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

摘要

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 0.1 μm. 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.