Automated cerebrum segmentation from three-dimensional sagittal brain MR images

We present a fully automated cerebrum segmentation algorithm for full three-dimensional sagittal brain MR images. First, cerebrum segmentation from a midsagittal brain MR image is performed utilizing landmarks, anatomical information, and a connectivity-based threshold segmentation algorithm as previously reported. Recognizing that cerebrum in laterally adjacent slices tends to have similar size and shape, we use the cerebrum segmentation result from the midsagittal brain MR image as a mask to guide cerebrum segmentation in adjacent lateral slices in an iterative fashion. This masking operation yields a masked image (preliminary cerebrum segmentation) for the next lateral slice, which may truncate brain region(s). Truncated regions are restored by first finding end points of their boundaries, by comparing the mask image and masked image boundaries, and then applying a connectivity-based algorithm. The resulting final extracted cerebrum image for this slice is then used as a mask for the next lateral slice. The algorithm yielded satisfactory fully automated cerebrum segmentations in three-dimensional sagittal brain MR images, and had performance superior to conventional edge detection algorithms for segmentation of cerebrum from 3D sagittal brain MR images.

[1]  Victoria Interrante,et al.  Multiscale, Geometric Image Descriptions for Interactive Object Definition , 1989, DAGM-Symposium.

[2]  C. Y. Lee.  An Algorithm for Path Connections and Its Applications , 1961, IRE Trans. Electron. Comput..

[3]  Chin-Tu Chen,et al.  Medical image segmentation by a constraint satisfaction neural network , 1990 .

[4]  Benoit M. Dawant,et al.  Correction of intensity variations in MR images for computer-aided tissue classification , 1993, IEEE Trans. Medical Imaging.

[5]  A. Alavi,et al.  Analysis of brain and cerebrospinal fluid volumes with MR imaging: impact on PET data correction for atrophy. Part II. Aging and Alzheimer dementia. , 1991, Radiology.

[6]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

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

[8]  I. Kapouleas.  Segmentation and feature extraction for magnetic resonance brain image analysis , 1990, [1990] Proceedings. 10th International Conference on Pattern Recognition.

[9]  Lawrence O. Hall,et al.  Knowledge-based classification and tissue labeling of MR images of human brain , 1993, IEEE Trans. Medical Imaging.

[10]  Charles R. Meyer,et al.  Retrospective correction of intensity inhomogeneities in MRI , 1995, IEEE Trans. Medical Imaging.

[11]  U Tiede,et al.  3-D segmentation of MR images of the head for 3-D display. , 1990, IEEE transactions on medical imaging.

[12]  L. Fan,et al.  Nonlinear wavelet filter for intracoronary ultrasound images , 1996, Computers in Cardiology 1996.

[13]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[14]  H E Cline,et al.  3D reconstruction of the brain from magnetic resonance images using a connectivity algorithm. , 1987, Magnetic Resonance Imaging.

[15]  Jerry L Prince,et al.  Measurement of Radiotracer Concentration in Brain Gray Matter Using Positron Emission Tomography: MRI-Based Correction for Partial Volume Effects , 1992, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[16]  J Toriwaki,et al.  Automatic segmentation of head MRI images by knowledge guided thresholding. , 1991, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[17]  P. Narayana,et al.  Accuracy and Reproducibility in Volumetric Analysis of Multiple Sclerosis Lesions , 1993, Journal of computer assisted tomography.

[18]  M W Vannier,et al.  Post‐acquisition correction of MR inhomogeneities , 1996, Magnetic resonance in medicine.

[19]  M Unser,et al.  Unsupervised connectivity-based thresholding segmentation of midsagittal brain MR images , 1998, Comput. Biol. Medicine.

[20]  Steven R. Fleagle,et al.  Methods of graph searching for border detection in image sequences with applications to cardiac magnetic resonance imaging , 1995, IEEE Trans. Medical Imaging.

[21]  S. Dellepiane,et al.  Image Segmentation: Errors, sensitivity, and uncertainty , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[22]  W.E. Snyder,et al.  3-D intracoronary ultrasound surface detection using the "thin-plate" model , 1996, Computers in Cardiology 1996.

[23]  W.E. Snyder,et al.  A gradient field metric for quality control of automated intracoronary ultrasound boundaries , 1994, Computers in Cardiology 1994.

[24]  Atam P. Dhawan,et al.  Segmentation of medical images through competitive learning , 1993 .

[25]  Karl J. Friston,et al.  Spatial registration and normalization of images , 1995 .

[26]  M. Stella Atkins,et al.  Fully automatic segmentation of the brain in MRI , 1998, IEEE Transactions on Medical Imaging.

[27]  J. Friedman,et al.  Quantitative MRI studies for assessment of multiple sclerosis , 1992, Magnetic resonance in medicine.