Brain tumor segmentation using wavelet Multi-resolution expectation maximization algorithm

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

doi:10.1109/intelcis.2017.8260030

Cited by 3 publications

(2 citation statements)

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“…These methods could be very simple such as Thresholding, or region-based as region growing and watershed, or supervised classification as in artificial neural networks, or statistical-based such as expectation maximization and its modifications [22]- [26]. We used wavelet multi-resolution expectation maximization algorithm (WMEM) [27] proposed by Salem [28], [29] for tumor segmentation.…”

Section: Proposed Methodsmentioning

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

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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: Proposed Methodsmentioning

confidence: 99%

“…It is computed as Area/ConvexArea. It was found that tumors have higher solidity than the skull, so we selected the high solidity to get a binary image that only contains the tumor to be used in the reconstruction step [27].…”

Section: Tumor Extractionmentioning

confidence: 99%