Information-theoretic-criterion-based tumor detection in contrast-enhanced microwave imaging

We present a new approach to the problem of detecting cancerous tissues at low-to-medium signal-to-noise ratios (SNRs) in an interference-prone biological medium, where the dielectric properties of the surrounding heterogeneous healthy tissues are comparable to those of the tumor. Suppose that microwave contrast agents such as microbubbles or nanocomposites are selectively delivered to the cancer site via systemic administration, and the difference between the backscatter responses before and after the administration of contrast medium to the tissue anomalies are extracted. We can then formulate the tumor detection problem from the perspective of signal model selection. As a result, the Akaike information criterion (AIC) can be applied as a blind method to detect the malignant tumor. Numerical examples based on a canonical biological phantom are presented to evaluate the performance of the proposed AIC-based algorithm.