
  • Received:2011-05-06 Revised:2011-06-19 Online:2011-09-24 Published:2011-09-24
  • About author:Feng Min★, Master, Lecturer, College of Information Engineering, Taishan Medical University, Taian 271016, Shandong Province, China fmxxsc@126.com

PDF

263

被引次数

9

摘要：

背景：在进行临床诊断的时候，医学影像中许多微小的纹理变化细节和形态特征不容易被发现，会影响对病情的早期判断。
目的：为数字医学图像中病变的计算机诊断提供一种新的思路和方法，帮助医生及早发现和诊断恶性病变、提高诊断效率和准确性。
方法：运用孤立点数据挖掘技术，分析提取医学图像数据集中隐藏、不为人所注意、易被抛弃的但非常有用的信息，找出其中的医学诊断规则和模式，从而辅助医生进行疾病诊断。
结果与结论：实验证明基于医学图像象素聚类的孤立点分析算法对于发现脑部病变是切实可行的。

关键词: 孤立点分析, 聚类, 图像挖掘, 医学图像, 数据挖掘

Abstract:

BACKGROUND: Due to the low resolution of naked eyes, many small details and texture changes in morphology are not easy to be found, it will affect the early judgement of diseases.
OBJECTIVE: To provide a new way of thinking and methods for the computer diagnosis of diseases in digital medical images, which helps doctors to detect and diagnose the early malignant lesions and improve diagnostic efficiency and accuracy.
METHODS: Outlier data mining technique was used to analyze large data sets, extract the hidden, unnoticed, and easily discarded, but very useful information, and find out the rules and patterns of medical diagnosis to assist doctors to diagnose disease.
RESULTS AND CONCLUSION: Experiments show that outlier analysis algorithm based on clustering of the medical image pixels is feasible for the brain lesions.

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