Robust Midsagittal Plane Extraction from Normal and Pathological 3-D Neuroradiology Images

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 adve..

Automatic extraction of the central symmetry (mid-sagittal) plane from neuroradiology images

This research is sponsored by the Allegheny-Singer Research Institute under prime contract through the Nationa

Automatic Bilateral Symmetry (Midsagittal) Plane Extraction from Pathological 3D Neuroradiological Images

Most pathologies (tumor, bleed, stroke) of the human brain can be determined by a symmetry-based analysis of neural scans showing the brain's 3D internal structure. Detecting departures of this internal structure from its normal bilateral symmetry can guide the classification of abnormalities. This process is facilitated by first locating the ideal symmetry plane (midsagittal) with respect to which the brain is invariant under reflection. An algorithm to automatically identify this bilateral symmetry plane from a given 3D clinical image has been developed. The method has been tested on both normal and pathological brain scans, multimodal data (CT and MR), and on coarsely sliced samples with elongated voxel sizes. Keywords: bilateral symmetry, midsagittal plane, cross-correlation 1. INTRODUCTION Normal human brains present an approximate bilateral symmetry with respect to their midsagittal planes, this symmetry is often absent in pathological brains. Most pathologies (tumor, bleed, st..

Content-based 3d neuroradiologic image retrieval: Preliminary results

Acontent-based 3D neuroradiologic image retrieval system is being developed at the Robotics Institute of CMU. The special characteristics of this system include: 1) directly dealing with multimodal 3D images (MR/CT); 2) image similarity based on anatomical structures of the human brain; 3) combining both visual and collateral information for indexing and retrieval. A testbed has been implemented for using detected salient visual features for indexing and retrieving 3D images.

Automatic Bilateral Symmetry (Midsagittal) Plane Extraction from Pathological 3D Neuroradiological Images

Most pathologies (tumor, bleed, stroke) of the human brain can be determined by a symmetry-based analysis of neural scans showing the brain's 3D internal structure. Detecting departures of this internal structure from its normal bilateral symmetry can guide the classi cation of abnormalities. This process is facilitated by rst locating the ideal symmetry plane (midsagittal) with respect to which the brain is invariant under re ection. An algorithm to automatically identify this bilateral symmetry plane from a given 3D clinical image has been developed. The method has been tested on both normal and pathological brain scans, multimodal data (CT and MR), and on coarsely sliced samples with elongated voxel sizes