Fast fully automatic heart fat segmentation in computed tomography datasets

Victor Hugo C de Albuquerque; Douglas de A Rodrigues; Roberto F Ivo; Solon A Peixoto; Tao Han; Wanqing Wu; Pedro P Rebouças Filho

doi:10.1016/j.compmedimag.2019.101674

Fast fully automatic heart fat segmentation in computed tomography datasets

Comput Med Imaging Graph. 2020 Mar:80:101674. doi: 10.1016/j.compmedimag.2019.101674. Epub 2019 Dec 6.

Authors

Victor Hugo C de Albuquerque¹, Douglas de A Rodrigues², Roberto F Ivo³, Solon A Peixoto⁴, Tao Han⁵, Wanqing Wu⁶, Pedro P Rebouças Filho⁷

Affiliations

¹ DGUT-CNAM Institute, Dongguan University of Technology, Dongguan 523106, China; Universidade de Fortaleza, Fortaleza-CE, Brazil. Electronic address: victor.albuquerque@ieee.org.
² Programa de Pós-Graduação em Engenharia de Teleinformática - Universidade Federal do Ceará, Fortaleza-CE, Brazil; Laboratório de Processamento de Imagens, Sinais e Computação Aplicada (LAPISCO), Instituto Federal do Ceará, Fortaleza-CE, Brazil. Electronic address: douglas@lapisco.ifce.edu.br.
³ Programa de Pós-Graduação em Engenharia de Teleinformática - Universidade Federal do Ceará, Fortaleza-CE, Brazil; Laboratório de Processamento de Imagens, Sinais e Computação Aplicada (LAPISCO), Instituto Federal do Ceará, Fortaleza-CE, Brazil. Electronic address: robertoivo@lapisco.ifce.edu.br.
⁴ Programa de Pós-Graduação em Engenharia de Teleinformática - Universidade Federal do Ceará, Fortaleza-CE, Brazil; Laboratório de Processamento de Imagens, Sinais e Computação Aplicada (LAPISCO), Instituto Federal do Ceará, Fortaleza-CE, Brazil. Electronic address: solon.alves@lapisco.ifce.edu.br.
⁵ DGUT-CNAM Institute, Dongguan University of Technology, Dongguan 523106, China. Electronic address: hant@dgut.edu.cn.
⁶ School of Biomedical Engineering, Sun Yat-Sen University, Guanzhou 510275, PR China. Electronic address: wuwanqing@mail.sysu.edu.cn.
⁷ Programa de Pós-Graduação em Engenharia de Teleinformática - Universidade Federal do Ceará, Fortaleza-CE, Brazil; Laboratório de Processamento de Imagens, Sinais e Computação Aplicada (LAPISCO), Instituto Federal do Ceará, Fortaleza-CE, Brazil. Electronic address: pedrosarf@ifce.edu.br.

PMID: 31884225
DOI: 10.1016/j.compmedimag.2019.101674

Abstract

Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new approach to the segmentation of cardiac fat from Computed Tomography (CT) images. The study employs a clustering algorithm called Floor of Log (FoL). The advantage of this method is the significant drop in segmentation time. Support Vector Machine was used to learn the best FoL algorithm parameter as well as mathematical morphology techniques for noise removal. The time to segment cardiac fat on a CT is only 2.01 s on average. In contrast, literature works require more than one hour to perform segmentation. Therefore, this job is one of the fastest to segment an exam completely. The value of the Accuracy metric was 93.45% and Specificity of 95.52%. The proposed approach is automatic and requires less computational effort. With these results, the use of this approach for the segmentation of cardiac fat proves to be efficient, besides having good application times. Therefore, it has the potential to be a medical diagnostic aid tool. Consequently, it is possible to help experts achieve faster and more accurate results.

Keywords: Cardiac fat segmentation; Digital image processing; Floor of log; Heart.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adipose Tissue / diagnostic imaging*
Cardiovascular Diseases / diagnostic imaging*
Humans
Imaging, Three-Dimensional
Radiographic Image Interpretation, Computer-Assisted / methods*
Sensitivity and Specificity
Support Vector Machine*
Tomography, X-Ray Computed*