3D CBIR with sparse coding for image-guided neurosurgery

• Published in: Signal processing Vol. 93; no. 6; pp. 1673 - 1683
• Main Authors: Qian, Yu, Hui, Rui, Gao, Xiaohong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.06.2013
• Subjects: 3D SIFT; Algorithms; CBIR; Coding; Computer aided path planning; Machine learning; Neurosurgery; Path planning; Retrieval; Sparse coding; Sustaining; Three dimensional; Computer aided path planning; 3D SIFT; Neurosurgery; Sparse coding; CBIR
• ISSN: 0165-1684; 1872-7557; 1872-7557
• DOI: 10.1016/j.sigpro.2012.10.020

This research takes an application-specific approach to investigate, extend and implement the state of the art in the fields of both visual information retrieval and machine learning, bridging the gap between theoretical models and real world applications. During an image-guided neurosurgery, path planning remains the foremost and hence the most important step to perform an operation and ensures the maximum resection of an intended target and minimum sacrifice of health tissues. In this investigation, the technique of content-based image retrieval (CBIR) coupled with machine learning algorithms are exploited in designing a computer aided path planning system (CAP) to assist junior doctors in planning surgical paths while sustaining the highest precision. Specifically, after evaluation of approaches of sparse coding and K-means in constructing a codebook, the model of sparse codes of 3D SIFT has been furthered and thereafter employed for retrieving, The novelty of this work lies in the fact that not only the existing algorithms for 2D images have been successfully extended into 3D space, leading to promising results, but also the application of CBIR that is mainly in a research realm, to a clinical sector can be achieved by the integration with machine learning techniques. Comparison with the other four popular existing methods is also conducted, which demonstrates that with the implementation of sparse coding, all methods give better retrieval results than without while constituting the codebook, implying the significant contribution of machine learning techniques. ► A codebook for 3D MR brain images has been generated to represent content features. ► 3D SIFT coupled with sparse codes are tuned for the creation of a codebook. ► Extensions into 3D from 2D algorithms have been refined to fit for purpose. ► The proposed feature representation approach using codebook performs the best. ► The specified research demonstrates potentials in future clinical applications.