Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears

2014 ◽  
Vol 257 (3) ◽  
pp. 238-252 ◽  
Author(s):  
D. K. DAS ◽  
A. K. MAITI ◽  
C. CHAKRABORTY
Keyword(s):  
Automated System ◽  
Microscopic Images ◽  
Blood Smears ◽  
Thin Blood Smears

scholarly journals AUTOMATED CLASSIFICATION OF NORMAL AND ABNORMAL LEUKOCYTES

1974 ◽  
Vol 22 (7) ◽  
pp. 697-706 ◽  
Author(s):  
J. F. BRENNER ◽  
P. W. NEURATH ◽  
W. D. SELLES ◽  
T. F. NECHELES ◽  
E. S. GELSEMA ◽  
...  
Keyword(s):  
Automated System ◽  
Classification Error ◽  
Correct Classification ◽  
Automated Classification ◽  
Texture Parameters ◽  
Blood Smears ◽  
Definition Of ◽  
Peripheral Blood Smears ◽  
Optimal Set

The development of an automated system for counting and classifying normal and abnormal leukocytes in peripheral blood smears is described. General requirements are discussed and the results of a simulation experiment are presented. A sample of 1572 leukocytes, divided equally among 17 types, was photographed and analyzed using computerized pattern recognition techniques. Various geometrical, color and texture parameters were extracted from the cell images and an optimal set of 20 were used in several computerized classification runs. Training on one-half of the sample and classifying the other half resulted in an over-all correct classification of between 67 and 77% depending on the definition of classification error. When only normal cells are considered, correct classification is obtained for 9l.5% of the cells.

Erythrocyte segmentation for quantification in microscopic images of thin blood smears

2017 ◽  
Vol 32 (4) ◽  
pp. 2847-2856 ◽  
Author(s):  
Salam Shuleenda Devi ◽  
Joyeeta Singha ◽  
Manish Sharma ◽  
Rabul Hussain Laskar
Keyword(s):  
Microscopic Images ◽  
Blood Smears ◽  
Thin Blood Smears

Detecting and segmenting overlapping red blood cells in microscopic images of thin blood smears

2018 ◽  
Author(s):  
Golnaz Moallem ◽  
Hamed Sari-Sarraf ◽  
Mahdieh Poostchi ◽  
Richard J. Maude ◽  
Kamolrat Silamut ◽  
...  
Keyword(s):  
Red Blood Cells ◽  
Blood Cells ◽  
Microscopic Images ◽  
Blood Smears ◽  
Thin Blood Smears

scholarly journals Automated status identification of microscopic images obtained from malaria thin blood smears

2011 ◽  
Author(s):  
Dian Anggraini ◽  
Anto Satriyo Nugroho ◽  
Christian Pratama ◽  
Ismail Ekoprayitno Rozi ◽  
Aulia Arif Iskandar ◽  
...  
Keyword(s):  
Microscopic Images ◽  
Blood Smears ◽  
Thin Blood Smears

Brightfield and fluorescence in-channel staining of thin blood smears generated in a pumpless microfluidic

2021 ◽  
Author(s):  
Kokou S. Dogbevi ◽  
Paul Gordon ◽  
Kimberly L. Branan ◽  
Bryan Khai D. Ngo ◽  
Kevin B. Kiefer ◽  
...  
Keyword(s):  
Peripheral Blood ◽  
Blood Smears ◽  
Peripheral Blood Smears ◽  
Thin Blood Smears

Effective staining of peripheral blood smears which enhances the contrast of intracellular components and biomarkers is essential for the accurate characterization, diagnosis, and monitoring of various diseases such as malaria.

scholarly journals In situ loop-mediated isothermal amplification (LAMP) for identification of Plasmodium species in wide-range thin blood smears

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Muneaki Hashimoto ◽  
Hirokazu Sakamoto ◽  
Yusuke Ido ◽  
Masato Tanaka ◽  
Shouki Yatsushiro ◽  
...  
Keyword(s):  
Plasmodium Species ◽  
Isothermal Amplification ◽  
Loop Mediated Isothermal Amplification ◽  
Blood Smears ◽  
Wide Range ◽  
Thin Blood Smears

Performance of the CellaVision DC-1 digital cell imaging analyser for differential counting and morphological classification of blood cell

2021 ◽  
pp. jclinpath-2021-207863
Author(s):  
Lisa N van der Vorm ◽  
Henriët A Hendriks ◽  
Simone M Smits
Keyword(s):  
Blood Cell ◽  
Peripheral Blood ◽  
Cell Imaging ◽  
Small Satellite ◽  
Morphological Classification ◽  
Clinical Sensitivity ◽  
Blood Smears ◽  
Low Volume ◽  
Peripheral Blood Smears

AimsRecently, a new automated digital cell imaging analyser (Sysmex CellaVision DC-1), intended for use in low-volume and small satellite laboratories, has become available. The purpose of this study was to compare the performance of the DC-1 with the Sysmex DI-60 system and the gold standard, manual microscopy.MethodsWhite blood cell (WBC) differential counts in 100 normal and 100 abnormal peripheral blood smears were compared between the DC-1, the DI-60 and manual microscopy to establish accuracy, within-run imprecision, clinical sensitivity and specificity. Moreover, the agreement between precharacterisation and postcharacterisation of red blood cell (RBC) morphological abnormalities was determined for the DC-1.ResultsWBC preclassification and postclassification results of the DC-1 showed good correlation compared with DI-60 results and manual microscopy. In addition, the within-run SD of the DC-1 was below 1 for all five major WBC classes, indicating good reproducibility. Clinical sensitivity and specificity were, respectively, 96.7%/95.9% compared with the DI-60% and 96.6%/95.3% compared with manual microscopy. The overall agreement on RBC morphology between the precharacterisation and postcharacterisation results ranged from 49% (poikilocytosis) to 100% (hypochromasia, microcytosis and macrocytosis).ConclusionsThe DC-1 has proven to be an accurate digital cell imaging system for differential counting and morphological classification of WBCs and RBCs in peripheral blood smears. It is a compact and easily operated instrument that can offer low-volume and small satellite laboratories the possibilities of readily available blood cell analysis that can be stored and retrieved for consultation with remote locations.

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