scholarly journals Quantification of Liver Fibrosis—A Comparative Study

2020 ◽  
Vol 10 (2) ◽  
pp. 447 ◽  
Author(s):  
Alexandros Arjmand ◽  
Markos G. Tsipouras ◽  
Alexandros T. Tzallas ◽  
Roberta Forlano ◽  
Pinelopi Manousou ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
Liver Disease ◽  
Liver Fibrosis ◽  
Digital Image Analysis ◽  
Common Cause ◽  
Image Processing Techniques ◽  
Liver Biopsies ◽  
Gray Areas ◽  
Processing Techniques

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic.

Image processing techniques used for dental x-ray image analysis

2008 ◽  
Author(s):  
Stefan Oprea ◽  
Costin Marinescu ◽  
Ioan Lita ◽  
Mariana Jurianu ◽  
Daniel Alexandru Visan ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Analysis ◽  
X Ray ◽  
Image Processing Techniques ◽  
Processing Techniques

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. 

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