A golden section search method for the identification of skin subsurface abnormalities

Abstract

The metabolic heat generation rate directly quantifies either the presence or the absence of tumour and the degree of inflammations in the biological tissue. This work presents an inverse method aimed at estimating the rate of metabolic heat generation using the dynamic temperature response from the skin obtained under an imposed heat flux by solving the transient non-Fourier thermal wave bioheat equation using the finite difference method. The golden section search method (GSSM)-based inverse method has been applied to solve an inverse problem minimizing the Euclidean norm of the known and guessed temperature distributions. The effect of random noise has been studied and a reasonably accurate identification of healthy and unhealthy metabolic heat generation rates has been characterized. The present work offers a novel inverse technique based on GSSM algorithm to predict the pertinent heat generation rate from the transient temperature distribution that can be obtained using spatially offset Raman spectroscopy, microwave thermography and other potential techniques.