A Fully Automatic Method for Breast Lesions Segmentation in Ultrasound Images
Abstract
Due to inherent speckle noise, poor quality data, low
contrast, and large shape variations of ultrasound lesions, automatic
segmentation in breast ultrasound images is still a challenging task. In
this paper, we propose a novel algorithm to segment automatically
breast lesion in ultrasound images. The images are first filtered with a
Speckle Reducing Anisotropic Diffusion algorithm to remove speckle
noise. After that we propose the use of some morphological operators
namely the contrast enhancement, the adaptive thresholding, the
suppression of small regions and regions connected to the image
border, the erosion and dilation, and finally the edge detection
technique for initial lesion localization. Finally, we apply the
Muti-scale Vector Field Convolution snake for boundary lesion
segmentation. A comparative study with previous state of the art
algorithms of active contours using both qualitative and quantitative
measures for real breast ultrasound images are presented in this study
in order to evaluate the performance of our method. Experiments
demonstrate the ability to detect breast lesions automatically, quickly,
efficiently and with high accuracy.
Keywords
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