Classification of radiological errors in chest radiographs, using support vector machine on the spatial frequency features of False-Negative and False-Positive regions

Mariusz W. Pietrzyk, Tim Dono van, Patrick C. Brennan, Alan Dix, David J. Manning

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddCyfraniad mewn cynhadleddadolygiad gan gymheiriaid

7 Dyfyniadau (Scopus)

Crynodeb

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.

Iaith wreiddiolSaesneg
TeitlMedical Imaging 2011
Is-deitlImage Perception, Observer Performance, and Technology Assessment
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2 Maw 2011
Cyhoeddwyd yn allanolIe
DigwyddiadMedical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment - Lake Buena Vista, FL, Yr Unol Daleithiau
Hyd: 16 Chwef 201117 Chwef 2011

Cyfres gyhoeddiadau

EnwProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Cyfrol7966
ISSN (Argraffiad)1605-7422

Cynhadledd

CynhadleddMedical Imaging 2011: Image Perception, Observer Performance, and Technology Assessment
Gwlad/TiriogaethYr Unol Daleithiau
DinasLake Buena Vista, FL
Cyfnod16/02/1117/02/11

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