Classification of Hyperspectral Endocrine Tissue Images Using Support Vector Machines.

: Thyroidectomy is one of the most commonly performed surgical procedures. The region of the neck has a very complex structural organization. It would be beneficial to introduce a tool that can assist the surgeon in tissue discrimination during the procedure. One such solution is the non-invasive and contactless technique, called hyperspectral imaging (HSI). To interpret the HSI data, we implemented a supervised classification method to automatically discriminate the parathyroid, the thyroid and the recurrent laryngeal nerve (RLN) from surrounding tissue (muscle, skin) and materials (instruments, gauze). A one-leave-out-patient cross-validation was performed. The best performance was obtained using support vector machine (SVM) with a classification and visualization in less than 1.4 seconds. A mean patient accuracy of 68 ± 23% was obtained for all tissues and material types. The proposed method showed promising results and have to be confirmed on a larger cohort of patient data.