%0 Journal Article
%T Nonrigid Registration of Lung CT Images Based on Tissue Features
%A Rui Zhang
%A Wu Zhou
%A Yanjie Li
%A Shaode Yu
%A Yaoqin Xie
%J Computational and Mathematical Methods in Medicine
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/834192
%X Nonrigid image registration is a prerequisite for various medical image process and analysis applications. Much effort has been devoted to thoracic image registration due to breathing motion. Recently, scale-invariant feature transform (SIFT) has been used in medical image registration and obtained promising results. However, SIFT is apt to detect blob features. Blobs key points are generally detected in smooth areas which may contain few diagnostic points. In general, diagnostic points used in medical image are often vessel crossing points, vascular endpoints, and tissue boundary points, which provide abundant information about vessels and can reflect the motion of lungs accurately. These points generally have high gradients as opposed to blob key points and can be detected by Harris. In this work, we proposed a hybrid feature detection method which can detect tissue features of lungs effectively based on Harris and SIFT. In addition, a novel method which can remove mismatched landmarks is also proposed. A series of thoracic CT images are tested by using the proposed algorithm, and the quantitative and qualitative evaluations show that our method is statistically significantly better than conventional SIFT method especially in the case of large deformation of lungs during respiration. 1. Introduction Lung cancer is the most common cause of cancer-related death all over the world, with exceeding 1 million deaths annually [1]. Image-guided radiation therapy (IGRT) plays an important role in both the curative and palliative treatment of lung cancer, and precise targeting of lung tumors is an essential step in IGRT [2]. However, it is difficult to target tumors in lungs by taking into account respiration and tumor motion. Deformable or nonrigid image registration has been recognized as a key technology to locate position of tumor precisely [3]. By definition, image registration is a process of establishing spatial correspondences between two images. It can be classified into rigid registration and nonrigid registration. As the motion and shape change of lung is nonlinear, it is appropriate to register the lung CT images by using nonrigid registration. Various nonrigid image registration methods have been applied for the lung CT images. In general, the registration methods can be divided into intensity-based and feature-based methods. There are a lot of intensity-based methods used in the thoracic CT image registration, such as fast intensity-based freeform registration [4], multiresolution optical flow technique [5], regional narrow shell model [6], and
%U http://www.hindawi.com/journals/cmmm/2013/834192/