Image processing and pattern recognition for ROI compression in cancer detection

One of the goals of the National Cancer Institute (NCI) cancer control program to reach more than 80% of the eligible women in mammography screening by the year 2000 was not fully realized and yet remains as a challenge. In fact, breast cancer is the only type of cancer with a positive growth rate over the last few years (+1 .2%). That is primarily due to 1)the fact that examination process is a complex and lengthy one and 2) it is not available to the majority of women who live in remote and urban sites. This problem can be solved using advanced networking technologies and signal processing algorithms. On one hand, software modules can help detect, with high precision, true negatives (TN), while marking true positives (TP) for further investigation. Since TNs are the majority of examinations on a randomly selected population, this first step reduces the load on radiologists by a tremendous amount. On the other hand, high-speed networking equipment can accelerate the required clinic-lab connection and make detection, segmentation and image enhancement algorithms readily available to the radiologists. This research describes the Asynchronous Transfer Mode (ATM) Telemammography Network (ATMTN) architecture for real-time, on-line screening, detection and diagnosis of breast cancer. ATMTN is a high-speed network integrated with associated automatic robust Computer Assisted Diagnostic (CAD) methods for mass detection