The volume and complexity of neurological images have significantly increased, which leads to challenges in efficient data management and retrieval. In this paper, we developed a new content-based image retrieval framework with the localized multiscale Discrete Curvelet Transform (DCvT) features extracted from parametric neurological images. We also compared the performance of three different irregular-to-regular shape padding methods. 142 patient data with neurodegenerative disorders were used in the evaluation. The preliminary results show that our proposed framework supports fast neuroimaging retrieval, and the orthographic projection method can reduce the computational complexity and has a great potential to improve the retrieval for indefinite cases.
['Sidong Liu', 'Lei Jing', 'Weidong Cai', 'Lingfeng Wen', 'Stefan Eberl', 'Michael J. Fulham', 'David Dagan Feng']
Localized multiscale texture based retrieval of neurological image