title: Similarity guided feature labeling for lesion detection.
abstract: The performance of automatic lesion detection is often affected by the intra- and inter-subject feature variations of lesions and normal anatomical structures. In this work, we propose a similarity-guided sparse representation method for image patch labeling, with three aspects of similarity information modeling, to reduce the chance that the best reconstruction of a feature vector does not provide the correct classification. Based on this classification model, we then design a new approach for detecting lesions in positron emission tomography computed tomography (PET-CT) images. The approach works well with simple image features, and the proposed sparse representation model is effectively applied for both detection of all lesions and characterization of lung tumors and abnormal lymph nodes. The experiments show promising performance improvement over the state-of-the-art.
authors: BMT Research Group, School of IT, University of Sydney, Australia.
authors: Yang
authors: Y
authors: Song
authors: BMT Research Group, School of IT, University of Sydney, Australia.
authors: Weidong
authors: W
authors: Cai
authors: Computer Science and Engineering, University of Texas at Arlington, USA.
authors: Heng
authors: H
authors: Huang
authors: Department of Electronic Engineering, Chinese University of Hong Kong, China.
authors: Xiaogang
authors: X
authors: Wang
authors: Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Austral.ia
authors: Stefan
authors: S
authors: Eberl
authors: Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Austral.ia
authors: Michael
authors: M
authors: Fulham
authors: BMT Research Group, School of IT, University of Sydney, Australia.
authors: Dagan
authors: D
authors: Feng
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D064847:Multimodal Imaging / Q000379:methods*; D009369:Neoplasms / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*