Detecting Breast Cancer using a Compressive Sensing Unmixing Algorithm
This work addresses breast cancer detection for medical imaging, presenting an incremental improvement by applying compressive sensing to a specific unmixing approach in microwave imaging.
The paper tackles breast cancer detection by developing an unmixing algorithm that separates breast tissue into low water content, high water content, and cancerous components, using compressive sensing in a hybrid DBT/NRI system, and demonstrates through numerical analysis that cancerous lesions can be detected based on mixture proportions under appropriate conditions.
Traditional breast cancer imaging methods using microwave Nearfield Radar Imaging (NRI) seek to recover the complex permittivity of the tissues at each voxel in the imaging region. This approach is suboptimal, in that it does not directly consider the permittivity values that healthy and cancerous breast tissues typically have. In this paper, we describe a novel unmixing algorithm for detecting breast cancer. In this approach, the breast tissue is separated into three components, low water content (LWC), high water content (HWC), and cancerous tissues, and the goal of the optimization procedure is to recover the mixture proportions for each component. By utilizing this approach in a hybrid DBT / NRI system, the unmixing reconstruction process can be posed as a sparse recovery problem, such that compressive sensing (CS) techniques can be employed. A numerical analysis is performed, which demonstrates that cancerous lesions can be detected from their mixture proportion under the appropriate conditions.