Breast image pre-processing for mammographic tissue segmentation.
During mammographic image acquisition, a compression paddle is used to even the breast thickness in order to obtain optimal image quality. Clinical observation has indicated that some mammograms may exhibit abrupt intensity change and low visibility of tissue structures in the breast peripheral areas. Such appearance discrepancies can affect image interpretation and may not be desirable for computer aided mammography, leading to incorrect diagnosis and/or detection which can have a negative impact on sensitivity and specificity of screening mammography. This paper describes a novel mammographic image pre-processing method to improve image quality for analysis. An image selection process is incorporated to better target problematic images. The processed images show improved mammographic appearances not only in the breast periphery but also across the mammograms. Mammographic segmentation and risk/density classification were performed to facilitate a quantitative and qualitative evaluation. When using the processed images, the results indicated more anatomically correct segmentation in tissue specific areas, and subsequently better classification accuracies were achieved. Visual assessments were conducted in a clinical environment to determine the quality of the processed images and the resultant segmentation. The developed method has shown promising results. It is expected to be useful in early breast cancer detection, risk-stratified screening, and aiding radiologists in the process of decision making prior to surgery and/or treatment.
Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK. Electronic address: weh@aber.ac.uk.
Wenda
W
He
School of Health Sciences, University of Salford, Salford M6 6PU, UK. Electronic address: p.hogg@salford.ac.uk.
Peter
P
Hogg
Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK. Electronic address: arne.juette@nnuh.nhs.uk.
Arne
A
Juette
Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK. Electronic address: erika.denton@nnuh.nhs.uk.
Erika R E
ER
Denton
Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK. Electronic address: rrz@aber.ac.uk.
Reyer
R
Zwiggelaar
D015502:Absorptiometry, Photon / Q000379:methods*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique
BI-RADS; Density classification; Mammographic segmentation; Peripheral enhancement; Risk assessment; Tabár