A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast Cancer Detection
This work addresses computational efficiency for medical imaging in breast cancer detection, representing an incremental improvement over existing methods.
The paper tackles the high computational complexity of ultra-wideband imaging for breast cancer detection by proposing a low-complexity framework that iteratively segments the breast and increases resolution only in tumor-containing areas, achieving significant complexity reduction for DAS and DMAS beamforming techniques without accuracy degradation.
In this research work, a novel framework is pro- posed as an efficient successor to traditional imaging methods for breast cancer detection in order to decrease the computational complexity. In this framework, the breast is devided into seg- ments in an iterative process and in each iteration, the one having the most probability of containing tumor with lowest possible resolution is selected by using suitable decision metrics. After finding the smallest tumor-containing segment, the resolution is increased in the detected tumor-containing segment, leaving the other parts of the breast image with low resolution. Our framework is applied on the most common used beamforming techniques, such as delay and sum (DAS) and delay multiply and sum (DMAS) and according to simulation results, our framework can decrease the computational complexity significantly for both DAS and DMAS without imposing any degradation on accuracy of basic algorithms. The amount of complexity reduction can be determined manually or automatically based on two proposed methods that are described in this framework.