Computer Aided Diagnosis System of Brain Tumors in PET/CT Images

Abstract

In this paper, we proposed a computer aided diagnosis system (CADS) that supports to detect the existence of tumor in the area of the brain from a given FDG-PET/CT image. Firstly, tilt correction was applied to brain image based on a computation of the mid-sagittal plane in the head. Secondly, tumor detection was performed by detecting asymmetrical regions between images of the left and right cerebral hemispheres. In this step, an improved SURF algorithm was utilized to detect the asymmetrical regions between the left and right sides in a 2D axial slice of the brain image. In cases where the 2D axial slice data was identified as symmetrical, one supplementary method based on typical illegal shape identification was applied so that the symmetrical tumor could be detected. 80 cases (10 cases with brain tumors and 70 normal cases) were tested in this experiment. The results showed this system could detect brain tumors effectively and was capable of identifying a health brain pattern and mostly avoided the incorrect identification of tumors.

Extraction of Lesion-Partitioned Features and Retrieval of Contrast-Enhanced Liver Images

“…Thus, diagnostic results are often less than ideal. Nationally, along with the developments in image processing and artificial intelligence, designing and developing systems for computer-aided diagnosis to characterize liver lesions have received considerable attention over the past years, because these systems can provide diagnostic assistance to clinicians for the improvement of diagnosis [ 5 , 6 ]. Organically combining the key technologies of image processing and medical imaging has become a main research goal to provide scientific, convenient, and accurate medical means and to support diagnostic recommendations for radiologists.…”