Multiresolution Image Analysis for Automatic Quantification of Collagen Gel Contraction

Hsin-Chen Chen, Tai-Hua Yang, A. Thoreson, Chunfeng Zhao, P. Amadio, F. Su, Wenyan Jia, Yung-Nien Sun, K. An, Mingui Sun
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引用次数: 0

Abstract

Quantifying collagen gel contraction is important in tissue engineering and biological research because it provides spatial-temporal assessments of cell behaviors and tissue material properties. However, these assessments currently rely on manual processing, which is time-consuming and subjective to personal opinions. We present a multiresolution image analysis system for automatic quantification of gel contraction. This system includes a color conversion process to normalize and enhance the contrast between gel and background. Then, a deformable circular model is activated automatically to capture details of gel boundaries. These steps are coordinated by a multiresolution strategy. The target measurements are obtained after gel segmentation. Our experiments using 30 images demonstrated a high consistency between the proposed and manual segmentation methods. The system can process large-size images (4000×3000) at a rate of approximately one second per image. It thus serves as a useful tool for analyzing large biological and biomaterial imaging datasets efficiently and objectively.
用于胶原凝胶收缩自动定量的多分辨率图像分析
定量胶原凝胶收缩在组织工程和生物学研究中很重要,因为它提供了细胞行为和组织材料特性的时空评估。然而,这些评估目前依赖于人工处理,这是耗时和主观的个人意见。我们提出了一种用于凝胶收缩自动定量的多分辨率图像分析系统。该系统包括一个颜色转换过程,以规范和增强凝胶和背景之间的对比度。然后,自动激活可变形的圆形模型来捕获凝胶边界的细节。这些步骤由多分辨率策略协调。凝胶分割后得到目标测量值。我们使用30幅图像进行的实验表明,本文提出的分割方法与人工分割方法之间具有很高的一致性。该系统可以处理大尺寸图像(4000×3000),每幅图像的处理速度大约为1秒。因此,它可以作为有效客观地分析大型生物和生物材料成像数据集的有用工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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