Real-time imaging technology has the potential to be applied to many complex surgical procedures such as those used in treating people with breast cancer. Key delaying factors for the successful development of real-time surgical imaging solutions include long execution time due to poor medical infrastructure and inaccuracy in processing mammogram images. In this work, we introduce a novel imaging technique that identifies malignant cells and supports breast cancer surgical procedures by analyzing mammograms in real-time with excellent accuracy. According to this method, hidden attributes of a target breast image are extracted and the extracted pixel values are analyzed using machine learning (ML) tools to determine if there are malignant cells. A malignant image is divided into contours and the rate of change in pixel value is calculated to pinpoint the regions of interest (ROIs) for a surgical procedure. Experimental results using 1500 known mammograms show that the imaging mechanism has the potential to identify benign and malignant cells with more than 99% accuracy. Experimental results also show that the rate of change in pixel values can be used to determine the ROIs with more than 98% accuracy.