A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis.
Breast ultrasound images have several attractive properties that make them an interesting tool in breast cancer detection. However, their intrinsic high noise rate and low contrast turn mass detection and segmentation into a challenging task. In this article, a fully automated two-stage breast mass segmentation approach is proposed. In the initial stage, ultrasound images are segmented using support vector machine or discriminant analysis pixel classification with a multiresolution pixel descriptor. The features are extracted using non-linear diffusion, bandpass filtering and scale-variant mean curvature measures. A set of heuristic rules complement the initial segmentation stage, selecting the region of interest in a fully automated manner. In the second segmentation stage, refined segmentation of the area retrieved in the first stage is attempted, using two different techniques. The AdaBoost algorithm uses a descriptor based on scale-variant curvature measures and non-linear diffusion of the original image at lower scales, to improve the spatial accuracy of the ROI. Active contours use the segmentation results from the first stage as initial contours. Results for both proposed segmentation paths were promising, with normalized Dice similarity coefficients of 0.824 for AdaBoost and 0.813 for active contours. Recall rates were 79.6% for AdaBoost and 77.8% for active contours, whereas the precision rate was 89.3% for both methods.
Optics Center, Universidade da Beira Interior, Covilhã, Portugal. Electronic address: jrafael.ubi@gmail.com.
Rafael
R
Rodrigues
Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal.
Rui
R
Braz
Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal.
Manuela
M
Pereira
Faculty of Health Sciences, Universidade da Beira Interior, Covilhã, Portugal.
José
J
Moutinho
Optics Center, Universidade da Beira Interior, Covilhã, Portugal.
Antonio M G
AM
Pinheiro
D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D016217:Ultrasonography, Mammary
Breast ultrasound; Image analysis; Image segmentation; Scale-space analysis