Robust midsagittal plane extraction from normal and pathological 3-D neuroradiology images

• Published in: IEEE transactions on medical imaging Vol. 20; no. 3; pp. 175 - 192
• Main Authors: Yanxi Liu, Collins, R.T., Rothfus, W.E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Anisotropic magnetoresistance; Asymmetry; Biological and medical sciences; Brain; Brain - diagnostic imaging; Brain - pathology; Brain Neoplasms - diagnosis; Brain Neoplasms - diagnostic imaging; Clinical medicine; Data mining; Humans; Image edge detection; Image Processing, Computer-Assisted; Imaging, Three-Dimensional; Lesions; Magnetic resonance; Magnetic Resonance Imaging - methods; Medical sciences; Nervous system involvement in other diseases. Miscellaneous; Neurology; NIST; Pathology; Radiology; Real time systems; Robot kinematics; Robustness; Signal to noise ratio; Studies; Tomography, X-Ray Computed - methods; Human; Nervous system diseases; Radiodiagnosis; Nuclear magnetic resonance imaging; Cerebral disorder; Image analysis; Algorithm performance; Central nervous system disease; Cross correlation; Medical imagery; Tridimensional image; Robustness; Computerized axial tomography; Diagnosis; Sagittal slice; Pattern extraction; Brain (vertebrata)
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/42.918469

This paper focuses on extracting the ideal midsagittal plane (iMSP) from three-dimensional (3-D) normal and pathological neuroimages. The main challenges in this work are the structural asymmetry that may exist in pathological brains, and the anisotropic, unevenly sampled image data that is common in clinical practice. We present an edge-based, cross-correlation approach that decomposes the plane fitting problem into discovery of two-dimensional symmetry axes on each slice, followed by a robust estimation of plane parameters. The algorithm's tolerance to brain asymmetries, input image offsets and image noise is quantitatively evaluated. We find that the algorithm can extract the iMSP from input 3-D images with 1) large asymmetrical lesions; 2) arbitrary initial rotation offsets; 3) low signal-to-noise ratio or high bias field. The iMSP algorithm is compared with an approach based on maximization of mutual information registration, and is found to exhibit superior performance under adverse conditions. Finally, no statistically significant difference is found between the midsagittal plane computed by the iMSP algorithm and that estimated by two trained neuroradiologists.