Hippocampus Segmentation Based on Local Linear Mapping

نویسندگان

  • Shumao Pang
  • Jun Jiang
  • Zhentai Lu
  • Xueli Li
  • Wei Yang
  • Meiyan Huang
  • Yu Zhang
  • Yanqiu Feng
  • Wenhua Huang
  • Qianjin Feng
چکیده

We propose local linear mapping (LLM), a novel fusion framework for distance field (DF) to perform automatic hippocampus segmentation. A k-means cluster method is propose for constructing magnetic resonance (MR) and DF dictionaries. In LLM, we assume that the MR and DF samples are located on two nonlinear manifolds and the mapping from the MR manifold to the DF manifold is differentiable and locally linear. We combine the MR dictionary using local linear representation to present the test sample, and combine the DF dictionary using the corresponding coefficients derived from local linear representation procedure to predict the DF of the test sample. We then merge the overlapped predicted DF patch to obtain the DF value of each point in the test image via a confidence-based weighted average method. This approach enabled us to estimate the label of the test image according to the predicted DF. The proposed method was evaluated on brain images of 35 subjects obtained from SATA dataset. Results indicate the effectiveness of the proposed method, which yields mean Dice similarity coefficients of 0.8697, 0.8770 and 0.8734 for the left, right and bi-lateral hippocampus, respectively.

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

Comparing 511 keV Attenuation Maps Obtained from Different Energy Mapping Methods for CT Based Attenuation Correction of PET Data

Introduction:  The  advent  of  dual-modality  PET/CT  scanners  has  revolutionized  clinical  oncology  by  improving lesion localization and facilitating treatment planning for radiotherapy. In addition, the use of  CT images for CT-based attenuation correction (CTAC) decreases the overall scanning time and creates  a noise-free  attenuation  map  (6map).  CTAC  methods  include  scaling,  s...

متن کامل

Improved image features by training non-linear diabolo networks

This paper discusses a trainable system to extract features for image segmentation based on non-linear mapping of local features. Supervised training methods are presented, for artificial neural diabolo networks, which produce a mapping comparable to Fisher’s linear discriminant mapping. This mapping can be used to decrease dimensionality whilst preserving class separability. It is shown that t...

متن کامل

Hippocampus Segmentation using a Local Prior Model on its Boundary

Segmentation techniques based on Active Contour Models have been strongly benefited from the use of prior information during their evolution. Shape prior information is captured from a training set and is introduced in the optimization procedure to restrict the evolution into allowable shapes. In this way, the evolution converges onto regions even with weak boundaries. Although significant effo...

متن کامل

Surface processing methods for point sets using finite elements

We present a framework for processing point-based surfaces via partial differential equations (PDEs). Our framework efficiently and effectively brings well-known PDE-based processing techniques to the field of point-based surfaces. At the core of our method is a finite element discretization of PDEs on point surfaces. This discretization is based on the local assembly of PDE-specific mass and s...

متن کامل

A Modified Character Segmentation Algorithm for Farsi Printed Text Using Upper Contour Labelling

In this paper, a modified segmentation algorithm for printed Farsi words is presented. This algorithm is based on a previous work by Azmi that uses the conditional labeling of the upper contour to find the segmentation points. The main objective is to improve the segmentation results for low quality prints. To achieve this, various modifications on local baseline detection, contour labeling an...

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 7  شماره 

صفحات  -

تاریخ انتشار 2017