A hybrid energy model for region based curve evolution – Application to CTA coronary segmentation. (June 2017)

• Record Type: Journal Article
• Authors: Jawaid, Muhammad Moazzam; Rajani, Ronak; Liatsis, Panos; Reyes-Aldasoro, Constantino Carlos; Slabaugh, Greg
• Is Part Of: Computer methods and programs in biomedicine. Volume 144(2017)
• Page Start: 189
• Page End: 202
• Publication Date: 2017-06
• Subjects: Computed tomography images -- Coronary segmentation -- Hybrid image energy -- Level set method; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.03.020
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Localized Energy for Non-Homogeneous Image Data Global Discontinuity Map of CTA using Probabilistic Model Hybrid Energy for Complex Coronary Segmentation Rotterdam Coronary Artery CTA Dataset Manual Annotaion-Inter Observer Variability. Abstract: Background and Objective: State-of-the-art medical imaging techniques have enabled non-invasive imaging of the internal organs. However, high volumes of imaging data make manual interpretation and delineation of abnormalities cumbersome for clinicians. These challenges have driven intensive research into efficient medical image segmentation. In this work, we propose a hybrid region-based energy formulation for effective segmentation in computed tomography angiography (CTA) imagery. Methods: The proposed hybrid energy couples an intensity-based local term with an efficient discontinuity-based global model of the image for optimal segmentation. The segmentation is achieved using a level set formulation due to the computational robustness. After validating the statistical significance of the hybrid energy, we applied the proposed model to solve an important clinical problem of 3D coronary segmentation. An improved seed detection method is used to initialize the level set evolution. Moreover, we employed an auto-correction feature that captures the emerging peripheries during the curve evolution for completeness of the coronary tree. Results: We evaluated the segmentation accuracy of the proposed energy model against the existing techniques in two stages. Qualitative and quantitative results demonstrate the effectiveness of the proposed framework with a consistent mean sensitivity and specificity measures of 80% across the CTA data. Moreover, a high degree of agreement with respect to the inter-observer differences justifies the generalization of the proposed method. Conclusions: The proposed method is effective to segment the coronary tree from the CTA volume based on hybrid image based energy, which can improve the clinicians ability to detect arterial abnormalities.