A Robust and Efficient Segmentation Method Applied for Cardiac Left Ventricle with Abnormal Shapes
Segmentation of left ventricle (LV) from cardiac
ultrasound images provides a quantitative functional analysis of the
heart to diagnose disease. Active Shape Model (ASM) is widely used
for LV segmentation, but it suffers from the drawback that
initialization of the shape model is not sufficiently close to the target,
especially when dealing with abnormal shapes in disease. In this work,
a two-step framework is improved to achieve a fast and efficient LV
segmentation. First, a robust and efficient detection based on Hough
forest localizes cardiac feature points. Such feature points are used to
predict the initial fitting of the LV shape model. Second, ASM is
applied to further fit the LV shape model to the cardiac ultrasound
image. With the robust initialization, ASM is able to achieve more
accurate segmentation. The performance of the proposed method is
evaluated on a dataset of 810 cardiac ultrasound images that are mostly
abnormal shapes. This proposed method is compared with several
combinations of ASM and existing initialization methods. Our
experiment results demonstrate that accuracy of the proposed method
for feature point detection for initialization was 40% higher than the
existing methods. Moreover, the proposed method significantly
reduces the number of necessary ASM fitting loops and thus speeds up
the whole segmentation process. Therefore, the proposed method is
able to achieve more accurate and efficient segmentation results and is
applicable to unusual shapes of heart with cardiac diseases, such as left
atrial enlargement.
[1] C. Corsi and G. Saracino, “Left ventricular volume estimation for
real-time three-dimensional echocardiography,” IEEE Trans. Med. Imag.,
vol. 21, no. 9, pp. 1202–1208, Sep. 2002.
[2] T. McInerney, “Deformable models in medical image analysis: a survey,”
Med. Image Anal., vol. 1, no. 2, pp. 91–108, 1996.
[3] B. Georgescu, “Databased-guided segmentation of anatomical structures
with complex appearance,” Proc. Conf. CVPR, pp. 429–436, 2005.
[4] T. Cootes, “Active shape models—Their training and application,”
Comput. Vis. Image Understand., vol. 61, no. 1, pp. 38–59, Jan. 1995. [5] J. Gall, "Hough forests for object detection, tracking, and action
recognition," IEEE Trans. on PAMI., vol. 33, no. 11, pp. 2188–2202,
2011.
[6] P. Viola and M. Jones, “Robust real-time face detection,” Int. J. Comput.
Vis., vol. 57, no. 2, pp. 137–154, 2004.
[1] C. Corsi and G. Saracino, “Left ventricular volume estimation for
real-time three-dimensional echocardiography,” IEEE Trans. Med. Imag.,
vol. 21, no. 9, pp. 1202–1208, Sep. 2002.
[2] T. McInerney, “Deformable models in medical image analysis: a survey,”
Med. Image Anal., vol. 1, no. 2, pp. 91–108, 1996.
[3] B. Georgescu, “Databased-guided segmentation of anatomical structures
with complex appearance,” Proc. Conf. CVPR, pp. 429–436, 2005.
[4] T. Cootes, “Active shape models—Their training and application,”
Comput. Vis. Image Understand., vol. 61, no. 1, pp. 38–59, Jan. 1995. [5] J. Gall, "Hough forests for object detection, tracking, and action
recognition," IEEE Trans. on PAMI., vol. 33, no. 11, pp. 2188–2202,
2011.
[6] P. Viola and M. Jones, “Robust real-time face detection,” Int. J. Comput.
Vis., vol. 57, no. 2, pp. 137–154, 2004.
@article{"International Journal of Information, Control and Computer Sciences:71747", author = "Peifei Zhu and Zisheng Li and Yasuki Kakishita and Mayumi Suzuki and Tomoaki Chono", title = "A Robust and Efficient Segmentation Method Applied for Cardiac Left Ventricle with Abnormal Shapes", abstract = "Segmentation of left ventricle (LV) from cardiac
ultrasound images provides a quantitative functional analysis of the
heart to diagnose disease. Active Shape Model (ASM) is widely used
for LV segmentation, but it suffers from the drawback that
initialization of the shape model is not sufficiently close to the target,
especially when dealing with abnormal shapes in disease. In this work,
a two-step framework is improved to achieve a fast and efficient LV
segmentation. First, a robust and efficient detection based on Hough
forest localizes cardiac feature points. Such feature points are used to
predict the initial fitting of the LV shape model. Second, ASM is
applied to further fit the LV shape model to the cardiac ultrasound
image. With the robust initialization, ASM is able to achieve more
accurate segmentation. The performance of the proposed method is
evaluated on a dataset of 810 cardiac ultrasound images that are mostly
abnormal shapes. This proposed method is compared with several
combinations of ASM and existing initialization methods. Our
experiment results demonstrate that accuracy of the proposed method
for feature point detection for initialization was 40% higher than the
existing methods. Moreover, the proposed method significantly
reduces the number of necessary ASM fitting loops and thus speeds up
the whole segmentation process. Therefore, the proposed method is
able to achieve more accurate and efficient segmentation results and is
applicable to unusual shapes of heart with cardiac diseases, such as left
atrial enlargement.", keywords = "Hough forest, active shape model, segmentation,
cardiac left ventricle.", volume = "10", number = "1", pages = "41-5", }