Malaria disease is one of the deadliest diseases. According to WHO report, in 2016, there were 212 million malaria cases with 429,000 estimated deaths around the world. Rapid and accurate detection of malarial infection could certainly help in reducing the above numbers. This paper focuses on segmentation and detection of microscopy images infected with malaria parasites using the colour-based cascading method. The method begins with colour normalization process, followed by gamma correction, then noise reduction, exposure compensation, edge enhancement, fuzzy c-means clustering, and lastly, morphological processes. We used the method to detect malaria infection for four Plasmodium types and three malaria development phases in 574 images. The experimental results demonstrated that the method achieved 97.91%, 98.61%, and 98.26% for sensitivity, specificity, and accuracy. These results suggest that the proposed method can detect malaria in microscopy images robustly.