Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy.
Mahdieh PoostchiIlker ErsoyKatie McMenaminEmile GordonNila PalaniappanSusan PierceRichard James MaudeAbhisheka BansalPrakash SrinivasanLouis MillerKannappan PalaniappanGeorge ThomaStefan JaegerPublished in: Journal of medical imaging (Bellingham, Wash.) (2018)
Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.
Keyphrases
- plasmodium falciparum
- induced apoptosis
- machine learning
- endothelial cells
- deep learning
- cell cycle arrest
- flow cytometry
- pulmonary tuberculosis
- global health
- optical coherence tomography
- induced pluripotent stem cells
- single cell
- red blood cell
- cell therapy
- high resolution
- stem cells
- public health
- computed tomography
- high throughput
- oxidative stress
- type diabetes
- magnetic resonance
- mesenchymal stem cells
- risk factors
- mycobacterium tuberculosis
- big data
- label free
- clinical practice
- real time pcr
- quantum dots