Blood Smear Malarial Parasite Detection Austin Zheng
EE368, Department of Electrical Engineering, Stanford University
Motivation Parasite Detection and Isolation Pipeline
Further Work Results (5 test images)
HSV conversion
H, S and V components (histogram equalization applied)
Thresholding
Candidate analysis Nuclei detection
Attempt cell segmentation of the region.
Malarial RBC? Spurious signal?
Free-floating parasite?
Partial to complete automation of blood smear counting of red blood cells (RBCs) infected with malaria parasite (P. falciparum)
Valid RBC
Spurious stain
Better circle detection better handle elliptical cells, poor segmentation, blended shapes, etc.
Automatic preprocessing parameter calculation resilience to noise, differing lighting, poor image quality, etc.
Challenges: • Low image quality (lighting, focus) • Flaws in smear procedure • Overlapping cells • Clutter • Non-circular (ovoid or deformed RBCs)
Original image
Cell edge detection
Better nucleus discrimination
Schizont Trophozoite Ring Note: Edge nuclei intentionally omitted.
Schizont form (large dark mass) currently produces poor results
Segmentation may be impossible in some cases. Other heuristics may be necessary in conjunction
with cell segmentation.
Image 1 3 successfully detected
clusters, 6 spurious/missed
clusters
Image 2 4 successfully detected
clusters, 0 spurious/missed
clusters
Image 3 6 successfully detected
clusters, 0 spurious/missed
clusters
Image 4 2 successfully detected
clusters, 12 spurious/missed
clusters
Image 5 5 successfully detected
clusters, 6 spurious/missed
clusters
Post-processing can remove redundant cell signatures from well-
formed output.
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