Medical Image Analysis

Dhawan, Atam P.

doi:10.1002/9780470918548

Cited by 128 publications

(120 citation statements)

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“…K-space visualization was performed with fast Fourier transform (FFT) [1] [2] [5]. Different phantoms and materials were tested to see which one provides the most homogeneous k-space pattern.…”

Section: Overviewmentioning

confidence: 99%

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Short Report: A Sponge Phantom Provides a Homogeneous k-Space Pattern at MRI

Menke¹

2014

OJRad

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Background: The spectral coverage of magnetic resonance (MR) sequences can be well assessed in k-space. However, many objects do not provide high signal intensities in the peripheral k-space. Purpose: To experimentally find a phantom that provides a homogeneous spectral pattern also at the high spatial frequencies of the k-space periphery. Material and Methods: Different phantoms were imaged on a 1.5 Tesla magnet, and the resulting MR images were viewed in k-space after fast Fourier transform. Results: Firstly, phantoms with a homogeneous physical structure were studied with a T2-weighted MR sequence, but they provided an inhomogeneous k-space pattern with dominant central low-frequency components. Secondly, phantoms with an inhomogeneous physical structure were studied. In this group, a water-soaked sponge showed a relatively homogeneous k-space pattern also at high spatial frequencies, owing to the fine porous structure. This sponge phantom can also be soaked with Gadolinium chelates for T1-weighted MR imaging. Conclusion: A simple sponge phantom provides a homogeneous k-space pattern, owing to its fine porous structure. This could be utilized in MR sequence development and for viewing the spectral coverage of MR sequences in k-space.

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Section: Overviewmentioning

confidence: 99%

“…All magnetic resonance (MR) images are primarily acquired in k-space that is the spatial frequency domain [1]- [3]. The central k-space represents low-frequency components and therefore determines image contrast and large-sized structures.…”

Section: Introductionmentioning

confidence: 99%

Short Report: A Sponge Phantom Provides a Homogeneous k-Space Pattern at MRI

Menke¹

2014

OJRad

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“…The value of κ was assigned with 1,000, and the value of L was 13. The value of I f was determined by two standard deviations from the mean intensity of the second highest cluster, which is obtained by applying the K-means clustering approach (K04) to the T1 images [29].…”

Section: Energy Function Of the Deformable Modelmentioning

confidence: 99%

A Knowledge-Based Approach for Carpal Tunnel Segmentation from Magnetic Resonance Images

Chen

Wang²,

Lin³

et al. 2012

J Digit Imaging

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Carpal tunnel syndrome (CTS) has been reported as one of the most common peripheral neuropathies. Carpal tunnel segmentation from magnetic resonance (MR) images is important for the evaluation of CTS. To date, manual segmentation, which is time-consuming and operator dependent, remains the most common approach for the analysis of the carpal tunnel structure. Therefore, we propose a new knowledge-based method for automatic segmentation of the carpal tunnel from MR images. The proposed method first requires the segmentation of the carpal tunnel from the most proximally cross-sectional image. Three anatomical features of the carpal tunnel are detected by watershed and polygonal curve fitting algorithms to automatically initialize a deformable model as close to the carpal tunnel in the given image as possible. The model subsequently deforms toward the tunnel boundary based on image intensity information, shape bending degree, and the geometry constraints of the carpal tunnel. After the deformation process, the carpal tunnel in the most proximal image is segmented and subsequently applied to a contour propagation step to extract the tunnel contours sequentially from the remaining cross-sectional images. MR volumes from 15 subjects were included in the validation experiments. Compared with the ground truth of two experts, our method showed good agreement on tunnel segmentations by an average margin of error within 1 mm and dice similarity coefficient above 0.9.

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“…These applications can be incorporated in video surveillance [22], human computer interaction [23], video and image retrieval [24], biometrics [25], and medical imaging [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

DTCTH: a discriminative local pattern descriptor for image classification

Rahman

et al. 2017

J Image Video Proc.

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Despite lots of effort being exerted in designing feature descriptors, it is still challenging to find generalized feature descriptors, with acceptable discrimination ability, which are able to capture prominent features in various image processing applications. To address this issue, we propose a computationally feasible discriminative ternary census transform histogram (DTCTH) for image representation which uses dynamic thresholds to perceive the key properties of a feature descriptor. The code produced by DTCTH is more stable against intensity fluctuation, and it mainly captures the discriminative structural properties of an image by suppressing unnecessary background information. Thus, DTCTH becomes more generalized to be used in different applications with reasonable accuracies. To validate the generalizability of DTCTH, we have conducted rigorous experiments on five different applications considering nine benchmark datasets. The experimental results demonstrate that DTCTH performs as high as 28.08% better than the existing state-of-the-art feature descriptors such as GIST, SIFT, HOG, LBP, CLBP, OC-LBP, LGP, LTP, LAID, and CENTRIST.

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Medical Image Analysis

Cited by 128 publications

References 0 publications

Short Report: A Sponge Phantom Provides a Homogeneous k-Space Pattern at MRI

Short Report: A Sponge Phantom Provides a Homogeneous k-Space Pattern at MRI

A Knowledge-Based Approach for Carpal Tunnel Segmentation from Magnetic Resonance Images

DTCTH: a discriminative local pattern descriptor for image classification

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