Segmentation & Classification of Plasmodium Species Using Image Processing and Machine Learning Techniques

Segmentation is one of the important steps for image analysis. Multilevel thresholding image segmentation was more popular in image segmentation. Otsu and Kapur based methods are most popular for multilevel threshold image segmentation. 

Many authors implemented evolutionary algorithms for the optimal multilevel threshold selection based on the above methods. In this work, an efficient approach for multilevel image segmentation has been proposed and implemented based on Kapur and Otsu strategy. 

After the segmentation process, classification will be performed on Plasmodium species using Machine Learning algorithm- Random Forest. The types of plasmodium species are P. Falciparum, P. Malariae and P. Vivax which are collected from Kaggle portal. To check the effectiveness of our method/work, image entropy of Kapur and Otsu strategy and classification results are evaluated.

Keywords: Image Segmentation, Multilevel Thresholding, Kapur and Otsu Strategy, Machine Learning, Classification, Random Forest, Plasmodium Species.