&lt;title&gt;Dynamic programming algorithm for contrast correction in medical images&lt;/title&gt;

Wagenknecht, Gudrun; Kaiser, Hartmut; Sabri, O.; Buell, Udalrich

doi:10.1117/12.379395

Cited by 6 publications

(8 citation statements)

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“…The data set used here was acquired coronally with a matrix size of (256256127,) and a voxel size of (lmm•lmm•1.56mm), spoiling artifacts reduced by dynamic programming [1]. To get isotropic voxels, the matrix elements are linearly interpolated to (lrnm.lmm.lmm) and the matrix size increased to (256.256.256).…”

Section: Classification Of Tissue Typesmentioning

confidence: 99%

“…A grayscale image of dimension (X, Y,Z) can be interpreted as a mapping function g assigning each voxel to the set of grayscale values G: g: XxYxZ->G (1) with GE G={O G} g(x,y,z)=G…”

Section: Classification Of Tissue Typesmentioning

confidence: 99%

“…The values used were determined by analyzing 24 T1-weighted 3D-FLASH patient data sets in artifact-modified and artifact-free regions for each tissue type (or class): GM°'2425, WM°°6875, CSF003265, SB=-°•°°'7, BG=°°263 With these percentage values, mutual contrast is decreased for tissue types CSF, GM, WM, SB, and contrast to BG for tissue types CSF, GM, WM, SB is increased. The spoiling artifact can then be integrated into the software phantom using the following formulas [1]: for zsp z<zsr+Ezsp:…”

Section: Spoiling Artifact (30)mentioning

confidence: 99%

“…Two examples of applications of this software phantom: 1 . evaluation of a dynamic programming (DP) algorithm for contrast correction based on the cumulative grayscale value histograms of neighboring slices of gradient-spoiled T1-weighted 3D-FLASH image data [1]; 2. evaluation of the first main steps in individual region-of-interest (ROT) atlas extraction, which are the automatic detection of training points and supervised classification of T1-weighted MRI grayscale images into brain tissue types (GM, WM, CSF, SB, BG) [2,3]. Error rates for these examples are calculated based on this software phantom.…”

Section: Spoiling Artifact (30)mentioning

confidence: 99%

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<title>Simulation of 3D MRI brain images for quantitative evaluation of image segmentation algorithms</title>

et al. 2000

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To model the true shape of MRI brain images, automatically classified Ti-weighted 3D MRI images (gray matter, white matter, cerebrospinal fluid, scalp/bone and background) are utilized for simulation of grayscale data and imaging artifacts. For each class, Gaussian distribution of grayscale values is assumed, and mean and variance are computed from grayscale images. A random generator fills up the class images with Gauss-distributed grayscale values. Since grayscale values of neighboring voxels are not correlated, a Gaussian low-pass filtering is done, preserving class region borders. To simulate anatomical variability, a Gaussian distribution in space with user-defined mean and variance can be added at any user-defined position. Several imaging artifacts can be added: 1 . to simulate partial volume effects, every voxel is averaged with neighboring voxels if they have a different class label; 2. a linear or quadratic bias field can be added with user-defined strength and orientation; 3. additional background noise can be added; and 4. artifacts left over after spoiling can be simulated by adding a band with increasing/decreasing grayscale values. With this method, realistic-looking simulated MRI images can be produced to test classification and segmentation algorithms regarding accuracy and robustness even in the presence of artifacts.Evaluation of classification or segmentation results and validation of automatic algorithms is difficult in the case of nonsimulated MRI images because the true borders of tissue types are unknown. Therefore, evaluation is often done by comparing automatically obtained results with results supplied by a physician. This has the disadvantage of subjectivity and inter-or intra-observer variability. Furthermore, 3D brain images have very complex region borderlines. A complete interactive delineation is practically impossible.In this approach, Ti-weighted 3D-FLASH MRI grayscale images are utilized to generate a software phantom for evaluation and validation purposes. The images are classified into gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), scalpfbone (SB) and background (BG) using a simple thresholding method. To model the true shape of MRI brain images, these automatically classified 3D MIRI images are utilized for simulation of grayscale data and imaging artifacts.The main topics of this paper are the detailed descriptions of the methods developed for classification and simulation of Ti-weighted 3D-MRI images and possible artifacts. Examples of applications of this software phantom are briefly described.

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Section: Classification Of Tissue Typesmentioning

confidence: 99%

“…A grayscale image of dimension (X, Y,Z) can be interpreted as a mapping function g assigning each voxel to the set of grayscale values G: g: XxYxZ->G (1) with GE G={O G} g(x,y,z)=G…”

Section: Classification Of Tissue Typesmentioning

confidence: 99%

Section: Spoiling Artifact (30)mentioning

confidence: 99%

Section: Spoiling Artifact (30)mentioning

confidence: 99%

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<title>Simulation of 3D MRI brain images for quantitative evaluation of image segmentation algorithms</title>

et al. 2000

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“…The data sets used in this study are simulated [7] and real gradient-spoiled T1-weighted 3D-FLASH images. The patient data sets are acquired coronally with a matrix size of (256.256.127,) and a voxel size of (1mm •lmm .1.56mm), and spoiling artifacts reduced by a dynamic programming approach [8]. To get isotropic voxels, the matrix elements are linearly interpolated to (1mm 1mm .1mm) and the matrix size increased to (256.256.256).…”

Section: Tissue Typesmentioning

confidence: 99%

<title>Individual 3D region-of-interest atlas of the human brain: neural-network-based tissue classification with automatic training point extraction</title>

et al. 2000

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The purpose of individual 3D region-of-interest atlas extraction is to automatically define anatomically meaningful regions in 3D MRT images for quantification of functional parameters (PET, SPECT: rMRG1u, rCBF). The first step of atlas extraction is to automatically classify brain tissue types into gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), scalp/bone (SB) and background (BG). A feed-forward neural network with back-propagation training algorithm is used and compared to other numerical classifiers. It can be trained by a sample from the individual patient data set in question.Classification is done by a "winner takes all' decision. Automatic extraction of a user-specified number of training points is done in a cross-sectional slice. Background separation is done by simple region growing. The most homogeneous voxels define the region for WM training point extraction (TPE). Non-white-matter and non-background regions are analyzed for GM and CSF training points. For SB TPE, the distance from the BG region is one feature. For each class, spatially uniformly distributed training points are extracted by a random generator from these regions. Simulated and real 3D MRI images are analyzed and error rates for TPE and classification calculated. The resulting class images can be analyzed for extraction of anatomical ROIs.

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Deep Learning Identifies Neuroimaging Signatures Of Alzheimer’s Disease Using Structural And Synthesized Functional Mri Data

Zhu

Liu

Feng

et al. 2021

2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)

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<title>Dynamic programming algorithm for contrast correction in medical images</title>

Cited by 6 publications

References 0 publications

<title>Simulation of 3D MRI brain images for quantitative evaluation of image segmentation algorithms</title>

<title>Simulation of 3D MRI brain images for quantitative evaluation of image segmentation algorithms</title>

<title>Individual 3D region-of-interest atlas of the human brain: neural-network-based tissue classification with automatic training point extraction</title>

Deep Learning Identifies Neuroimaging Signatures Of Alzheimer’s Disease Using Structural And Synthesized Functional Mri Data

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