Image Segmentation Methods for Identifying Submerged Particles of Low Contrast Images

Abstract: The lack of automated concepts is considered as a major reason for barriers for investments into biotechnological processes that serve bulk chemicals and can provide a substantial part towards a more sustainable economy. Hence, this paper presents a method for enhancing the automation of submerged microalgae particles recognition, counting, and classifying using a microscopic device for in-situ imaging. The proposed method includes image de-noising using Anisotropic Diffusion technique; image normalization by Contrast Limited Histogram Equalization (CLAHE) method, image enhancement by morphological operations, a region of interest (ROI) extraction, and image segmentation. Furthermore, the ROI are classified into biological algae cells and others (i.e. grain stones) based on ROI’s size and texture. This method is applied on different datasets as synthetic and real microscopic images of microalgae. The experimental results proved that the microalgae particles can be quantified and classified correctly with accuracy reaches up to 99% and 100% for the segmentation and classification processes, respectively, compared to reference values.