Template-driven segmentation of confocal microscopy images

Authors:
Ying-Cheng Chen;Yung-Chang Chen;Ann-Shyn Chiang
Affiliations:
Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan, ROC;Department of Electrical Engineering, National Tsing Hua University, Hsinchu 300, Taiwan, ROC;Institute of Biotechnology, National Tsing Hua University, Hsinchu 300, Taiwan, ROC
Venue:
Computer Methods and Programs in Biomedicine
Year:
2008

Citing 8
Cited 0

Piecewise-linear interpolation between polygonal slices

Computer Vision and Image Understanding
Global Minimum for Active Contour Models: A Minimal Path Approach

International Journal of Computer Vision
General Object Reconstruction Based on Simplex Meshes

International Journal of Computer Vision
Reconstruction and simplification of surfaces from contours

Graphical Models - Pacific Graphics '99 in Graphical Models
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Volumetric medical images segmentation using shape constrained deformable models

CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Snakes, shapes, and gradient vector flow

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

High quality 3D visualization of anatomic structures is necessary for many applications. The anatomic structures first need to be segmented. A variety of segmentation algorithms have been developed for this purpose. For confocal microscopy images, the noise introduced during the specimen preparation process, such as the procedure of penetration or staining, may cause images to be of low contrast in some regions. This property will make segmentation difficult. Also, the segmented structures may have rugged surfaces in 3D visualization. In this paper, we present a hybrid method that is suitable for segmentation of confocal microscopy images. A rough segmentation result is obtained from the atlas-based segmentation via affine registration. The boundaries of the segmentation result are close to the object boundaries, and are regarded as the initial contours of the active contour models. After convergence of the snake algorithm, the resulting contours in regions of low contrast are locally refined by parametric bicubic surfaces to alleviate the problem of incorrect convergence. The proposed method increases the accuracy of the snake algorithm because of better initial contours. Besides, it can provide smoother segmented results in 3D visualization.