Feature Extraction and Image Matching of 3D Lung Cancer Cell Image

H. Madzin, R. Zainuddin
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引用次数: 6

Abstract

The demand for automation in medical analysis is continuously growing with large number of application in biotechnology and medical research. Feature extraction and image matching are important steps in analyzing medical cells. In this research paper, we are concentrating on extracting and matching features from a full 3D volume data of lung cancer cell that was recorded with a confocal laser scanning microscopy (LSM) at a voxel size of about (0.3μm)3. In order to apply feature extraction on 3D cell image, the image is slices into ten different viewpoints of 2D images with thickness of each slice are about 0.1μm. An experiment has been done based on local invariant features methods which are HarrisLaplace method to extract features of each slices and SIFT matching method to find and match same features in each slices. The experiment shows that these methods can extract the same features although in different viewpoints. This research paper application can be served as preliminary step for further research study in analyzing 3D structure of cancer cell image.
肺癌细胞三维图像特征提取与图像匹配
随着自动化在生物技术和医学研究中的大量应用,对医学分析自动化的需求不断增长。特征提取和图像匹配是医学细胞分析的重要步骤。在本研究中,我们专注于从共聚焦激光扫描显微镜(LSM)记录的肺癌细胞的完整三维体积数据中提取和匹配特征,体素尺寸约为(0.3μm)3。为了对三维细胞图像进行特征提取,将图像切片为10个不同视点的二维图像,每个切片的厚度约为0.1μm。基于局部不变特征方法进行了实验,分别采用HarrisLaplace法提取每个切片的特征,采用SIFT匹配法寻找并匹配每个切片中的相同特征。实验结果表明,这些方法可以从不同的视点提取出相同的特征。本文的应用为进一步研究分析癌细胞图像的三维结构提供了基础。
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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