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Discrete Cosine Transform-based Microscope Focal Adjustment with RBC Classification Using Support Vector Machine

ommon blood disorders can be diagnosed from the condition of several components and features of the blood. Although hemocytometers exist, many medical laboratories still conduct manual blood counting (CBC). The objective of this study aims to lessen human intervention on a CBC by integrating an autofocus mechanism on the adjustment knobs of microscopes through a 2D-Discrete Cosine Transform algorithm, processing through several enhancements and techniques – morphological image processing, watershed algorithm, and blob analysis, and classify red blood cell abnormalities among discocytes, echinocytes, and stomatocytes with Support Vector Machine. The results of the SVM yielded an average of 91.55% verifying the accuracy of the system.