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

TL;DR

This paper introduces a low-complexity imaging framework for breast cancer detection that adaptively refines resolution in tumor regions, significantly reducing computational load without sacrificing accuracy.

Contribution

It proposes an iterative segmentation and resolution enhancement framework applicable to beamforming techniques like DAS and DMAS, reducing computational complexity effectively.

Findings

Significant reduction in computational complexity for DAS and DMAS.

Maintains accuracy of traditional imaging methods despite complexity reduction.

Provides manual and automatic methods for complexity control.

Abstract

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.