Classification of radiological errors in chest radiographs, using support vector machine on the spatial frequency features of false- negative and false-positive regions

Pietrzyk, Mariusz W., Donovan, Tim, Brennan, Patrick C., Dix, Alan and Manning, David J. (2011) Classification of radiological errors in chest radiographs, using support vector machine on the spatial frequency features of false- negative and false-positive regions. In: Manning, David J. and Abbey, Craig K., (eds.) Medical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment. SPIE Proceedings, 7966 . Society of Photo-Optical Instrumentation Engineers (SPIE), Bellingham, WA, US, 79660A. Full text not available from this repository.

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Official URL: https://doi.org/10.1117/12.878740

Abstract

Aim: To optimize automated classification of radiological errors during lung nodule detection from chest radiographs (CxR) using a support vector machine (SVM) run on the spatial frequency features extracted from the local background of selected regions.

Background: The majority of the unreported pulmonary nodules are visually detected but not recognized; shown by the prolonged dwell time values at false-negative regions. Similarly, overestimated nodule locations are capturing substantial amounts of foveal attention. Spatial frequency properties of selected local backgrounds are correlated with human observer responses either in terms of accuracy in indicating abnormality position or in the precision of visual sampling the medical images.

Methods: Seven radiologists participated in the eye tracking experiments conducted under conditions of pulmonary nodule detection from a set of 20 postero-anterior CxR. The most dwelled locations have been identified and subjected to spatial frequency (SF) analysis. The image-based features of selected ROI were extracted with un-decimated Wavelet Packet Transform. An analysis of variance was run to select SF features and a SVM schema was implemented to classify False-Negative and False-Positive from all ROI.

Results: A relative high overall accuracy was obtained for each individually developed Wavelet-SVM algorithm, with over 90% average correct ratio for errors recognition from all prolonged dwell locations.

Conclusion: The preliminary results show that combined eye-tracking and image-based features can be used for automated detection of radiological error with SVM. The work is still in progress and not all analytical procedures have been completed, which might have an effect on the specificity of the algorithm.

Item Type: Book Section
Publisher: Society of Photo-Optical Instrumentation Engineers (SPIE)
ISSN: 0277786X
ISBN: 9780819485083
Departments: Health and Medical Sciences
Additional Information: Event: SPIE Medical Imaging, 16–17 February 2011, Lake Buena Vista (Orlando), Florida, United States.
Depositing User: Anna Lupton
Date Deposited: 09 May 2019 14:10
Last Modified: 09 May 2019 14:10
URI: http://insight.cumbria.ac.uk/id/eprint/4749

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