A Preliminary Approach for the Segmentation of Mitral Valve Regurgitation Jet in Doppler Ecocardiography Images

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Costa, E (Author) Other; Martins, N (Author) Other; Sultan, MS (Author) Other; Veiga, D (Author) Other; Ferreira, MJ (Author) Other; Mattos, S (Author) Other; Coimbra, M (Author) FCUP

Conference proceedings International

Title: Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - Volume 2: BIOIMAGING, Porto, Portugal, February 21-23, 2017.

Pages: 47-54

Indexing

ISI Web of Knowledge - 2 Citations

Other information

Authenticus ID: P-00M-S8E

DOI: 10.5220/0006248900470054

Abstract (EN): Rheumatic Fever and Rheumatic Heart Disease remain a major burden among children in developing countries. Echocardiography with colour flow Doppler is key to early diagnosis. However, the technique requires time and experienced operators, which are scarce resources in the affected areas. Automatic segmentation of colour Doppler regurgitation jets could, potentially, reduce the cost of screening, and spread diagnostic accessibility for a larger number of patients. Ultrasound processing is very challenging due to speckle noise and similarity of representation of all kinds of tissue. Region-based active contours are suitable tools for the segmentation in cases of intensity heterogeneities, which makes them interesting algorithms for left atrium segmentation. HSV colour space describes colour in terms of hues and saturation, which may facilitate the translation of medical interpretation of the Doppler pseudo-colour into mathematical expression for colour segmentation. A total of 979 frames from 20 sequences were manually annotated and used to validate the proposed pipeline. Overall, the results for colour pattern segmentation are promising (sensitivity=0.91 false detection rate=0.10), but further developments are required for the atrium segmentation (sensitivity=0.80, false detection rate=0.28).

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 8