On the Use of Fully Convolutional Networks on Evaluation of Infrared Breast Image Segmentations - de Melo, Rafael H. C.; Conci, Aura; Vasconcelos, Cristina Nader (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2017: Anais do XVII Workshop de Informática Médica, 2017)
Medical images usually must have their region of interest (ROI) segmented as a first step in a pattern recognition procedure. Automatic segmentation of these images is an open issue. This paper presents an automated technique to define the ROI for infrared breast exams, based on the use of Fully Convolutional Networks (FCN). Adequate comparison among new approaches by using available databases is very important, here some comparisons with other techniques are made. Moreover, concerning on line diagnosis, the comparison among possible techniques must be efficient enough to be done in real time. With our approach the time to segment the ROI was 100 milliseconds and the average accuracy obtained was 95%.