Radial-Based Cell Formation Algorithm for Separation of Overlapping Cells in Medical Microscopic Images

A Novel Contrast Enhancement Technique Based on Combination of Local and Global Statistical Data on Malaria Images

Malaria appears to be one of the main reasons for detrimental health issue at the global scale that is responsible for approximately half a million deaths every year. As the cases of malaria seem to escalate at an annual rate, it is vital to provide a rapid and accurate diagnosis through manual microscopic assessment in the attempt to control the spread of malaria. Nevertheless, varied staining steps and noise disruptions can cause inaccurate diagnosis due to wrong interpretation. Hence, to address such issues, this study investigated the performance upon removing background noise and the method of correcting illumination that has an impact upon segmentation for a computer-assisted diagnostic system. The findings display that the technique of based on Otsu threshold and statistic data used to enhance the contrast image as to determine cells infected by the malaria parasite, in comparison to other methods. In fact, this method was tested on 450 malaria images, which consisted of P. Vivax, P. Falciparum, and P. Knowlesi species at the stages of trophozoite, schizont, and gametocyte. As a result, the HSE approach yielded 1.31 for Global Contrast Factor (GCF), while 10.56 for Signal Noise Ratio (SNR).