Feature Extraction and Image Matching of 3D Lung Cancer Cell Image Hizmawati Madzin Multimedia Department University Putra Malaysia Serdang, Malaysia hizma@fsktm.upm.edu.my Roziati Zainuddin Artificial Intelligence Department University Malaya Kuala Lumpur, Malaysia roziati@um.edu.my 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. Local invariant features; feature extraction; image matching; 3D cancer cell image. I. INTRODUCTION In biological and medical research, the application of fast volumetric imaging technique and aim of extracting quantitative data from these images is obvious. Usual 3D structure representations lack of concise information about the object. As the representation of 3D objects is not canonical and object occur at different spatial position and viewpoint [1]. The demand for efficient image analysis technique of 3D object is continuously growing. As for 3D microscopy images, image analysis system is an essential yet very challenging problem where the quantitative analysis of morphology and cell phenotypes is needed. The difficult part of microscopy image mostly come from the variability and complexity of the images that caused by the intrinsic properties of the samples as well as the staining and imaging procedures [2]. Moreover microscopy image can be complicated due to the imperfect staining or intrinsic intracellular characteristics. The major objectives of image analysis in biomedical engineering are to gather information, screening or investigating, to diagnose, therapy and control, monitoring and evaluation [3]. Feature extraction and image matching are important steps that relate to each other in order to apply 3D medical image analysis successfully. Local invariant features have been found as well suited to matching, recognition and other applications as they are robust to occlusion and content changes [4]. According to Patrick [5], feature based matching means finding the same features in different images that represent in the same object. For this research paper, Harris-Laplace detector method is used to extract features from multiple viewpoints of confocal images and SIFT method is used to identify and calculate the same features found in each images. 3D volume data of lung cancer cell has been used in the experiment of feature extraction and image matching. The lung cancer cell image was taken from confocal laser scan microscopy. In order to apply feature extraction, the 3D cancer cell image is slices into ten slides of 2D images with different viewpoints. It is important to emphasize that the purpose this research paper is to experiment whether local invariant features methods is suitable to apply on multiple viewpoints of confocal images. Therefore this research can be served as preliminary step for further research study in analyzing 3D structure on confocal microscopy image. This paper is organized in four sections. The first section is introduction, followed by the description of local invariant features in section two. The next section will be the methodology description of HarrisLaplace and SIFT methods. The following section is the result of both methods. The final section will be conclusion. II. LOCAL INVARIANT FEATURES Methods based on local invariant features have shown promise good result for image analysis task. A local feature is an image pattern which has different value or characteristic from its instantaneous neighborhood and associated with a change of image properties (intensity, color and texture) simultaneously [6]. Invariant feature is a value stay unchanged when a transformation (object’s position and/or orientation changes) is applied [7]. Therefore, local invariant feature is a new image representation that allows to describe the objects/parts in any different transformation (translation, rotation and scale). The features can be in 2009 International Conference of Soft Computing and Pattern Recognition 978-0-7695-3879-2/09 $26.00 © 2009 IEEE DOI 10.1109/SoCPaR.2009.103 517 2009 International Conference of Soft Computing and Pattern Recognition 978-0-7695-3879-2/09 $26.00 © 2009 IEEE DOI 10.1109/SoCPaR.2009.103 511