Identification of Malaria-Infected Red Blood Cells Via Digital Shearing Interferometry and Statistical Inference

Abstract

Malaria is one of the most widespread diseases, particularly in Asia and Africa. Correct diagnosis of malaria is necessary for its proper treatment. A compact automated tool for malaria identification will greatly benefit healthcare professionals in these regions. We propose a method that has the potential to automatically detect malaria-infected red blood cells (RBCs). This method combines the simplicity and robustness of lateral shearing interferometry with the flexibility of statistical methods to achieve the classification of diseased RBCs. Shearing interferograms generated using a glass plate in a common path setup were Fourier analyzed to retrieve the gradient phase and amplitude information of the cell. Then, multiple features based on the complex amplitude information of the cells are measured automatically and used to differentiate healthy and malaria-infected cells. Multivariate statistical inference algorithm of the experimental data shows that there is a difference between the populations of healthy and malaria-infected RBCs by using the measured RBC features.

Microscopic malaria parasitemia diagnosis and grading on benchmark datasets

“…However, the study is highly subjected to occluded RBCs problems and there is no any procedure for malaria parasitemia grading or estimation. The study carried out by Moon, Anand, Cruz, and Javidi (), has used fluorescence slides obtained from fluorescence microscope. The main issue is these slides are not in access of a common man, doctor, and hospital because the microscope is very expensive.…”

“… The RBCs are considered as round in (a) (Moon et al, ) but according to experts RBCs due to other diseases become (b) (Clinical_Lab, ) [Color figure can be viewed at http://wileyonlinelibrary.com] …”

Novel image processing approach to detect malaria

“…This allows measuring the cell membrane stiffness, and distinguishing between different elements of the cell [20]. Speckle sensing microscopy [21] and shearing interferometry [22] schemes have been proposed for fast and automated detection of malaria. Moreover, multimodal imaging in microscopy provided by the combination of different techniques is a valuable tool for quantifying information about cell dynamics and evolutions at nanoscale range by cross-checking information between them [23][24][25][26][27].…”

Development of full‐field optical spatial coherence tomography system for automated identification of malaria using the multilevel ensemble classifier

“…From the World Health Organization record, the growing age children were more affected by the malaria parasite disease in the rural areas [1]. However, there are few limitations of these techniques [3][4][5][6][7][8][9][10][11][12], either they are suffering from poor interference signal contrast for deep layers or system is too complex [2,3,13]. It is mandatory to know the early stage malaria diagnosed from the patient's blood to avoid the risk of death and long-lasting infection, which increases the malaria associated diseases [2][3][4].…”

“…In the last decades, quantitative phase microscopic imaging methods, including refractive index tomography, optical diffraction tomography, digital holography quantitative phase microscopy/spectroscopy have been employed for the malaria infected stages of cell studies using structural, biochemical and mechanical features. However, there are few limitations of these techniques [3][4][5][6][7][8][9][10][11][12], either they are suffering from poor interference signal contrast for deep layers or system is too complex [2,3,13]. Low-coherence interferometry-based full-field optical coherence microscopy emerges as alternative imaging techniques to produce a complex image (both amplitude and phase image) [14].…”