3D CBIR with sparse coding for image-guided neurosurgery

Abstract

an open access repository of Middlesex University research http://eprints.mdx.ac.uk Qian, Yu; Hui, Rui; Gao, Xiaohong W., 2012. 3D CBIR with sparse coding for image-guided neurosurgery. Available from Middlesex University's Research Repository. Copyright:Middlesex University Research Repository makes the University's research available electronically.Copyright and moral rights to this work are retained by the author and/or other copyright owners. No part of the work may be sold or exploited commercially in any format or medium without the prior written permission of the copyright holder(s). A copy may be downloaded for personal, noncommercial, research or study without prior permission and without charge. Any use of the work for private study or research must be properly acknowledged with reference to the work's full bibliographic details.This work may not be reproduced in any format or medium, or extensive quotations taken from it, or its content changed in any way, without first obtaining permission in writing from the copyright holder(s).If you believe that any material held in the repository infringes copyright law, please contact the Repository Team at Middlesex University via the following email address: eprints@mdx.ac.ukThe item will be removed from the repository while any claim is being investigated. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Abstract: 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 th...

Classification of CT brain images based on deep learning networks

“…As demonstrated in Figure 3, the implementation of 3D SIFT engages in the process of SIFT feature detection through the application of Difference of Gaussian (DoG) operators and sparse coding [18,19], codebook generation by the employment of the paradigm of Bag of visual Words (BoW) [20,21], image representation using the created codebook and finally classification based on support vector machines (SVM).…”

Tracking multiple surgical instruments in a near-infrared optical system

“…Finally, after simulating the surgical procedure, the doctor can actually perform the operation aided by a video monitoring system and stereotaxic device. With the rapid development in CAS, it has been used in neurosurgery, [6,7] plastic surgery, [8,9] osteopathy [10][11][12] and otolaryngology, [13] among other medical fields. As an important tool in CAS, a surgical navigation system (SNS) can show the location and direction of surgical instruments relative to the lesion region to guide surgical operations with real-time images.…”

Proposta de um sistema CBIR baseado em ordem

“…Em relevância de feedback, são estudados os exemplos positivos e negativos selecionados pelos usuários com base no resultado do retorno, a fim de aprimorar o processo de recuperação(QIAN RUI HUI, 2012). and e YU (2007) cita uma aplicação de classificação e recuperação com foco em movimentação humana num espaço 3D.…”