GK News 2 - шаблон joomla Видео

Banner1

Log in
Powered by Spearhead Software Labs Joomla Facebook Like Button
27 August 2017
Adel Hamed Elbaih1, Eman Adel Elzeky1, Islam Elshaboury1, Mohamed Oraby2 1) Department of Emergency Medicine, Faculty of Medicine, Suez Canal University, Ismailia, Egypt. 2) Department of
10 May 2017
Naoual Benhmidou1, Fadoua Rais1, Fadila
10 May 2017
Khadija Bellahammou1, Asmaa Lakhdissi1,
10 May 2017
Khaled Moursy Salama1, Monira T.

BREAST DENSITY EVALUATION: A COMPARISON BETWEEN ASSESSMENT BY A RADIOLOGIST AND THE COMPUTER-ASSISTED THRESHOLD TECHNIQUE

freedigitalsphotos.com freedigitalsphotos.com

George Baitchev1, Ivan Ivanov2, Ivan Inkov1, Emilia Zlateva4, Zdravko Kamenov3, George Dimitrov3

1) Department Of Thoracic Surgery, Military Medical Academy, Sofia, Bulgaria;

2) Department Of Medicodiagnostic Research, Medical University of Pleven, Bulgaria;

3) Medical University of Sofia, Bulgaria;

4) Department Of Radiology, Hospital “Avis Medica”, Pleven, Bulgaria.

Disclosure: The author has declared no conflicts of interest.

Received: 06.08.15 Accepted: 03.09.15

1486385429 Quote  doi: 10.5455/ijsm.20150903063859

pdf icon  Fulltext PDF Download

export icona  EndNote/RefWorks

statistics market icon32  Article Statistics

Abstract:

Traditionally, mammographic density (MD) of the breast has been assessed by a radiologist visually. This subjective evaluation requires significant experience to distinguish the relative proportions of the fibrous connective tissue and adipose tissue in the mammary gland correctly.
The aim of this study is to compare the capabilities of the different methods (visual and computer-assisted) for assessing mammographic density.
Our sample in this study consists of 66 patients with digital mammography. The mammographic density has been evaluated using the four-grade scale of the American College of Radiology (ACR); visually, visually using an analog scale and semi-automated using UTHSCSA Image Tool 3.0, Image J and Adobe Photoshop CS6 software.
The average mammographic density calculated using the different methods is as follows: 34.8% (from 10% to 70%); 32.1% (from 10% to 60%); 23% (from 0% to 70.9%); 22.7% (from 2.5% to 78.1%) and 22.5% (from 1.5% to 72.4%).
There is a strong correlation between the results obtained visually and those calculated using a computer-assisted measurement (p< 0.0001). A strong correlation was found also between the results acquired using the different semi-automated programs (p< 0.0001).
Precise measurement of mammographic density is of great importance for the mammographic screening and evaluation of breast cancer risk. The semi-automated methods, used for this purpose are objective, accessible and reproducible tools and have some advantages over the subjective visual assessment.

Keywords: imagej breast density, early signs of breast cancer, breast density photoshop, breast density measurement, breast density classification


How to Cite this Article

Bibliography

Baitchev, G., Ivanov, I., Inkov, I., Zlateva, E., Kamenov, Z. and Dimitrov, G. (2015) ‘Breast density evaluation: A comparison between assessment by a radiologist and the computer-assisted threshold technique’, International Journal of Surgery and Medicine, 1(2), pp. 48–52. doi: 10.5455/ijsm.20150903063859.

Citations, Quotes & Annotations

Baitchev, G., Ivanov, I., Inkov, I., Zlateva, E., Kamenov, Z. and Dimitrov, G. (2015) ‘Breast density evaluation: A comparison between assessment by a radiologist and the computer-assisted threshold technique’, International Journal of Surgery and Medicine, 1(2), pp. 48–52. doi: 10.5455/ijsm.20150903063859.
(Baitchev, Ivanov, et al., 2015)
 

Cited by:

google scholar

 
Last modified onThursday, 25 May 2017 10:47

Leave a comment

Make sure you enter the (*) required information where indicated. HTML code is not allowed.

 09 May 2017
  1748  
Fazlý Yanýk1; Gonul Sagiroglu2; Elif Copuroglu2; Yekta Altemur Karamustafaoglu1
 1748 
Fazlý Yanýk1; Gonul Sagiroglu2; Elif Copuroglu2; Yekta Altemur Karamustafaoglu1
09 May 2017
 27 August 2017
  1923  
Adel Hamed Elbaih1, Eman Adel Elzeky1, Islam Elshaboury1, Mohamed Oraby2
 1923 
Adel Hamed Elbaih1, Eman Adel Elzeky1, Islam Elshaboury1, Mohamed Oraby2
27 August 2017
 28 March 2017
  1324  
Zuvdija Cecunjanin1, Amina Selimovic2, Selma Milisic1, Ermina Mujicic3
 1324 
Zuvdija Cecunjanin1, Amina Selimovic2, Selma Milisic1, Ermina Mujicic3
28 March 2017
 28 March 2017
  1576  
Michele Bisaccia1, Luigi Piscitelli1, Giovanni Colleruoli1, Giuseppe Rinonapoli1, Cristina Ibáñez Vicente1, Gabriele Falzarano2, Antonio Medici2, Luigi Meccariello3, Olga Bisaccia4, ...
 1576 
Michele Bisaccia1, Luigi Piscitelli1, Giovanni Colleruoli1, Giuseppe Rinonapoli1, Cristina Ibáñez Vicente1, Gabriele Falzarano2, ...
28 March 2017
 26 March 2017
  1332  
Roman Romansky1, George Baytchev2, Ivan Inkov2, Stefan Komitski1
 1332 
Roman Romansky1, George Baytchev2, Ivan Inkov2, Stefan Komitski1
26 March 2017
 26 March 2017
  1047  
Andrea Cappiello1, Verdiana Stano2, Michele Bisaccia1, Luigi Meccariello3, Gabriele Falzarano4, Antonio Medici4, Marco Pellegrino1, Olga Bisaccia5, Giuseppe Rinonapoli1, Auro Caraffa1.
 1047 
Andrea Cappiello1, Verdiana Stano2, Michele Bisaccia1, Luigi Meccariello3, Gabriele Falzarano4, Antonio Medici4, Marco Pellegrino1, Olga Bisaccia5, ...
26 March 2017
 26 March 2017
  958  
Ventsislav Mutafchiyski1, Georgi Popivanov1, Dimitar Penchev1, Albena Fakirova2, Ivan Inkov3, Rumen Popov4
 958 
Ventsislav Mutafchiyski1, Georgi Popivanov1, Dimitar Penchev1, Albena Fakirova2, Ivan Inkov3, Rumen Popov4
26 March 2017
 26 March 2017
  1355  
Thulasikumar Ganapathy1, Marunraj Gnanasekaran2, Aravind Moorthy3
 1355 
Thulasikumar Ganapathy1, Marunraj Gnanasekaran2, Aravind Moorthy3
26 March 2017