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。
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.
出处
《中国组织工程研究与临床康复》
CAS
CSCD
北大核心
2011年第39期7340-7342,共3页
Journal of Clinical Rehabilitative Tissue Engineering Research
