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In this research work, a novel framework is pro- posed as an efficientsuccessor to traditional imaging methods for breast cancer detection in orderto decrease the computational complexity. In this framework, the breast isdevided into seg- ments in an iterative process and in each iteration, the onehaving the most probability of containing tumor with lowest possible resolutionis selected by using suitable decision metrics. After finding the smallesttumor-containing segment, the resolution is increased in the detectedtumor-containing segment, leaving the other parts of the breast image with lowresolution. Our framework is applied on the most common used beamformingtechniques, such as delay and sum (DAS) and delay multiply and sum (DMAS) andaccording to simulation results, our framework can decrease the computationalcomplexity significantly for both DAS and DMAS without imposing any degradationon accuracy of basic algorithms. The amount of complexity reduction can bedetermined manually or automatically based on two proposed methods that aredescribed in this framework.

A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast Cancer Detection