Literature DB >> 11679214

Segmentation of avascular necrosis of the femoral head using 3-D MR images.

R A Zoroofi1, T Nishii, Y Sato, N Sugano, H Yoshikawa, S Tamura.   

Abstract

Avascular necrosis of the femoral head (ANFH) is a common clinical disorder in the orthopedic field. Traditional approaches to study the extent of ANFH rely primarily on manual segmentation of clinical magnetic resonance images (MRI). However, manual segmentation is insufficient for quantitative evaluation and staging of ANFH. This paper presents a new computerized approach for segmentation of necrotic lesions of the femoral head. The segmentation method consists of several steps including histogram based thresholding, 3-D morphological operations, oblique data reconstruction, and 2-D ellipse fitting. The proposed technique is rapid and efficient. In addition, it is available as a Microsoft Windows free software package on the Internet. Feasibility of the method is demonstrated on the data sets of 30 patients (1500 MR images).

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Year:  2001        PMID: 11679214     DOI: 10.1016/s0895-6111(01)00013-1

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  4 in total

1.  Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks.

Authors:  Ilige S Hage; Ramsey F Hamade
Journal:  J Bone Miner Metab       Date:  2015-06-24       Impact factor: 2.626

2.  A two-stage rule-constrained seedless region growing approach for mandibular body segmentation in MRI.

Authors:  Dong Xu Ji; Kelvin Weng Chiong Foong; Sim Heng Ong
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-02-09       Impact factor: 2.924

3.  Segmentation of Drug-Treated Cell Image and Mitochondrial-Oxidative Stress Using Deep Convolutional Neural Network.

Authors:  Awais Khan Nawabi; Sheng Jinfang; Rashid Abbasi; Muhammad Shahid Iqbal; Md Belal Bin Heyat; Faijan Akhtar; Kaishun Wu; Baidenger Agyekum Twumasi
Journal:  Oxid Med Cell Longev       Date:  2022-05-26       Impact factor: 7.310

4.  Segmentation of the Proximal Femur from MR Images using Deep Convolutional Neural Networks.

Authors:  Cem M Deniz; Siyuan Xiang; R Spencer Hallyburton; Arakua Welbeck; James S Babb; Stephen Honig; Kyunghyun Cho; Gregory Chang
Journal:  Sci Rep       Date:  2018-11-07       Impact factor: 4.379

  4 in total

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