Computer-aided breast cancer diagnosis based on image segmentation and interval analysis
Qing Liu, Zhigang Liu, Shenghui Yong, Kun Jia, Navid Razmjooy
Abstract
Uncertainties are one principal part of any practical problem. Like any application, image processing process has different unknown parts as uncertainties which are derived from different reasons like initial digitalization, sampling to noise, special domain, and intensity. This study presents strong image segmentation for the breast cancer mammography images by considering the interval uncertainties. To consider the system uncertainties, interval analysis has been proposed. The main prominence of this method is taking into account errors in independent variables. An unclear method has the element of subjectivity, while the deterministic methods are not applicable in all cases. Besides, this method is always guaranteed to include the exact result, no matter that its upper and lower bounds happen to be overestimated. The principle theory here is to develop the traditional Laplacian of Gaussian filter based on interval analysis to consider the intensity uncertainties. Experimental results are applied on MIAS that is a popular breast cancer database for medical image segmentation. The performance of the system has been compared with Prewitt, LoG and canny filters based on PSNR.