A Two-Step Segmentation Method for Breast Ultrasound Masses Based on Multi-resolution Analysis

Ultrasound in Medicine & Biology
Rafael RodriguesAntonio M G Pinheiro

Abstract

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 normalize...Continue Reading

References

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Citations

Sep 7, 2016·The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS·Teresa AraújoSarthak Misra
Jan 11, 2017·International Journal of Computer Assisted Radiology and Surgery·Qinghua HuangQiangzhi Zhang
Jan 25, 2020·Current Medical Imaging Reviews·Rajendaran VairavanVithyacharan Retnasamy
Jan 12, 2021·Journal of Ultrasound·Ademola E IlesanmiStanislav S Makhanov
Jul 22, 2018·Ultrasonics·Yuan XuPaul L Carson

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