Book Description
Breast tumor segmentation in ultrasound is important for breast ultrasound (BUS) quantitative analysis and clinical diagnosis. Even this topic has been studied for a long time, it is still a challenging task to segment tumor in BUS accurately arising from difficulties of speckle noise and tissue background inconsistence. To overcome these difficulties, we formulate breast tumor segmentation as a classification problem in the neutrosophic set (NS) domain which has been previously studied for removing speckle noise and enhancing contrast in BUS images. The similarity set score and homogeneity value for each pixel have been calculated in the NS domain to characterize each pixel of BUS image. Based on that, the seed regions are selected by an adaptive Otsu-based thresholding method and morphology operations, then an adaptive region growing approach is developed for obtaining candidate tumor regions in NS domain.
