González José R, Damião Charbel, Conci Aura
Computing Institute, Federal Fluminense University, Niterói, Rio de Janeiro, Brazil.
Antonio Pedro University Hospital, Federal Fluminense University, Niterói, Rio de Janeiro, Brazil.
Stud Health Technol Inform. 2017;245:384-387.
Thyroid nodules diseases are a common health problem and thyroidal cancer is becoming increasingly prevalent. They appear in the neck and bottom neck region, superficially over the trachea. Cancer tissues are characterized by higher temperatures than surrounding tissues. Thermography is a diagnostic tool increasingly used to detect cancer and abnormalities. Artificial intelligence is an approach which can be applied to thyroid nodules classification, but is necessary to have a proper number of cases with proven diagnosis. In this paper, a new database that contain infrared thermal images, clinical and physiological data is presented. The description of each nodule per patient, and the acquisition protocol (based on Dynamic Infrared Thermography approach) is considered as well. A semi-automatic method for image registration was implemented to pre-process the thermograms and a new method for the Region of Interest (ROI) extraction is proposed. Moreover, the obtained ROI results are confirmed by medical specialists and turned available for future comparison with other works.
甲状腺结节疾病是一个常见的健康问题，甲状腺癌的发病率也在日益上升。它们出现在颈部和颈部底部区域，位于气管表面。癌组织的特征是温度高于周围组织。热成像技术是一种越来越多地用于检测癌症和异常情况的诊断工具。人工智能是一种可应用于甲状腺结节分类的方法，但需要有足够数量的经证实诊断的病例。本文介绍了一个包含红外热图像、临床和生理数据的新数据库。还考虑了对每位患者每个结节的描述以及采集协议（基于动态红外热成像方法）。实施了一种半自动图像配准方法来预处理热成像图，并提出了一种新的感兴趣区域（ROI）提取方法。此外，获得的ROI结果得到了医学专家的确认，并可供未来与其他研究进行比较。
