An eigen-support differential operator to fuse CT and MR images for nasopharyngeal carcinoma diagnosis

One advantage of using instrinsic image properties in medical registration is not necessarily to put fixed markers to identify features which could significantly enlarge the scanning time and cause the patients discomfort. But detecting the same significant landmarks from different image modalities is a high-level image processing task that might prove quite difficult. Using differential geometric features resolves the difficulty of detecting anatomic landmarks. However, operators for differential geometric feature extraction usually generate overwhelming redundant features resulting lack of registration accuracy. In this paper, a second-order differential operator embedded with an eigenimage filter called eigen-Lvv operator is proposed to detect the position of Nasopharyngeal Carcinoma (NPC) in MR images, and then fuse the NPC lesion on the CT image for physicians to comprehend the precise location and tumor range for treatment. The embedded eigenimage filter in this eigen-Lvv operator enhances nasal region, where NPC is located, and suppresses surrounding tissues. Thus, the ridge-like features extracted by the eigen-Lvv operator would focus on the surrounding are of NPC (nasal cavity), aptly avoiding the problem of feature redundancy in most second-order differential operators. A two-stage registration, which consists of a course registration based on geometric contours and a fine registration based on ridge-like anatomical features extracted from eigenimage, is used to reduce the registration time while maintain the registration accuracy. The proposed system has been proven to be effective for NPC detection.