In Vivo Thermography-Based Image for Early Detection of Breast Cancer Using Two-Tier Segmentation Algorithm and Artificial Neural Network

Abstract

Breast cancer is the most common form of cancer among women globally. Detecting a tumor at its early stages is very crucial for a higher possibility of successful treatment. Cancerous cells have high metabolic rate which generate more heat compared to healthy tissue and will be transferred to the skin surface. Thermography technique has distinguished itself as an adjunctive imaging modality to the current gold standard mammography approach due to its capability in measuring the heat radiated from the skin surface for early detection of breast cancer. It provides an additional set of functional information, describing the physiological changes of the underlying thermal and vascular properties of the tissues. However, the thermography technique is shown to be highly dependent on the trained analyst for image interpretation and most of the analyses were conducted qualitatively. Therefore, the current ability of this technique is still limited especially for massive screening activity. This chapter presented a proposed technical framework for automatic segmentation and classification of abnormality on multiple in vivo thermography-based images. A new two-tier automatic segmentation algorithm was developed using a series of thermography screening conducted on both pathological and healthy Sprague-Dawley rats. Features extracted show that the mean values for temperature standard deviation and pixel intensity of the abnormal thermal images are distinctively higher when compared to normal thermal images. For classification, Artificial Neural Network system was developed and produced a validation accuracy performance of 92.5% for thermal image abnormality detection. In conclusion, this study has successfully demonstrated that for massive or routine screening activities, the proposed technical framework could provide a highly reliable clinical decision support to the clinicians in making a diagnosis based on the medical thermal images.