Investigation of 3D and 4D Feature Extraction from Echocardiography Images Using Local Phase Based Method

Ultrasound images are difficult to segment because of their noisy and low contrast nature which makes them 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 local phase based technique provides far better results than intensity based methods for ultrasound images. In this study, we adopt local phase based feature asymmetry (FA) measure using monogenic signal for 3-D and 4-D feature extraction. This paper presents our work on: (1) comparison between intensity gradient based feature detectors and phase based feature detector, (2) formulating a 4-D version of FA measure and comparing its results with 3-D FA measure, and (3) comparing quadrature pair of filters in terms of feature detection performance.

[1]  A.V. Oppenheim,et al.  The importance of phase in signals , 1980, Proceedings of the IEEE.

[2]  Kashif Rajpoot,et al.  Feature detection from echocardiography images using local phase information , 2008 .

[3]  Kashif Rajpoot,et al.  Local-phase based 3D boundary detection using monogenic signal and its application to real-time 3-D echocardiography images , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  Ahror Belaid,et al.  Phase based level set segmentation of ultrasound images , 2009, 2009 9th International Conference on Information Technology and Applications in Biomedicine.

[5]  Michael Brady,et al.  On the Choice of Band-Pass Quadrature Filters , 2004, Journal of Mathematical Imaging and Vision.

[6]  J. Alison Noble,et al.  2D+T acoustic boundary detection in echocardiography , 2000, Medical Image Anal..

[7]  Michael Felsberg,et al.  The monogenic signal , 2001, IEEE Trans. Signal Process..

[8]  J. Alison Noble,et al.  2D+T Acoustic Boundary Detection in Echocardiography , 1998, MICCAI.

[9]  J. Alison Noble,et al.  Ultrasound image segmentation: a survey , 2006, IEEE Transactions on Medical Imaging.

[10]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Peter Kovesi,et al.  Symmetry and Asymmetry from Local Phase , 1997 .

[12]  Robyn A. Owens,et al.  Feature detection from local energy , 1987, Pattern Recognit. Lett..