Extraction of the midsagittal plane from morphological neuroimages using the Kullback–Leibler’s measure

A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP) from volumetric neuroimages is presented. The method works in two stages (coarse and fine) and is based on calculation of the Kullback and Leibler’s (KL) measure, which characterizes the difference bet...

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Bibliographic Details
Published in:Medical image analysis 2006-12, Vol.10 (6), p.863-874
Main Authors: Volkau, Ihar, Bhanu Prakash, K.N., Ananthasubramaniam, Anand, Aziz, Aamer, Nowinski, Wieslaw L.
Format: Article
Language:English
Subjects:
Arachnoid Cysts - pathology
Brain - anatomy & histology
Brain - diagnostic imaging
Brain - pathology
Brain Neoplasms - pathology
Ependymoma - pathology
Humans
Image Interpretation, Computer-Assisted
Interhemispheric fissure
Kullback–Leibler’s measure
Magnetic Resonance Imaging
Meningioma - pathology
Midsagittal plane
Phantoms, Imaging
Talairach transformation
Tomography, X-Ray Computed
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Summary:A theoretically simple and computationally efficient method to extract the midsagittal plane (MSP) from volumetric neuroimages is presented. The method works in two stages (coarse and fine) and is based on calculation of the Kullback and Leibler’s (KL) measure, which characterizes the difference between two distributions. Slices along the sagittal direction are analyzed with respect to a reference slice to determine the coarse MSP. To calculate the final MSP, a local search algorithm is applied. The proposed method does not need any preprocessing, like reformatting, skull stripping, etc. The algorithm was validated quantitatively on 75 MRI datasets of different pulse sequences (T1WI, T2WI, FLAIR and SPGR) and MRA. The angular and distance errors between the calculated MSP and the ground truth lines marked by the expert were calculated. The average distance and angular deviation were 1.25 pixels and 0.63°, respectively. In addition, the algorithm was tested qualitatively on PD, FLAIR, MRA, and CT datasets. To analyze the robustness of the method against rotation, inhomogeneity and noise, the phantom data were used.
ISSN:1361-8415
1361-8423
DOI:10.1016/j.media.2006.07.005