Analysis of thyroid thermographic images for detection of thyroid tumor: An experimental-numerical study

Thermography is a developing and noninvasive medical imaging technique that can be used for diagnosis of body disorders based on temperature deviation from normal body temperature. This research investigates the feasibility of thermography method in conjunction with artificial neural networks (ANNs) for detection of thyroid tumors. For this purpose, first, a 3-D model of the healthy human neck is constructed based on patient-specific computed tomography (CT) images. This model is used for analyzing bio-heat transfer in the human neck. The healthy thyroid gland is considered as a heat source and generates heat according to its temporal temperature. Finite element results verify the thermography potential for detection of thyroid gland location and estimation of its butterfly shape on the neck thermogram. The numerical analysis is carried out on 35 models with varying thermo-physical parameters of the healthy thyroid gland, including heat generation and blood perfusion. The acquired thermograms are used to develop an ANN for correlating the thermo-physical parameters of the gland and temperature profile on the neck surface. In the next stage, dynamic thermal images are captured from 10 healthy and three cancerous human cases. The experimental thermal images are analyzed by the developed ANN and the corresponding thermo-physical parameters are obtained. Results show that the estimated heat generation values for the healthy cases are about 3000 $$ while it increases to more than 12 000 $$ for the cases with tumors. This significant variation confirms the potential of dynamic thermography in diagnosis of thyroid tumors.