Automatic brain matter segmentation of computed tomography images using a statistical model: A tool to gain working time!

Francesco Bertè, Giuseppe Lamponi, Placido Bramanti, Rocco S. Calabrò

Research output: Contribution to journalArticle

Abstract

Brain computed tomography (CT) is useful diagnostic tool for the evaluation of several neurological disorders due to its accuracy, reliability, safety and wide availability. In this field, a potentially interesting research topic is the automatic segmentation and recognition of medical regions of interest (ROIs). Herein, we propose a novel automated method, based on the use of the active appearance model (AAM) for the segmentation of brain matter in CT images to assist radiologists in the evaluation of the images. The method described, that was applied to 54 CT images coming from a sample of outpatients affected by cognitive impairment, enabled us to obtain the generation of a model overlapping with the original image with quite good precision. Since CT neuroimaging is in widespread use for detecting neurological disease, including neurodegenerative conditions, the development of automated tools enabling technicians and physicians to reduce working time and reach a more accurate diagnosis is needed.

Original languageEnglish
Pages (from-to)460-467
Number of pages8
JournalNeuroradiology Journal
Volume28
Issue number5
DOIs
Publication statusPublished - Oct 1 2015

Keywords

  • active appearance model
  • cerebral ventricles
  • clinical decision-making
  • medical imaging
  • Segmentation

ASJC Scopus subject areas

  • Clinical Neurology
  • Radiology Nuclear Medicine and imaging
  • Medicine(all)

Fingerprint Dive into the research topics of 'Automatic brain matter segmentation of computed tomography images using a statistical model: A tool to gain working time!'. Together they form a unique fingerprint.

  • Cite this