Left ventricle Hermite-based segmentation. (1st August 2017)

• Record Type: Journal Article
• Authors: Olveres, Jimena; Nava, Rodrigo; Escalante-Ramírez, Boris; Vallejo, Enrique; Kybic, Jan
• Is Part Of: Computers in biology and medicine. Volume 87(2017)
• Page Start: 236
• Page End: 249
• Publication Date: 2017-08-01
• Subjects: Active shape models -- Level sets -- Steered Hermite transform -- Left ventricle segmentation -- Local binary patterns -- Ray Feature error; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2017.05.025
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: In recent years, computed tomography (CT) has become a standard technique in cardiac imaging because it provides detailed information that may facilitate the diagnosis of the conditions that interfere with correct heart function. However, CT-based cardiac diagnosis requires manual segmentation of heart cavities, which is a difficult and time-consuming task. Thus, in this paper, we propose a novel technique to segment endocardium and epicardium boundaries based on a 2D approach. The proposal computes relevant information of the left ventricle and its adjacent structures using the Hermite transform. The novelty of the work is that the information is combined with active shape models and level sets to improve the segmentation. Our database consists of mid-third slices selected from 28 volumes manually segmented by expert physicians. The segmentation is assessed using Dice coefficient and Hausdorff distance. In addition, we introduce a novel metric called Ray Feature error to evaluate our method. The results show that the proposal accurately discriminates cardiac tissue. Thus, it may be a useful tool for supporting heart disease diagnosis and tailoring treatments. Highlights: Active shape models and fast level sets performance improves with the addition of Hermite coefficients information. Computed Tomography left ventricle segmentation techniques are compared through entire cardiac cycle, on a 2D basis. Ray Feature error is introduced as a metric to evaluate segmentation performance error.