MRI Slice Segmentation and 3D Modelling of Temporomandibular Joint Measured by Microscopic Coil

Smirg, Ondrej; Liberda, Ondřej; Smékal, Zdeněk; Šprláková-Puková, Andrea

doi:10.2478/v10048-012-0018-5

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Today's CT scanners can produce very thin slices, but volume averaging can still influence the spatial resolution of the image. 3,6 The accurate reconstruction process of geometry depends on the type of scanner, 4,5 method of segmentation, 10,11 software algorithm 11,12 and manufacturing technology. [13][14][15] Currently, scientists are working toward reasonable accuracy at the stage of processing 2D data 2,4,6 and on methods to improve the quality of medical manufacturing models using Rapid Prototyping (RP) and Computerized Numerical Control (CNC) techniques.…”

Section: Introductionmentioning

confidence: 99%

The influence of change in slice thickness on the accuracy of reconstruction of cranium geometry

Budzik

Turek

Traciak

2017

Proc Inst Mech Eng H

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The article presents a comparative study of change in slice thickness on the accuracy of reconstruction of cranium geometry. Research was performed on 10 different patients. Digital Imaging and Communications in Medicine data were obtained on the Siemens Somatom Sensation Open 40 scanner. At the stage of reconstruction, the same parameters were utilized, while only slice thickness was changed. Modeling with voxel dimensions of 0.4 mm × 0.4 mm × 2.4 mm was chosen as the gold standard over the modeling approach comprising voxel dimensions of 0.4 mm × 0.4 mm × 4.8 mm. The influence of layer thickness on the accuracy of cranium geometry is very similar for the 10 presented patients. The average results show a distribution with a positive skew and kurtosis. The value of skewness is 0.284 (small asymmetry) and kurtosis is 3.746 (a distribution more peaked). Based on 95% confidence, the changes in layer thickness from 2.4 to 4.8 mm generated errors reconstructing the geometry of the cranium by 0.516 mm ± 1.345 mm. The presented research highlights new opportunities to control deviations at the stage of data processing and modeling geometry of the cranium.

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Section: Introductionmentioning

confidence: 99%

The influence of change in slice thickness on the accuracy of reconstruction of cranium geometry

Budzik

Turek

Traciak

2017

Proc Inst Mech Eng H

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“…However, most of the existing methods have used thresholding [1, 2, 9 10 and 11] based methods for segmentation. Moreover, there are many methods like Watershed Transform [3,7], Region growing algorithm [4], Model-based methods, edge-tracking approach [17], 3D modelling [22], and Multiscale morphological segmentation techniques [18,5].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

Khan

Kannan

2014

Measurement Science Review

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The performance of assessment in medical image segmentation is highly correlated with the extraction of anatomic structures from them, and the major task is how to separate the regions of interests from the background and soft tissues successfully. This paper proposes a fuzzy logic based bitplane method to automatically segment the background of images and to locate the region of interest of medical images. This segmentation algorithm consists of three steps, namely identification, rule firing, and inference. In the first step, we begin by identifying the bitplanes that represent the lungs clearly. For this purpose, the intensity value of a pixel is separated into bitplanes. In the second step, the triple signum function assigns an optimum threshold based on the grayscale values for the anatomical structure present in the medical images. Fuzzy rules are formed based on the available bitplanes to form the membership table and are stored in a knowledge base. Finally, rules are fired to assign final segmentation values through the inference process. The proposed new metrics are used to measure the accuracy of the segmentation method. From the analysis, it is observed that the proposed metrics are more suitable for the estimation of segmentation accuracy. The results obtained from this work show that the proposed method performs segmentation effectively for the different classes of medical images.

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“…Traditional clinical 1.5 T magnets are being replaced by 3 T, while research magnets of 7 T, 8 T, and 9.4 T referred to as high field magnets are being used for animal and human experiments [7]- [14]. The great appeal of high field MRI is its improved image quality and resolution [15].…”

Section: Introductionmentioning

confidence: 99%

Comparing Saddle, Slotted-tube and Parallel-plate Coils for Magnetic Resonance Imaging

Nespor

Bartušek

Dokoupil

2014

Measurement Science Review

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The paper is concerned with a comparison of the properties of RF coils of three configurations for MRI measurements on small animals. In comparison with the classical saddle coil the proposed modification of slotted-tube coil exhibits identical homogeneity of B 1 field in a larger space. The parallel-plate coil has a satisfactory homogeneity of B 1 field over the whole internal space. The signal-to-noise ratio measured for all three coils is roughly the same and is given by the magnitude of RF pre-amplifier noise. As the slotted-tube and parallel-plate coils have a lower inductance compared with the saddle coil, they can be tuned to resonance on the 200 MHz frequency even at larger dimensions. The results show that the parallel-plate coil has very good properties for the measurement of small animals.

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MRI Slice Segmentation and 3D Modelling of Temporomandibular Joint Measured by Microscopic Coil

Cited by 6 publications

References 17 publications

The influence of change in slice thickness on the accuracy of reconstruction of cranium geometry

The influence of change in slice thickness on the accuracy of reconstruction of cranium geometry

Intelligent Segmentation of Medical Images Using Fuzzy Bitplane Thresholding

Comparing Saddle, Slotted-tube and Parallel-plate Coils for Magnetic Resonance Imaging

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