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Overview
Overview
The project dealt with development of a novel algorithm is for computer aided diagnosis (CAD) of Malaria which is able to diagnose Malaria parasites from the microscopic images of thin blood smear as well as thick blood smear. Additionally, the algorithm is also able to classify the stained particles like white blood cells and other 3 forms of parasite in order to estimate parasitemia. The discrepancy in the automated parasite count by the proposed algorithm is 7.14%, which is suitable for computer aided diagnosis (CAD) of malaria according to world health organization (WHO) quality control standards. Studied and implemented various computer vision techniques like image denoising, focus plane stacking, image segmentation, feature extraction techniques and machine learning algorithms in order to detect and classify malaria parasites from microscopic images.
Results of Malaria Parasite Detection for Microscopic images of Thin Blood Smear
Results of Malaria Parasite Detection for Microscopic images of Thin Blood Smear
The blue components you can see in the image is called "Red Blood Cells". And RBCs, which are infected by Malarial Parasites are detected and shown in red highlighted color.
Results of Malaria Parasite Detection for Microscopic images of Thick Blood Smear
Results of Malaria Parasite Detection for Microscopic images of Thick Blood Smear
Red Blood Cells" are completely liased in Thick blood smear and Malarial parasite detection is not easy. The detected Malarial parasites are highlighted using white rings in the results.
Results of Complete Analysis for Microscopic images of Thick Blood Smear
Results of Complete Analysis for Microscopic images of Thick Blood Smear
Results shown here is complete analysis of Microscopic image of Thick Blood smear.
The complete analysis performs following tasks:
- Detection and classification of Malarial parasite of all life stage which is novelty of the algorithm.
- Detection of White Blood Cells which allows the user to generate report of Malaria severity in less than a minute.
Related Publication
Related Publication
- I. Dave, "Image Analysis for Malaria Parasite Detection from Microscopic Images of Thick Blood Smear", in International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2017, pp. 1331-1335. (DOI: 10.1109/WiSPNET.2017.8299974) (Scopus Indexed)
(Full-text available) - I. Dave and K. Upla, "Computer Aided Diagnosis of Malaria Disease for Thin and Thick Blood Smear Microscopic Images", in 4th International Conference on Signal Processing and Integrated Networks (SPIN), Delhi-NCR, 2017, pp. 561-565. (DOI: 10.1109/SPIN.2017.8050013) (Scopus Indexed)
(Full-text available)
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