Particle Method for Sub-Voxel Extraction of Cerebral Surface in Neonatal MR images

نویسندگان

  • Daisuke Yokomichi
  • Syoji Kobashi
  • Yuki Wakata
  • Kumiko Ando
  • Reiichi Ishikura
  • Kei Kuramoto
  • Tomomoto Ishikawa
  • Shozo Hirota
  • Yutaka Hata
چکیده

Cerebral contour extraction from magnetic resonance (MR) images is a fundamental work to analyze brain MR images. The methods can be roughly classified into three approaches, voxel-based, mesh-based and particle-based. Each method has advantages and disadvantages. Especially, particle based method can extract the complicated sulci with sub-voxel accuracy. The remained work is to develop a method for estimating probability of particle transition among gray matter, white matter and cerebrospinal fluid. This paper proposes a new method for calculating the particle transition probability based on fuzzy inference technique. The proposed method was applied to computer synthesized MR images and neonatal brain MR images of volunteers. Keywordsneonate, MR image, cerebral surface, particle method, fuzzy inference

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Comparison of state-of-the-art atlas-based bone segmentation approaches from brain MR images for MR-only radiation planning and PET/MR attenuation correction

Introduction: Magnetic Resonance (MR) imaging has emerged as a valuable tool in radiation treatment (RT) planning as well as Positron Emission Tomography (PET) imaging owing to its superior soft-tissue contrast. Due to the fact that there is no direct transformation from voxel intensity in MR images into electron density, itchr('39')s crucial to generate a pseudo-CT (Computed Tomography) image ...

متن کامل

Automatic segmentation of neonatal images using convex optimization and coupled level sets

Accurate segmentation of neonatal brain MR images remains challenging mainly due to their poor spatial resolution, inverted contrast between white matter and gray matter, and high intensity inhomogeneity. Most existing methods for neonatal brain segmentation are atlas-based and voxel-wise. Although active contour/surface models with geometric information constraint have been successfully applie...

متن کامل

Cortical Surface Reconstruction from High-Resolution MR Brain Images

Reconstruction of the cerebral cortex from magnetic resonance (MR) images is an important step in quantitative analysis of the human brain structure, for example, in sulcal morphometry and in studies of cortical thickness. Existing cortical reconstruction approaches are typically optimized for standard resolution (~1 mm) data and are not directly applicable to higher resolution images. A new PD...

متن کامل

Compensation of brain shift during surgery using non-rigid registration of MR and ultrasound images

Background: Surgery and accurate removal of the brain tumor in the operating room and after opening the scalp is one of the major challenges for neurosurgeons due to the removal of skull pressure and displacement and deformation of the brain tissue. This displacement of the brain changes the location of the tumor relative to the MR image taken preoperatively. Methods: This study, which is done...

متن کامل

Automatic 3D segmentation of the kidney in MR images using wavelet feature extraction and probability shape model

Numerical estimation of the size of the kidney is useful in evaluating conditions of the kidney, especially, when serial MR imaging is performed to evaluate the kidney function. This paper presents a new method for automatic segmentation of the kidney in three-dimensional (3D) MR images, by extracting texture features and statistical matching of geometrical shape of the kidney. A set of Wavelet...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2011