scholarly journals Leukocyte Detection in Blood Smear Image Based on Improved YOLOv3

2020 ◽  
Keyword(s):  
Blood Smear ◽  
Blood Smear Image

Blood Smear Image Based Malaria Parasite and Infected-Erythrocyte Detection and Segmentation

2015 ◽  
Vol 39 (10) ◽  
Author(s):  
Meng-Hsiun Tsai ◽  
Shyr-Shen Yu ◽  
Yung-Kuan Chan ◽  
Chun-Chu Jen
Keyword(s):  
Malaria Parasite ◽  
Blood Smear ◽  
Infected Erythrocyte ◽  
Blood Smear Image

scholarly journals IDENTIFICATION OF MEGALOBLASTIC ANEMIA CELLS THROUGH THE USE OF IMAGE PROCESSING TECHNIQUES

2018 ◽  
Vol 4 (3) ◽  
pp. 1-5 ◽  
Author(s):  
Asaad Babker ◽  
Vyacheslav Lyashenko
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Blood Smear ◽  
Color Image ◽  
Megaloblastic Anemia ◽  
Cell Structure ◽  
Color Space ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Blood Smear Image

Objective: Our aim is to show the possibility of using different image processing techniques for blood smear analysis. Also our aim is to determine the sequence of image processing techniques to identify megaloblastic anemia cells. Methods: We consider blood smear image. We use a variety of image processing techniques to identify megaloblastic anemia cells. Among these methods, we distinguish the modification of the color space and the use of wavelets. Results: We developed a sequence of image processing techniques for blood smear image analysis and megaloblastic anemia cells identification. As a characteristic feature for megaloblastic anemia cells identification, we consider neutrophil image structure. We also use the morphological methods of image analysis in order to reveal the nuclear lobes in neutrophil structure. Conclusion: We can identify the megaloblastic anemia cells. To do this, we use the following sequence of blood smear image processing: color image modification, change of the image contrast, use of wavelets and morphological analysis of the cell structure. 

Enhancement of low quality blood smear image using contrast and edge corrections

2020 ◽  
Vol 34 (4) ◽  
pp. 354
Author(s):  
Puji Budi Setia Asih ◽  
Ismail Ekoprayitno Rozi ◽  
Umi Salamah ◽  
Anto Satriyo Nugroho ◽  
Agus Zainal Arifin ◽  
...  
Keyword(s):  
Blood Smear ◽  
Blood Smear Image

Enhancement of low quality blood smear image using contrast and edge corrections

2020 ◽  
Vol 34 (4) ◽  
pp. 354
Author(s):  
Umi Salamah ◽  
Riyanarto Sarno ◽  
Agus Zainal Arifin ◽  
Anto Satriyo Nugroho ◽  
Ismail Ekoprayitno Rozi ◽  
...  
Keyword(s):  
Blood Smear ◽  
Blood Smear Image

Comparative Analysis of Morphological Techniques for Malaria Detection

2018 ◽  
Vol 13 (4) ◽  
pp. 49-65 ◽  
Author(s):  
P.A Pattanaik ◽  
Tripti Swarnkar
Keyword(s):  
Blood Smear ◽  
Visual Detection ◽  
Specific Treatment ◽  
Qualitative Assessment ◽  
Patient Specific ◽  
Histogram Method ◽  
Fast Detection ◽  
Plasmodium Parasite ◽  
Genus Plasmodium ◽  
Blood Smear Image

The genus Plasmodium parasite causes malaria infection. Fast detection and accurate diagnosis of infected and non-infected malaria erythrocytes from microscopic blood smear images open the door to effective assistance and patient-specific treatment. This article presents a comparative experimental analysis of visual detection of infected erythrocytes malaria parasites via the most efficient morphological techniques from gold standard blood smear images. In this article, twelve different widely-used morphological algorithms are evaluated followed by a random forest classifier for detecting infected erythrocytes based on their performance vis-a-vis microscopic blood smear images. Accurate detection of infected malaria erythrocytes is done using the two ranges of blood smear image datasets with varying malaria parasite density. Finally, compared to 11 morphological techniques in terms of accuracy, sensitivity, and specificity, the qualitative assessment of experimental results unveil that the Histogram method offers more meaningful and impactful findings.

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