Fluid segmentation in Neutrosophic domain
This is an incremental improvement for ophthalmologists using OCT to detect retinal diseases.
The paper tackles fluid segmentation in optical coherence tomography (OCT) images for diagnosing age-related macular degeneration and diabetic macular edema by adapting neutrosophic C-means clustering, resulting in a 6% improvement in dice coefficient and sensitivity over existing methods.
Optical coherence tomography (OCT) as retina imaging technology is currently used by ophthalmologist as a non-invasive and non-contact method for diagnosis of agerelated degeneration (AMD) and diabetic macular edema (DME) diseases. Fluid regions in OCT images reveal the main signs of AMD and DME. In this paper, an efficient and fast clustering in neutrosophic (NS) domain referred as neutrosophic C-means is adapted for fluid segmentation. For this task, a NCM cost function in NS domain is adapted for fluid segmentation and then optimized by gradient descend methods which leads to binary segmentation of OCT Bscans to fluid and tissue regions. The proposed method is evaluated in OCT datasets of subjects with DME abnormalities. Results showed that the proposed method outperforms existing fluid segmentation methods by 6% in dice coefficient and sensitivity criteria.