A novel fuzzy logic inspired edge detection technique for analysis of malaria infected microscopic thin blood images
Stephen Bias, Saumya Kareem Reni, İzzet Kale · 2017
This paper proposes a novel, efficient, low complexity algorithm for edge detection, specifically focusing on the analysis of malaria infected microscopic thin blood smears. The algorithm proposes a simple, dynamic thresholding technique that is computed via histogram analysis, designed to capture as much information about the blood cells with minimal computational effort, which is followed by a morphological filtering process to remove noise and artifacts. A binary edge tracking system inspired by the works in fuzzy logic is introduced, defined by a semi ambiguous rule system that can be efficiently implemented in hardware.