Three-dimensional coding of volumetric medical images using regions of interest

2003 ◽  
Author(s):  
D. Agrafiotis
Keyword(s):  
Medical Images ◽  
Three Dimensional ◽  
Regions Of Interest ◽  
Volumetric Medical Images

scholarly journals Employing New Hybrid Adaptive Wavelet-Based Transform and Histogram Packing to Improve JP3D Compression of Volumetric Medical Images

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1385
Author(s):  
Roman Starosolski
Keyword(s):  
Compression Ratio ◽  
Medical Images ◽  
Three Dimensional ◽  
Discrete Wavelet ◽  
Adaptive Wavelet ◽  
Entropy Estimation ◽  
New Methods ◽  
Adaptive Parameter ◽  
3D Dwt ◽  
Volumetric Medical Images

The primary purpose of the reported research was to improve the discrete wavelet transform (DWT)-based JP3D compression of volumetric medical images by applying new methods that were only previously used in the compression of two-dimensional (2D) images. Namely, we applied reversible denoising and lifting steps with step skipping to three-dimensional (3D)-DWT and constructed a hybrid transform that combined 3D-DWT with prediction. We evaluated these methods using a test-set containing images of modalities: Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Ultrasound (US). They proved effective for 3D data resulting in over two times greater compression ratio improvements than competitive methods. While employing fast entropy estimation of JP3D compression ratio to reduce the cost of image-adaptive parameter selection for the new methods, we found that some MRI images had sparse histograms of intensity levels. We applied the classical histogram packing (HP) and found that, on average, it resulted in greater ratio improvements than the new sophisticated methods and that it could be combined with these new methods to further improve ratios. Finally, we proposed a few practical compression schemes that exploited HP, entropy estimation, and the new methods; on average, they improved the compression ratio by up to about 6.5% at an acceptable cost.

scholarly journals Semantic Segmentation of 3D Medical Images with 3D Convolutional Neural Networks

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Alejandra Márquez Herrera ◽  
Alex J. Cuadros-Vargas ◽  
Helio Pedrini
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Loss Function ◽  
Medical Images ◽  
Three Dimensional ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Volumetric Medical Images ◽  
Magnetic Resonance Imaging Mri ◽  
3D Cnn

A neural network is a mathematical model that is able to perform a task automatically or semi-automatically after learning the human knowledge that we provided. Moreover, a Convolutional Neural Network (CNN) is a type of neural network that has shown to efficiently learn tasks related to the area of image analysis, such as image segmentation, whose main purpose is to find regions or separable objects within an image. A more specific type of segmentation, called semantic segmentation, guarantees that each region has a semantic meaning by giving it a label or class. Since CNNs can automate the task of image semantic segmentation, they have been very useful for the medical area, applying them to the segmentation of organs or abnormalities (tumors). This work aims to improve the task of binary semantic segmentation of volumetric medical images acquired by Magnetic Resonance Imaging (MRI) using a pre-existing Three-Dimensional Convolutional Neural Network (3D CNN) architecture. We propose a formulation of a loss function for training this 3D CNN, for improving pixel-wise segmentation results. This loss function is formulated based on the idea of adapting a similarity coefficient, used for measuring the spatial overlap between the prediction and ground truth, and then using it to train the network. As contribution, the developed approach achieved good performance in a context where the pixel classes are imbalanced. We show how the choice of the loss function for training can affect the nal quality of the segmentation. We validate our proposal over two medical image semantic segmentation datasets and show comparisons in performance between the proposed loss function and other pre-existing loss functions used for binary semantic segmentation.

Statistical analysis of normal and abnormal dissymmetry in volumetric medical images

2000 ◽  
Vol 4 (2) ◽  
pp. 111-121 ◽  
Author(s):  
J.-P Thirion ◽  
S Prima ◽  
G Subsol ◽  
N Roberts
Keyword(s):  
Statistical Analysis ◽  
Medical Images ◽  
Volumetric Medical Images

Automatic recognition of anatomical regions in three-dimensional medical images

2016 ◽  
Vol 76 ◽  
pp. 120-133
Author(s):  
Márton Tóth ◽  
László Ruskó ◽  
Balázs Csébfalvi
Keyword(s):  
Medical Images ◽  
Three Dimensional ◽  
Automatic Recognition

scholarly journals A three-dimensional thalamocortical dataset for characterizing brain heterogeneity

2020 ◽  
Author(s):  
Judy A. Prasad ◽  
Aishwarya H. Balwani ◽  
Erik C. Johnson ◽  
Joseph D. Miano ◽  
Vandana Sampathkumar ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Brain Regions ◽  
Regions Of Interest ◽  
Subcortical Structures ◽  
Neural Structure ◽  
Brain Areas ◽  
X Ray ◽  
Anatomical Imaging ◽  
Brain Pathways ◽  
Critical Aspects

AbstractNeural cytoarchitecture is heterogeneous, varying both across and within brain regions. The consistent identification of regions of interest is one of the most critical aspects in examining neurocircuitry, as these structures serve as the vital landmarks with which to map brain pathways. Access to continuous, three-dimensional volumes that span multiple brain areas not only provides richer context for identifying such landmarks, but also enables a deeper probing of the microstructures within. Here, we describe a three-dimensional X-ray microtomography imaging dataset of a well-known and validated thalamocortical sample, encompassing a range of cortical and subcortical structures. In doing so, we provide the field with access to a micron-scale anatomical imaging dataset ideal for studying heterogeneity of neural structure.

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