Fast fully automatic heart fat segmentation in computed tomography datasets

• Published in: Computerized medical imaging and graphics 2020-03, Vol.80, p.101674-101674, Article 101674
• Main Authors: de Albuquerque, Victor Hugo C., de A. Rodrigues, Douglas, Ivo, Roberto F., Peixoto, Solon A., Han, Tao, Wu, Wanqing, Rebouças Filho, Pedro P.
• Format: Article
• Language: English
• Subjects: Algorithms; Cardiac fat segmentation; Cardiovascular diseases; Clustering; Computed tomography; Computer applications; Coronary artery disease; Diagnostic software; Diagnostic systems; Digital image processing; Floor of log; Heart; Heart diseases; Image segmentation; Mathematical morphology; Population studies; Support vector machines
• ISSN: 0895-6111; 1879-0771
• DOI: 10.1016/j.compmedimag.2019.101674

•Thorough assessment of Floor of Log method when coping with the task of heart diagnosis through CT images.•Comparison of the proposed method with other current approaches.•Floor of Log method has outperformed the other models in terms of efficiency and effectiveness criteria. 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.