30 April 2004 Extraction of the human cerebral ventricular system from MRI: inclusion of anatomical knowledge and clinical perspective
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Abstract
The human cerebral ventricular system is a complex structure that is essential for the well being and changes in which reflect disease. It is clinically imperative that the ventricular system be studied in details. For this reason computer assisted algorithms are essential to be developed. We have developed a novel (patent pending) and robust anatomical knowledge-driven algorithm for automatic extraction of the cerebral ventricular system from MRI. The algorithm is not only unique in its image processing aspect but also incorporates knowledge of neuroanatomy, radiological properties, and variability of the ventricular system. The ventricular system is divided into six 3D regions based on the anatomy and its variability. Within each ventricular region a 2D region of interest (ROI) is defined and is then further subdivided into sub-regions. Various strict conditions that detect and prevent leakage into the extra-ventricular space are specified for each sub-region based on anatomical knowledge. Each ROI is processed to calculate its local statistics, local intensity ranges of cerebrospinal fluid and grey and white matters, set a seed point within the ROI, grow region directionally in 3D, check anti-leakage conditions and correct growing if leakage occurs and connects all unconnected regions grown by relaxing growing conditions. The algorithm was tested qualitatively and quantitatively on normal and pathological MRI cases and worked well. In this paper we discuss in more detail inclusion of anatomical knowledge in the algorithm and usefulness of our approach from clinical perspective.
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Aamer Aziz, Qingmao Hu, Wieslaw L. Nowinski, "Extraction of the human cerebral ventricular system from MRI: inclusion of anatomical knowledge and clinical perspective", Proc. SPIE 5369, Medical Imaging 2004: Physiology, Function, and Structure from Medical Images, (30 April 2004); doi: 10.1117/12.536313; https://doi.org/10.1117/12.536313
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