.png){kind=icon}
返回
3D CBIR with sparse coding for image-guided neurosurgery
.png){kind=icon}
PRE
AI
DOI：10.1016/j.sigpro.2012.10.020.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.
Keyword:
CBIR
Computer aided path planning
Neurosurgery
3D SIFT
Sparse coding
期刊
IF:
3.6
论文数:
10.0K
被引数:
1.7W
机构
引用论文
.png){kind=icon}
分享收藏
Assessment of the Pacific Tsunami Warning Center’s Capabilities for Puerto Rico and the Virgin Islands based on the Computation of Detection and Response Times Accounting for Seismic Network Topology and Data Latencies基于地震网络拓扑和数据延迟计算检测与响应时间，对太平洋海啸预警中心为波多黎各和维尔京群岛提供的能力评估
PREAI
分享收藏
查看更多内容