Automatic Computation of Left Ventricular Volume Changes Over a Cardiac Cycle from Echocardiography Images by Nonlinear Dimensionality Reduction

• Published in: Journal of digital imaging 2015-02, Vol.28 (1), p.91-98
• Main Authors: Alizadeh Sani, Zahra, Shalbaf, Ahmad, Behnam, Hamid, Shalbaf, Reza
• Format: Article
• Language: English
• Subjects: Algorithms; Automation; Cardiovascular Physiological Phenomena; Diseases; Echocardiography; Heart Ventricles - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted - methods; Imaging; Medicine; Medicine & Public Health; Nonlinearity; Organ Size; Pattern Recognition, Automated - methods; Radiology; Reduction; Reproducibility of Results; Segmentation; Ultrasonography
• ISSN: 0897-1889; 1618-727X
• DOI: 10.1007/s10278-014-9722-z

Curve of left ventricular (LV) volume changes throughout the cardiac cycle is a fundamental parameter for clinical evaluation of various cardiovascular diseases. Currently, this evaluation is often performed manually which is tedious and time consuming and suffers from significant interobserver and intraobserver variability. This paper introduces a new automatic method, based on nonlinear dimensionality reduction (NLDR) for extracting the curve of the LV volume changes over a cardiac cycle from two-dimensional (2-D) echocardiography images. Isometric feature mapping (Isomap) is one of the most popular NLDR algorithms. In this study, a modified version of Isomap algorithm, where image to image distance metric is computed using nonrigid registration, is applied on 2-D echocardiography images of one cycle of heart. Using this approach, the nonlinear information of these images is embedded in a 2-D manifold and each image is characterized by a symbol on the constructed manifold. This new representation visualizes the relationship between these images based on LV volume changes and allows extracting the curve of the LV volume changes automatically. Our method in comparison to the traditional segmentation algorithms does not need any LV myocardial segmentation and tracking, particularly difficult in the echocardiography images. Moreover, a large data set under various diseases for training is not required. The results obtained by our method are quantitatively evaluated to those obtained manually by the highly experienced echocardiographer on ten healthy volunteers and six patients which depict the usefulness of the presented method.