Role of Segmentation in Medical Imaging: A Comparative Study

Abstract

Rapid advances in the field of medical imaging are revolutionizing medicine. The determination of the presence or severity of disease will impact clinical care for a patient or outcome status in a study. The use of computer-aided diagnosis (CAD) systems to improve the sensitivity and specificity of lesion detection has become a focus of medical imaging and diagnostic radiology research. Accurate segmentation of medical images is a key step in contouring during radiotherapy planning. In this paper, segmentation problems in medical imaging modalities especially for lung CT as well as for thyroid ultrasound (US) are discussed along with their comparative results are shown using automatic tools as well as with some specific algorithms. Various automatic tools have been used and discussed. The results shows that though segmentation is the crucial, required and most difficult phase yet the outcome is really advantageous in medicine for the perfect diagnosis of any disease. Both the outcomes either from automatic tool as well as using an algorithm provide the required ROI (region of interest).