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

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 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.

Automated breast tumor detection and segmentation with a novel computational framework of whole ultrasound images

“…It is assumed that the regions which directly connected with the border were not part of the lesion regions, often resulting from artifacts or shadows in the ultrasound exam [29]. The regions connected with the image borders were directly deleted in many kinds of literature, such as [29]. However, sometimes, the lesion region is also connected with the border.…”

Lesion Segmentation in Ultrasound Using Semi-Pixel-Wise Cycle Generative Adversarial Nets

“…Agarwal et al [19] developed a semi-automatic framework for breast lesion segmentation in ABUS volumes which is based on the Watershed algorithm. Rodrigues et al [20] took the advantage of pixel-wise classification and achieved a DSC of 0.824. Kumar et al [21] proposed convolutional neural network approaches for breast ultrasound lesion segmentation and their algorithms effectively segmented the breast masses, achieving a mean DSC of 0.82.…”

Using Mean-shift Clustering and Statistical Analysis for Breast Mass Segmentation and Category

“…The degree of freedom f is expressed as (13) Since the weight used in Welch statistic is , one cannot compute the statistic if any one group has zero standard deviation. Moreover, sample sizes of all groups have to be greater than or equal to zero.…”

“…Breast mass segmentation is considered a crucial step in CAD systems. Several methods have proposed for segmentation of breast masses, such as the studies [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Terada et al [16] applied mean shift algorithm and an Iris filter to identify the possible regions and obtain gradient vectors of an image.…”