Book chapter
Mammographic Density Effect on Readers’ Performance and Visual Search Pattern
Breast Imaging, pp.174-180
Lecture Notes in Computer Science, Springer International Publishing
2014
DOI: 10.1007/978-3-319-07887-8_25
Abstract
A test set of 150 digital mammograms were examined by 14 radiologists, seven of which underwent eye-position recording. Mammograms were classified into low- and high- density cases, in order to investigate the impact of density on readers’ performance and visual search patterns. Lesions overlaying were compared to those outside the dense fibroglandular tissue. Our results suggest that when the lesion was overlaying the fibroglandular tissue, readers’ performance significantly improved in high- compared to low- density cases. Also the dense area of breast parenchyma attracted the radiologists’ visual attention, in both low- and high- mammographic density cases. When the lesions were outside the dense fibroglandular tissue, no difference was noted in radiologist’ performance. In conclusion, dense areas of the breast parenchyma attracted the radiologists’ visual attention, in both low- and high density cases, which might improve lesion detection when the malignancy is overlaying the dense parts of the breast tissue.
Details
- Title: Subtitle
- Mammographic Density Effect on Readers’ Performance and Visual Search Pattern
- Creators
- Dana S AL Mousa - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, AustraliaPatrick C Brennan - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, AustraliaElaine A Ryan - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, AustraliaClaudia Mello-Thoms - Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Sydney, Lidcombe, Australia
- Resource Type
- Book chapter
- Publication Details
- Breast Imaging, pp.174-180
- Publisher
- Springer International Publishing; Cham
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-319-07887-8_25
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2014
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology
- Record Identifier
- 9984051501502771
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