Literature DB >> 21831474

Automatic evaluation of cardiac hypertrophy using cardiothoracic area ratio in chest radiograph images.

Muhammad A Hasan1, Seok-Lyong Lee, Deok-Hwan Kim, Myung-Kwan Lim.   

Abstract

To evaluate the cardiac hypertrophy from chest radiograph images, radiologists usually examine the cardiothoracic ratio (frequently called CTR) which is a standard diagnostic index. The CTR is computed by the maximum transverse diameter of the heart shadow divided by the maximum transverse diameter of right and left lung boundaries. In this paper, we present a method to evaluate the cardiac hypertrophy by comparing the area of heart with that of lung, instead of the cardiothoracic ratio to get more desirable diagnostic results. We introduce a new index, a cardiothoracic area ratio (CTAR), which is computed by dividing the area of heart region by the area of lung region of specific interest. We first segment a chest region of interest in a radiograph image and then automatically compute the traditional CTR and the CTAR to evaluate the cardiac hypertrophy. And finally, we provide the visual presentation of those ratios on the chest radiograph image. The experimental results using a set of radiograph images show that the proposed method can be used effectively for determining the cardiac hypertrophy in a real-time diagnostic environment. It provides the higher discrimination power than the CTR to identify hypertrophied hearts by recognizing the heart enlargement. It also can be used together with the traditional CTR as a complementary measure when it is difficult to determine abnormalities by the CTR, reducing the rate of wrong diagnosis.
Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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Year:  2011        PMID: 21831474     DOI: 10.1016/j.cmpb.2011.07.009

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  5 in total

1.  A deep learning-based automatic analysis of cardiovascular borders on chest radiographs of valvular heart disease: development/external validation.

Authors:  Cherry Kim; Gaeun Lee; Hongmin Oh; Gyujun Jeong; Sun Won Kim; Eun Ju Chun; Young-Hak Kim; June-Goo Lee; Dong Hyun Yang
Journal:  Eur Radiol       Date:  2021-10-13       Impact factor: 5.315

2.  CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases.

Authors:  Abbas Jafar; Muhammad Talha Hameed; Nadeem Akram; Umer Waqas; Hyung Seok Kim; Rizwan Ali Naqvi
Journal:  J Pers Med       Date:  2022-06-17

Review 3.  A review on lung boundary detection in chest X-rays.

Authors:  Sema Candemir; Sameer Antani
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-02-07       Impact factor: 2.924

4.  Comparison of radiological findings of chest x-ray with echocardiography in determination of the heart size.

Authors:  Ali Biharas Monfared; Shahnaz Agha Farajollah; Fahimeh Sabour; Roya Farzanegan; Shahram Taghdisi
Journal:  Iran Red Crescent Med J       Date:  2015-01-17       Impact factor: 0.611

5.  Artificial Intelligence-Based Diagnosis of Cardiac and Related Diseases.

Authors:  Muhammad Arsalan; Muhammad Owais; Tahir Mahmood; Jiho Choi; Kang Ryoung Park
Journal:  J Clin Med       Date:  2020-03-23       Impact factor: 4.241

  5 in total

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