| Full text | |
| Author(s): |
Baracho, Salety Ferreira
;
Leal Pinheiro, Daniel Jose Lins
;
de Melo, Vinicius Veloso
;
Coelho, Regina Celia
;
IEEE
Total Authors: 5
|
| Document type: | Journal article |
| Source: | 2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN); v. N/A, p. 7-pg., 2016-01-01. |
| Abstract | |
Echocardiographic exams allow the observation and extraction of measures related to cardiac structures. In the longitudinal parasternal view, these measures include the left ventricle end-diastolic and end-systolic diameters, end-diastolic interventricular septum thickness (IVSd), and end-diastolic left ventricle posterior wall thickness (LVPWd). Among these measures, the IVSd is important for diagnosing pathologies like hypertrophic cardiomyopathy, aneurysms, abnormal movement and structural faults. This work presents a hybrid neural network system to segment interventricular septum in echocardiographic images of parasternal longitudinal view. The hybrid system developed here consist of a Self-Organizing Map and a Multilayer Perceptron (MLP) neural network. The approach has two phases: clustering and classification. First, the Self-Organizing Map clusters image patches that are previously labeled as Septum and Non-septum. Later, an MLP is trained with information generated by the map. The MLP is then employed to classify patches of a new image resulting in a mask that indicates the probable septum regions. To validate the results, we did a semi-automatic extraction of septum thickness. The average error between the septum thicknesses obtained by the algorithm and the one manually traced was 0.5477mm +/- 0.5277mm. Future recommendations are presented to improve the hybrid system performance to get more accurate results. (AU) | |
| FAPESP's process: | 16/04946-4 - Genetic and Evolutionary Computation Conference (GECCO 2016) |
| Grantee: | Vinícius Veloso de Melo |
| Support Opportunities: | Research Grants - Meeting - Abroad |