利用大量子宫颈图像中的生物标志物进行医学教育、研究和疾病筛查的技术

L. Long, Sameer Kiran Antani, J. Jeronimo, M. Schiffman, M. Bopf, Leif Neve, Carl Cornwell, S. Budihas, G. Thoma
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引用次数: 8

摘要

美国国家医学图书馆通信工程分馆正在与美国国家癌症研究所(NCI)合作,开发用于医学教育、研究和宫颈癌前期疾病筛查的应用程序。这些应用包括(1)组织区域的专家标记/标记,(2)组织图像的网络查看/解释,(3)图像数据库/检索,以及(4)临床图像解释的培训/测试。最初的NCI研究已经在专家宫颈造影标记和组织学评估中进行。我们正致力于通过基于内容的图像检索(CBIR)使子宫颈图像可搜索。图像预处理去除镜面反射伪影已经取得了90%的成功(120张图像)。使用具有Lab颜色和一个几何特征的高斯混合建模(GMM),在子宫颈区域的自动定位中获得了类似的结果。我们描述了区分临床重要组织的初步分类实验,使用RGB、HSV、Lab和YCbCr颜色模型、纹理测量、GMM、模糊c均值和确定性退火算法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technology for Medical Education, Research, and Disease Screening by Exploitation of Biomarkers in a Large Collection of Uterine Cervix Images
The Communications Engineering Branch of the National Library of Medicine is collaborating with the National Cancer Institute (NCI) in developing applications for medical education, research, and disease screening for precancer detection in the uterine cervix. These applications include (1) expert marking/labeling of tissue regions, (2) Web viewing/interpretation of histology images, (3) image database/retrieval, and (4) training/testing in clinical image interpretation. Initial NCI studies have been conducted in expert cervicography marking and histology evaluation. We are working toward making cervix images searchable by content-based image retrieval (CBIR). Image pre-processing to remove specular reflection artifacts has achieved 90% success (120 images). Similar results have been obtained for automated location of cervix regions, using Gaussian mixture modeling (GMM) with Lab color and one geometric feature. We describe initial classification experiments to discriminate clinically significant tissue, using RGB, HSV, Lab, and YCbCr color models, texture measures, and GMM, fuzzy C-means, and deterministic annealing algorithms
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