scholarly journals Automated Analysis of Retinal Images

1991 ◽  
Author(s):  
Piotr Jasiobedzki ◽  
Chris J. Taylor
Keyword(s):  
Automated Analysis ◽  
Retinal Images

Automated Analysis of Retinal Vascular Tortuosity on Color Retinal Images

2010 ◽  
Vol 36 (2) ◽  
pp. 689-697 ◽  
Author(s):  
Alauddin Bhuiyan ◽  
Baikunth Nath ◽  
Kotagiri Ramamohanarao ◽  
Ryo Kawasaki ◽  
Tien Yin Wong
Keyword(s):  
Automated Analysis ◽  
Retinal Images ◽  
Vascular Tortuosity ◽  
Retinal Vascular Tortuosity

scholarly journals Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy

2013 ◽  
Vol 131 (3) ◽  
pp. 351 ◽  
Author(s):  
Michael D. Abràmoff ◽  
James C. Folk ◽  
Dennis P. Han ◽  
Jonathan D. Walker ◽  
David F. Williams ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Automated Analysis ◽  
Retinal Images

Automated analysis of retinal images

1993 ◽  
Vol 11 (3) ◽  
pp. 139-144 ◽  
Author(s):  
Piotr Jasiobedzki ◽  
Chris J Taylor ◽  
John NH Brunt
Keyword(s):  
Automated Analysis ◽  
Retinal Images

Automated Analysis of Retinal Images

BMVC91 ◽  
1991 ◽  
pp. 275-283
Author(s):  
Piotr Jasiobedzki ◽  
Chris J. Taylor
Keyword(s):  
Automated Analysis ◽  
Retinal Images

DETECTION OF BLOOD VESSELS FROM RETINAL IMAGES USING WATERSHED TRANSFORMATION

2009 ◽  
Vol 09 (04) ◽  
pp. 633-642 ◽  
Author(s):  
A. BESSAID ◽  
A. FEROUI ◽  
M. MESSADI
Keyword(s):  
Blood Vessels ◽  
Automated Analysis ◽  
Retinal Images ◽  
Fundus Images ◽  
Vascular Tree ◽  
Important Indicator ◽  
Watershed Transformation ◽  
Low Contrast ◽  
Retinal Blood Vessels ◽  
Diagnostic Step

Automated analysis and interpretation of retinal images has become an incontournable diagnostic step in ophthalmology. Retinal blood vessels morphology can be an important indicator for diseases such as diabetic retinopathy; and their detection also serves for image registration. This paper presents a method based on mathematical morphology for extraction of vascular tree in color retinal image with low contrast. It consists in contrast enhancement and application of watershed transformation in order to segment blood vessels in digital fundus images.

Development and study of a method for cell separation during white blood cell segmentation on images of bone marrow preparations in information and measurement systems for diagnostics of acute leukemia

2020 ◽  
pp. 68-72
Author(s):  
V.G. Nikitaev ◽  
A.N. Pronichev ◽  
V.V. Dmitrieva ◽  
E.V. Polyakov ◽  
A.D. Samsonova ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Blood Cell ◽  
Acute Leukemia ◽  
White Blood Cell ◽  
Cell Separation ◽  
Large Scale ◽  
Bone Marrow Cells ◽  
Automated Analysis ◽  
Cell Segmentation ◽  
Measurement Systems

The issues of using of information and measurement systems based on processing of digital images of microscopic preparations for solving large-scale tasks of automating the diagnosis of acute leukemia are considered. The high density of leukocyte cells in the preparation (hypercellularity) is a feature of microscopic images of bone marrow preparations. It causes the proximity of cells to eachother and their contact with the formation of conglomerates. Measuring of the characteristics of bone marrow cells in such conditions leads to unacceptable errors (more than 50%). The work is devoted to segmentation of contiguous cells in images of bone marrow preparations. A method of cell separation during white blood cell segmentation on images of bone marrow preparations under conditions of hypercellularity of the preparation has been developed. The peculiarity of the proposed method is the use of an approach to segmentation of cell images based on the watershed method with markers. Key stages of the method: the formation of initial markers and builds the lines of watershed, a threshold binarization, shading inside the outline. The parameters of the separation of contiguous cells are determined. The experiment confirmed the effectiveness of the proposed method. The relative segmentation error was 5 %. The use of the proposed method in information and measurement systems of computer microscopy for automated analysis of bone marrow preparations will help to improve the accuracy of diagnosis of acute leukemia.

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