Knowledge-Driven Automated Extraction of the Human Cerebral Ventricular System from MR Images

Yan Xia, QingMao Hu, Aamer Aziz, Wieslaw L. Nowinski

Research output: Book chapter/Published conference paperConference paperpeer-review

2 Citations (Scopus)


This work presents an efficient and automated method to extract the human cerebral ventricular system from MRI driven by anatomic knowledge. The ventricular system is divided into six three-dimensional regions; six ROIs are defined based on the anatomy and literature studies regarding variability of the cerebral ventricular system. The distribution histogram of radiological properties is calculated in each ROI, and the intensity thresholds for extracting each region are automatically determined. Intensity inhomogeneities are accounted for by adjusting intensity threshold to match local situation. The extracting method is based on region-growing and anatomical knowledge, and is designed to include all ventricular parts, even if they appear unconnected on the image. The ventricle extraction method was implemented on the Window platform using C++, and was validated qualitatively on 30 MRI studies with variable parameters.
Original languageEnglish
Title of host publication18th Information Processing in Medical Imaging (IPMI2003)
Place of PublicationBerlin
PublisherSpringer-Verlag London Ltd.
Number of pages12
Publication statusPublished - 2003
EventInformation Processing in Medical Imaging (IPMI) Conference - Ambleside, UK, United Kingdom
Duration: 20 Jul 200325 Jul 2003


ConferenceInformation Processing in Medical Imaging (IPMI) Conference
Country/TerritoryUnited Kingdom


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