title: Hierarchical and binary spatial descriptors for lung nodule image retrieval.
abstract: With the increasing amount of image data available for cancer staging and diagnosis, it is clear that content-based image retrieval techniques are becoming more important to assist physicians in making diagnoses and tracking disease. Domain-specific feature descriptors have been previously shown to be effective in the retrieval of lung tumors. This work proposes a method to improve the rotation invariance of the hierarchical spatial descriptor, as well as presents a new binary descriptor for the retrieval of lung nodule images. The descriptors were evaluated on the ELCAP public access database, exhibiting good performance overall.
authors.forename: Gillian
authors.initials: G
authors.lastname: Ng
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.forename: Sidong
authors.initials: S
authors.lastname: Liu
authors.forename: David Dagan
authors.initials: DD
authors.lastname: Feng
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D003074:Solitary Pulmonary Nodule / Q000175:diagnosis*; D014057:Tomography, X-Ray Computed / Q000379:methods*