3D Visualization of Brain Tumors Using MR Images: A Survey

El-Torky, Dina Mohammed Sherif; Al-Berry, Maryam N.; Salem, Mohammed A.-M.; Roushdy, Mohamed

doi:10.2174/1573405614666180111142055

Cited by 10 publications

(7 citation statements)

References 28 publications

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“…A polygonal representation of the object is formed by extracting a zero-set using a contour algorithm. Therefore, surface reconstruction from a point cloud is simplified to computing the suitable function f which equals zero for the sampled points and does not equal zero for the rest of the points [3]. The most popular zero-set method is the marching cubes algorithm which was proposed by Lorensen and Cline [9].…”

Section: Zero-set Methodsmentioning

confidence: 99%

“…3D reconstruction is implemented using various methods that are based on diverse concepts. In this section, we will discuss the most widely-used methods [3].…”

Section: Related Workmentioning

confidence: 99%

“…However, the MRI yields a set of cross-sectional 2D images that need further interpretation from an expert radiologist to construct a 3D model of the tumor from these slices. This can result in an inaccurate diagnosis because the analysis depends on the experience of the radiologist [3]- [5]. Moreover, to avoid the slices from interference with each other (crosstalk artifacts), there must be an inter- slice spacing during the image acquisition [6] which causes loss of details in the gap spaces and extra effort from the radiologist to analyse the images.…”

Section: Introductionmentioning

confidence: 99%

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Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

El-Torky

Roushdy

Al-Berry

et al. 2022

IJECE

Self Cite

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<span>3D visualization plays an essential role in medical diagnosis and setting treatment plans especially for brain cancer. There have been many attempts for brain tumor reconstruction and visualization using various techniques. However, this problem is still considered unsolved as more accurate results are needed in this critical field. In this paper, a sequence of 2D slices of brain magnetic resonance Images was used to reconstruct a 3D model for the brain tumor. The images were automatically segmented using a wavelet multi-resolution expectation maximization algorithm. Then, the inter-slice gaps were interpolated using the proposed modified shape-based interpolation method. The method involves three main steps; transferring the binary tumor images to distance images using a suitable distance function, interpolating the distance images using cubic spline interpolation and thresholding the interpolated values to get the reconstructed slices. The final tumor is then visualized as a 3D isosurface. We evaluated the proposed method by removing an original slice from the input images and interpolating it, the results outperform the original shape-based interpolation method by an average of 3% reaching 99% of accuracy for some slice images.</span>

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Section: Zero-set Methodsmentioning

confidence: 99%

“…3D reconstruction is implemented using various methods that are based on diverse concepts. In this section, we will discuss the most widely-used methods [3].…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

El-Torky

Roushdy

Al-Berry

et al. 2022

IJECE

Self Cite

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show abstract

“…In this work, we have used only point clouds data inputs. Before the ToF datasets are registered, the data quality is improved by applying a distance correction followed by Kalman filtering [36]. Furthermore, temporal surface of phantom (or patient) is separated in a feature extraction step.…”

Section: Methodsmentioning

confidence: 99%

Registration and fusion of 3D surface data from CT and ToF camera for position verification in radiotherapy

et al. 2019

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The purpose of this study is to introduce a low-cost method for verification of patient positioning before and during irradiation in radiotherapy. The experiments strategy is implemented by using a phantom like a human head in two modes: with and without thermoplastic masks. Radiotherapy is an effective clinical option for topical tumors treatment. Ability to the monitoring and control the patient's position during treatment is among the important items in radiotherapy. We registered to modality of 3D surface image in point cloud format, dynamic 3D image of time-of-flight camera (includes outer shape of head) and static 3D image of CT slice sectional by coherent point drift algorithm. Accuracy of 3D surface registration was calculated by average symmetrical surface distances (ASD) that it is a new method based on surface distance. The mean ASD calculated for rotation around X and Y axes of phantom is 2.62 ± 0.97 mm with immobilized thermoplastic mask and accuracy be increased to 1.69 ± 0.88 mm in non-thermoplastic mode. By performing paired T tests for ASDs, it was found that the use of a thermoplastic mask does not make a significant difference in the data acquisition. By developing this research to image fusion of that's 3D data sets, we will design to augmented reality in radiotherapy for verification of treatment planning and compare it with the electronic portal imaging device.

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“…These facilitate the treatment center to accomplish an apparent vision of the human organ related problems. So, these 3D reconstruction techniques are great deal and provide extra precision without considering every 2D image from the original layer 21,22 . Nowadays, the latest advancement in 3D image reconstruction is spread all over the world and it has potential to become a very attractive field of research due to its extensive progress to acquire a 3D object mode along multi‐two dimensional views.…”

Section: Introductionmentioning

confidence: 99%

An affinity propagated clustering aided computerized Inherent Seeded Region Growing and Deep learned Marching Cubes Algorithm (ISRG‐DMCA) based three dimensional image reconstruction approach

Joseph

Dennisan

2022

Int J Imaging Syst Tech

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In this manuscript, the novel three dimensional (3D) image reconstruction approach based on affinity propagated clustering aided computerized Inherent Seeded Region Growing and Deep learned Marching Cubes Algorithm (ISRG-DMCA) is proposed. The major purpose of this manuscript is to divide the brain tumor based on Shapelets. Here, the information about the shape/depth that can be obtained in every two dimensional (2D) image on the image stack is handled to acquire a 3D reconstruction, which provides high accurate 3D view of tumor Region of Interest (ROI). Then, the 3D model is rendered with the help of the proposed Deep learned Marching Cubes Algorithm (Deep MCA) at 3D reconstruction technique. The performance of the proposed method is executed in MATLAB. The simulation results show that the proposed ISRG-DMCA algorithm attains a higher detection rate 14.117%, 5.435%, higher accuracy rate 9.556%, 26.41% and lower execution time 66.667%, 75%, compared with the existing methods, like Improved Marching Cubes Algorithm (IMCA), Improved CNN-CRF method, respectively. In the proposed ISRG-DMCA method, the volume of the tumor has a length of 2.56 mm. Finally, the simulation outcomes demonstrate that the proposed ISRG-DMCA method can be able to find the optimal solutions efficiently and accurately.

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3D Visualization of Brain Tumors Using MR Images: A Survey

Cited by 10 publications

References 28 publications

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

Brain tumor visualization for magnetic resonance images using modified shape-based interpolation method

Registration and fusion of 3D surface data from CT and ToF camera for position verification in radiotherapy

An affinity propagated clustering aided computerized Inherent Seeded Region Growing and Deep learned Marching Cubes Algorithm (ISRG‐DMCA) based three dimensional image reconstruction approach

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