Spatial and spatio-temporal feature extraction from 4D echocardiography images. (1st September 2015)

• Record Type: Journal Article
• Authors: Awan, Ruqayya; Rajpoot, Kashif
• Is Part Of: Computers in biology and medicine. Volume 64(2015)
• Page Start: 138
• Page End: 147
• Publication Date: 2015-09-01
• Subjects: 4-D echocardiography -- Feature extraction -- Band-pass filter -- Local phase -- Feature asymmetry; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.06.017
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: Ultrasound images are difficult to segment because of their noisy and low contrast nature which makes it challenging to extract the important features. Typical intensity-gradient based approaches are not suitable for these low contrast images while it has been shown that the local phase based technique provides better results than intensity based methods for ultrasound images. The spatial feature extraction methods ignore the continuity in the heart cycle and may also capture spurious features. It is believed that the spurious features (noise) that are not consistent along the frames can be excluded by considering the temporal information. Methods: In this paper, we present a local phase based 4D (3D+time) feature asymmetry (FA) measure using the monogenic signal. We have investigated the spatio-temporal feature extraction to explore the effect of adding time information in the feature extraction process. Results: To evaluate the impact of time dimension, the results of 4D based feature extraction are compared with the results of 3D based feature extraction which shows the favorable 4D feature extraction results when temporal resolution is good. The paper compares the band-pass filters (difference of Gaussian, Cauchy and Gaussian derivative) in terms of their feature extraction performance. Moreover, the feature extraction is further evaluated quantitatively by left ventricle segmentation using the extracted features. Conclusions: The results demonstrate that the spatio-temporal feature extraction is promising in frames with good temporal resolution.