A Novel LCM²ICM: Low Contrast Malaria Microscopic Image Classification Measure

Computer assisted diagnosis of malaria disease through microscopy imaging gained the attention of many researchers during the last decade. Much attention has been drawn on segmentation and detection of malaria parasites in which contrast enhancement is preprocessing step for low contrast images leads to more accurate diagnosis results and for good contrast images leads to time consuming and unnatural image and therefore, it affected the accuracy of the diagnosis (increasing false diagnosis). In this regard, there is need to introduce a measure to classify poor and good contrast images before further processing. In this paper, we proposed a simple performance measure for classification poor contrast malaria microscopic medical images, which we called it as Low Contrast Malaria Microscopic Image Classification Measure (LCM²ICM). Low contrast malaria microscopic images have LCM²ICM value is less than 3(L ⁺ –1)/7 while good contrast images have more than or equal to 3(L ⁺ –1)/7. Experimental results show that the proposed measure yields better classification results. This measure has applications in segmentation, detection and classification of malaria parasites for computer aided diagnosis.