| Summary: | Breast cancer is the most common form of cancer in women. The importance of diagnosing breast cancer is one of the important issues in medical science. Diagnosis of benign or malignant cancer is of great importance in addition to reducing costs in the direction of treatment. A non-destructive test method for early detection of breast cancer is image processing. Image processing has various uncertainties which are generated by different reasons such as sampling to noise, initial digitalization, intensity, and special domain. In this study, a strong image segmentation method based on interval uncertainties is proposed for the breast cancer images diagnosis. The main purpose of this paper is to improve the ordinary Sobel filter based on interval analysis by considering the intensity uncertainties. Simulation results have been implemented on MIAS which is an applicable database for breast cancer detection. The results of the proposed method have been compared with some state of the art methods such as LoG, Prewitt and canny filters. Final results showed that using the proposed method gives better achievement than the others by considering some kinds of uncertainties like Gaussian noise and salt and pepper noise.
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