Bioengineering and Geomatics: Automatic Brain Image Segmentation using Two-Stage Pipeline with SNN and Watershed Algorithm

Abstract

Digital image processing holds an increasingly essential role in the medical domain. This study emphasizes the significance of researching and implementing methods aimed at the segmentation of critical image regions and potential noise reduction, which is indispensable for medical professionals in disease diagnosis. Consequently, the investigation of software solutions in this context can substantially enhance diagnostic accuracy. In particular, neurology stands as a medical field wherein imaging plays a substantial contributory role. In pursuit of an automated brain image segmentation approach, this paper centers its attention on a two-step pipeline methodology to address the segmentation challenges inherent in medical imaging. The proposed method incorporates the use of a Self-Normalizing Neural Network (SNN) for denoising and employs the Watershed algorithm, typically employed in Geomatics imagery, for segmentation. Encouraging results are obtained, with a segmentation performance, as measured by IoU, reaching a noteworthy value of 0.93 when compared with alternative segmentation software.