Next Article in Journal / Special Issue
Automated Segmentation of MS Lesions in MR Images Based on an Information Theoretic Clustering and Contrast Transformations
Previous Article in Journal
Microwave Absorption of Barium Borosilicate, Zinc Borate, Fe-Doped Alumino-Phosphate Glasses and Its Raw Materials
Previous Article in Special Issue
A Hybrid Feature Extractor using Fast Hessian Detector and SIFT
Open AccessArticle

Medical Image Processing for Fully Integrated Subject Specific Whole Brain Mesh Generation

Department of Bioengineering, University of Illinois at Chicago, 851 S Morgan St Chicago, IL 60607, USA
Department of Neurosurgery, University of Illinois at Chicago, 912 S Wood St Chicago, IL 60607, USA
Author to whom correspondence should be addressed.
Academic Editors: Yudong Zhang and Zhengchao Dong
Technologies 2015, 3(2), 126-141;
Received: 16 April 2015 / Revised: 14 May 2015 / Accepted: 15 May 2015 / Published: 21 May 2015
(This article belongs to the Special Issue Medical Imaging & Image Processing)
Currently, anatomically consistent segmentation of vascular trees acquired with magnetic resonance imaging requires the use of multiple image processing steps, which, in turn, depend on manual intervention. In effect, segmentation of vascular trees from medical images is time consuming and error prone due to the tortuous geometry and weak signal in small blood vessels. To overcome errors and accelerate the image processing time, we introduce an automatic image processing pipeline for constructing subject specific computational meshes for entire cerebral vasculature, including segmentation of ancillary structures; the grey and white matter, cerebrospinal fluid space, skull, and scalp. To demonstrate the validity of the new pipeline, we segmented the entire intracranial compartment with special attention of the angioarchitecture from magnetic resonance imaging acquired for two healthy volunteers. The raw images were processed through our pipeline for automatic segmentation and mesh generation. Due to partial volume effect and finite resolution, the computational meshes intersect with each other at respective interfaces. To eliminate anatomically inconsistent overlap, we utilized morphological operations to separate the structures with a physiologically sound gap spaces. The resulting meshes exhibit anatomically correct spatial extent and relative positions without intersections. For validation, we computed critical biometrics of the angioarchitecture, the cortical surfaces, ventricular system, and cerebrospinal fluid (CSF) spaces and compared against literature values. Volumina and surface areas of the computational mesh were found to be in physiological ranges. In conclusion, we present an automatic image processing pipeline to automate the segmentation of the main intracranial compartments including a subject-specific vascular trees. These computational meshes can be used in 3D immersive visualization for diagnosis, surgery planning with haptics control in virtual reality. Subject-specific computational meshes are also a prerequisite for computer simulations of cerebral hemodynamics and the effects of traumatic brain injury. View Full-Text
Keywords: image processing; computational meshes; simulation image processing; computational meshes; simulation
Show Figures

Figure 1

MDPI and ACS Style

Hsu, C.-Y.; Schneller, B.; Ghaffari, M.; Alaraj, A.; Linninger, A. Medical Image Processing for Fully Integrated Subject Specific Whole Brain Mesh Generation. Technologies 2015, 3, 126-141.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

Only visits after 24 November 2015 are recorded.
Search more from Scilit
Back to TopTop