A knowledge‐guided active contour method of segmentation of cerebella on MR images of pediatric patients with medulloblastoma

Abstract

Purpose:To develop an automated method for identification of the cerebella on magnetic resonance (MR) images of patients with medulloblastoma. Materials and Methods:The method used a template constructed from 10 patients' aligned MR head images, and the contour of this template was superimposed on the aligned data set of a given patient as the starting contour. The starting contour was then actively adjusted to locate the boundary of the cerebellum of the given patient. Morphologic operations were applied to the outlined volume to generate cerebellum images. The method was then applied to data sets of 20 other patients to generate cerebellum images and volumetric results. Results:Comparison of the automatically generated cerebellum images with two sets of manually traced images showed a strong correlation between the automatically and manually generated volumetric results (correlation coefficient, 0.97). The average Jaccard similarities were 0.89 and 0.88 in comparison to each of two manually traced images, respectively. The same comparisons yielded average kappa indexes of 0.94 and 0.93, respectively. Conclusion:The method was robust and accurate for cerebellum segmentation on MR images of patients with medulloblastoma. The method may be applied to investigations that require segmentation and quantitative measurement of MR images of the cerebellum.

Efficient and Effective Extraction of Vocal Fold Vibratory Patterns from High-Speed Digital Imaging

“…The active contour method includes numerous iterations in computation. [12][13][14][24][25][26][27][28][29] Thus, the active contour method is much more computationally expensive than the histogram method or our method. This makes the active contour method difficult to apply to the realtime batch processing of thousands of high-speed images.…”

“…Although it is time consuming and sensitive to noise, the active contour algorithm has become an important method in the medical imaging community. [12][13][14][25][26][27][28][29] To investigate the effectiveness and efficiency of our imagedetection algorithm, we will compare the results with those obtained from the above two popular analytical algorithms. Although improvements to have been made to these algorithms address their deficiencies, we compare the results of our method to these in computation accuracy and computation time to show that our algorithm overcomes the obstacles commonly encountered in high-speed vocal fold image processing.…”

Interactive and Intelligent Approach for Brain Extraction from High-Resolution Volumetric MR Neuroimages

“…These methods include histogram-based thresholding and morphological operations [1,2] , connected component analysis [3] , region growing and edge detection [4] , voxel-based morphometry [5,6] , atlas-guided brain structure identification [7] , model-based or knowledge-guided active contour method [8] , and hybrid models [9,10] . In the scientific community, there are several downloadable software packages, which are widely cited by numerous papers, for brain extraction.…”

Improving segmentation accuracy for magnetic resonance imaging using a boosted decision tree

“…Another wavelet application has been used to design attribute vectors as spatial features of voxels for determining correspondence in 3D brain MR images (Xue et al, 2004). Segmentation applications include tissue volume quantification and 3D spatial structure reconstruction, which greatly aid in disease diagnosis (Joliot and Majoyer, 1993;Tang et al, 2000;Yoo et al, 2001;Zoroofi et al, 2001Zoroofi et al, , 2004Archibald et al, 2003;Mohr et al, 2004;Ali et al, 2005;Andrey and Maurin, 2005;He et al, 2005;Shan et al, 2005;Noulhiane et al, 2006).…”