基于区域增长的医学图像分割方法

Itzel Abundez Barrera, Citlalih Gutierrez Estrada, S. D. Zagal, M. N. Perez
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引用次数: 3

摘要

结合不同的技术和开发提供高质量结果的性能更好的系统的可能性导致创建具有增强的适应性和分析的系统。这种分析允许与先前评估的技术相互作用,提供可靠、成功的结果。这就是本文中详细介绍的研究工作的情况,它关注的是一种技术,该技术允许在统一建模语言(UML)的支持下重用以前分析过的和形式化的信息。信息在模块中处理,目的是在没有专家干预的情况下生成医学图像的分割。目的是为宫颈癌的早期发现提供感兴趣的区域。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Segmentation of medical images by region growing
The possibility of combining different technologies and developing better performing systems that offer quality results leads to the creation of systems with enhanced adaptation and analysis. Such analysis allows the interaction with previously evaluated techniques, providing reliable, successful results. Such is the case for the research work detailed in this article, which is focused on a technique that allows reusing previously analyzed and formalized information with the support of the Unified Modeled Language (UML). Information is handled in modules aimed to generate the segmentation of medical images without intervention of a specialist. The purpose is to deliver regions of interest for the early detection of cervical cancer.
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