Medical imaging and osteoporosis: fractal's lacunarity analysis of trabecular bone in MR images

The aim of this paper is to demonstrate the major role and potential of three of the most powerful open source computerized tools for the advanced processing of medical images, in the study of neuroanatomy. DICOM images were acquired with radiodiagnostic equipment using 1.5 Tesla Magnetic Resonance (MR) images. Images were further processed using the following applications: first, OsiriXTM version 4.0 32 bits for OS; Second, 3D Slicer version 4.3; and finally, MRIcron, version 6. Advanced neuroimaging processing requires two key features: segmentation and three-dimensional or volumetric reconstruction. Examples of identification and reconstruction of some of the most complex neuroimaging elements such vascular ones and tractographies are included in this paper. The three selected applications represent some of the most versatile technologies within the field of medical imaging.

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Multifractal-based lacunarity analysis of trabecular bone in radiography

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2019.103559 ◽

2020 ◽

Vol 116 ◽

pp. 103559 ◽

Cited By ~ 1

Author(s):

Dhevendra Alagan Palanivel ◽

Sivakumaran Natarajan ◽

Sainarayanan Gopalakrishnan ◽

Rachid Jennane

Keyword(s):

Trabecular Bone ◽

Lacunarity Analysis

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Processing and Analysis of In Vivo High-Resolution MR Images of Trabecular Bone for Longitudinal Studies: Reproducibility of Structural Measures and Micro-Finite Element Analysis Derived Mechanical Properties

Osteoporosis International ◽

10.1007/s001980200027 ◽

2002 ◽

Vol 13 (4) ◽

pp. 278-287 ◽

Cited By ~ 171

Author(s):

D. C. Newitt ◽

B. van Rietbergen ◽

S. Majumdar

Keyword(s):

Mechanical Properties ◽

Finite Element Analysis ◽

Finite Element ◽

High Resolution ◽

Trabecular Bone ◽

Longitudinal Studies ◽

Mr Images ◽

Element Analysis ◽

Structural Measures

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Prediction of Biomechanical Properties of Trabecular Bone in MR Images With Geometric Features and Support Vector Regression

IEEE Transactions on Biomedical Engineering ◽

10.1109/tbme.2011.2119484 ◽

2011 ◽

Vol 58 (6) ◽

pp. 1820-1826 ◽

Cited By ~ 37

Author(s):

Markus B Huber ◽

Sarah L Lancianese ◽

M B Nagarajan ◽

I Z Ikpot ◽

A L Lerner ◽

...

Keyword(s):

Trabecular Bone ◽

Support Vector Regression ◽

Biomechanical Properties ◽

Support Vector ◽

Geometric Features ◽

Mr Images

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Subvoxel processing: A method for reducing partial volume blurring with application to in vivo MR images of trabecular bone

Magnetic Resonance in Medicine ◽

10.1002/mrm.10138 ◽

2002 ◽

Vol 47 (5) ◽

pp. 948-957 ◽

Cited By ~ 51

Author(s):

Scott N. Hwang ◽

Felix W. Wehrli

Keyword(s):

Trabecular Bone ◽

Mr Images ◽

Partial Volume

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Preliminary study of slow and fast ultrasonic waves using MR images of trabecular bone phantom

10.1063/1.4901395 ◽

2014 ◽

Author(s):

S. E. Solis-Najera ◽

J. A. Neria-Pérez ◽

L. Medina ◽

R. Garipov ◽

A. O. Rodríguez

Keyword(s):

Trabecular Bone ◽

Ultrasonic Waves ◽

Mr Images ◽

Preliminary Study

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MEDICAL IMAGING ANALYSIS OF THE THREE DIMENSIONAL (3D) ARCHITECTURE OF TRABECULAR BONE: TECHNIQUES AND THEIR APPLICATIONS

Medical Imaging Systems Technology ◽

10.1142/9789812701060_0001 ◽

2005 ◽

pp. 1-41 ◽

Cited By ~ 2

Author(s):

AGOSTINO P. ACCARDO ◽

IGOR STROLKA ◽

RENATO TOFFANIN ◽

FRANCO VITTUR

Keyword(s):

Medical Imaging ◽

Trabecular Bone ◽

Three Dimensional ◽

Imaging Analysis ◽

3D Architecture

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Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images

Mathematical Problems in Engineering ◽

10.1155/2020/8273173 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Hyunhee Lee ◽

Jaechoon Jo ◽

Heuiseok Lim

Keyword(s):

Brain Tumor ◽

Medical Imaging ◽

Image Synthesis ◽

Generative Adversarial Networks ◽

Imaging Data ◽

Mr Images ◽

Style Transfer ◽

Adversarial Networks ◽

Robust Model ◽

Privacy Issues

Due to institutional and privacy issues, medical imaging researches are confronted with serious data scarcity. Image synthesis using generative adversarial networks provides a generic solution to the lack of medical imaging data. We synthesize high-quality brain tumor-segmented MR images, which consists of two tasks: synthesis and segmentation. We performed experiments with two different generative networks, the first using the ResNet model, which has significant advantages of style transfer, and the second, the U-Net model, one of the most powerful models for segmentation. We compare the performance of each model and propose a more robust model for synthesizing brain tumor-segmented MR images. Although ResNet produced better-quality images than did U-Net for the same samples, it used a great deal of memory and took much longer to train. U-Net, meanwhile, segmented the brain tumors more accurately than did ResNet.

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Intelligent Diagnosis Method of MRI Brain Image Using Parallel Self-Organizing Feature Maps Neural Network

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3285 ◽

2021 ◽

Vol 11 (2) ◽

pp. 487-496

Author(s):

Li Liu ◽

Chi Hua ◽

Zixuan Cheng ◽

Yunfeng Ji

Keyword(s):

Neural Network ◽

Image Segmentation ◽

Medical Imaging ◽

Automatic Segmentation ◽

Learning Ability ◽

Mr Images ◽

Feature Maps ◽

Intelligent Diagnosis ◽

Brain Mr Images ◽

Self Organizing

Advances in medical imaging skills have promoted the influence of medical imaging in neuroscience. Having advanced medical imaging technology is essential for the medical industry. Magnetic resonance imaging (MRI) plays a central role in medical imaging. It plays a key role in the treatment of various human diseases. Doctors analyze brain size, shape, and location in brain MR images to assess brain disease and develop a medical plan. The manual division of brain tissue by experts is heavy and subjective. Therefore, the study of automatic segmentation of brain MR images has practical significance. Because the characteristics of brain MRI images are low contrast and high noise, which seriously affects the accuracy of image segmentation, the current image segmentation methods have some limitations in this application. In this paper, multiple self-organizing feature maps neural network (SOM-NN) are utilized to construct a parallel self-organizing feature maps neural network (PSOM-NN), which converts the segmentation problem of brain images into the classification problem of PSOMNN. The experiments show that SOM has strong self-learning ability in learning and training, and the parallel ability of PSOM-NN model greatly reduces the segmentation time, improves the real-time performance of the model, and helps to realize fully automatic or semi-automatic segmentation of the lesion area. PSOM can promote the improvement of segmentation accuracy and facilitate intelligent diagnosis.

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