Synergy of Symmetric Region Grow and Active Contour in Reconstruction of a 3D Rat

The general goal of this work is modeling and reconstruction of a three-dimensional (3D) rat from a series of two-dimensional (2D) images captured from an inexpensive digital camera. We proposed a hybrid segmentation method that incorporates symmetric region grow (symRG) and active contour modeling (ACM) to robustly extract regions of interest (ROIs), such as organs, spines, and vessels. symRG is employed to enhance the segmentation performance while the edge information passed from the ACM can help prevent over-segmentation. We built a component-based software platform that includes the symRG and ACM components as well as the other image enhancement, post-segmentation processing, surface rendering components allowing the user to dynamically compose a streamlined 3D rat reconstruction procedure or script. The example dataset in this paper include 284 slices of 2D rat whole-body images. Separate scripts were used to model and visualize the body, heart, lung, stomach, and head. Few user-imposed parameters were required and the whole processing , from loading series of 2D images towards 3D rendition to demonstrate the results, is within two minutes

[1]  Ching Y. Suen,et al.  A recursive thresholding technique for image segmentation , 1998, IEEE Trans. Image Process..

[2]  R A Robb,et al.  Interactive display and analysis of 3-D medical images. , 1989, IEEE transactions on medical imaging.

[3]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Richard Alan Peters,et al.  A new algorithm for image noise reduction using mathematical morphology , 1995, IEEE Trans. Image Process..

[5]  Mark J. Carlotto,et al.  Histogram Analysis Using a Scale-Space Approach , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Theodosios Pavlidis,et al.  Integrating Region Growing and Edge Detection , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  William Schroeder,et al.  The Visualization Toolkit: An Object-Oriented Approach to 3-D Graphics , 1997 .

[8]  William E. Higgins,et al.  Symmetric region growing , 2003, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[9]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  J. P. Jones,et al.  Foundations of Medical Imaging , 1993 .

[11]  Rangaraj M. Rangayyan,et al.  Fuzzy fusion operators to combine results of complementary medical image segmentation techniques , 2003, J. Electronic Imaging.

[12]  Pedro F. Felzenszwalb Representation and detection of deformable shapes , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[13]  Jianping Fan,et al.  Automatic image segmentation by integrating color-edge extraction and seeded region growing , 2001, IEEE Trans. Image Process..

[14]  N. Kanopoulos,et al.  Design of an image edge detection filter using the Sobel operator , 1988 .

[15]  Josef Kittler,et al.  Minimum error thresholding , 1986, Pattern Recognit..

[16]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[17]  Mubarak Shah,et al.  A fast algorithm for active contours , 1990, [1990] Proceedings Third International Conference on Computer Vision.