This paper proposes a novel, efficient, low complexity algorithm for edge detection, with a cheap, easily accessible, networkable hardware implementation, specifically focused on the analysis of malaria infected thin blood smears. The algorithm presents a new and dynamic thresholding technique that eliminates inter-cell interference based on histogram analysis. Following this, binary image morphological processing is performed which is shown to outperform the same operation on the much more complex greyscale images. Edge tracking is done via a simplified fuzzy logic inspired rule system. The entire system is implemented on multiple platforms to test widespread compatibility but primarily developed for a battery powered standalone raspberry pi with low power, low resolution touchscreen and hardware buttons. The entire algorithm was pitted against the much more complex but still very well performing Canny algorithm, which despite the age, is still one of the most comprehensive edge detection techniques available; modern variants were considered and reviewed, but ultimately given the level of outperformance, they were not viable options.