Phase congruency map driven brain tumour segmentation

Szilágyi, Tünde; Brady, Michael; Berényi, Ervin

doi:10.1117/12.2082630

Cited by 4 publications

(6 citation statements)

References 18 publications

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“…After applying histogram differencing the order statistic filter and morphology has been applied as postprocessing to improve the result of segmentation. After successful segmentation of tumor region through histogram differencing using MRI images, the tumor size is than calculated through matrix manipulation in two different modes percentage and mm 2 . The values calculated in mm2 are then considered for KNN classification method to classify tumor into benign and malignant.…”

Section: Discussionmentioning

confidence: 99%

“…The comparison of both proposed and original technique is, that the technique proposed by the authors can also differentiate normal tissues from those which are affected by the tumor. Szilagyi et al [2] present two methods, one for features extraction from images provided by BRATS 2012 dataset and in the second method the features passed to the optimal decision tree for selecting the tumor region. After this, a level-set segmentation is used to separate tumor and edema in the image.…”

Section: Literaturementioning

confidence: 99%

“…In this research work, the KNN classification is used to classify the tumor values into two classes benign or malignant. For the classification, the values of tumor are generated from the segmented image showing the tumor tissues, the value represent different size of tumor in mm 2 . The values are generated for the complete dataset of the proposed images and consider for KNN classification.…”

Section: ………X N )mentioning

confidence: 99%

See 2 more Smart Citations

Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Nida-Ur-Rehman¹,

Ahmed²,

Masood³

et al. 2017

ijacsa

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Section: Discussionmentioning

confidence: 99%

Section: Literaturementioning

confidence: 99%

Section: ………X N )mentioning

confidence: 99%

See 1 more Smart Citation

Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Nida-Ur-Rehman¹,

Ahmed²,

Masood³

et al. 2017

ijacsa

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“…This deformable model has been introduced with a number of variants. [14][15][16] Recently, researchers aim to improve it by changing the additional external force, the energy function transformation, or the contour and region term guiding the level set propagation. Indeed, the edge indicator terms have also been modified for boundaries refinement.…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers 14,17 have used the local phase information instead of the gradient to stop the curve propagation. 16 Joshi et al 14 proposed a segmentation method based on the local phase information, for the detection of colorectal cancer from MRI images. Zaouche et al 3 proposed a semiautomatic LGG segmentation using specially designed spatial phase information.…”

Section: Introductionmentioning

confidence: 99%

Automatic three‐dimensional detection and volume estimation of low‐grade gliomas

Boudrioua

Aloui

Solaiman

et al. 2021

Int J Imaging Syst Tech

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Brain tumor segmentation is routinely needed in diagnostic radiology. Automatic volume estimation has been used for diagnosis improvement and treatment process. This article presents a new approach for tumor volume estimation, using a variational level set method. The new adapted representation of intensity allows the method to be efficient in the region of interest identification, regardless the shape, size, and location of the tumor. For the estimation of an optimal bounding box, a fuzzy preference optimization model is used. The proposed approach is suitable for the zero level set initialization as well as for the reduction of the processing area, which ultimately speeds up the curve evolution. Moreover, tumor contours are determined using the hybrid level set technique, which combines the gradient and local phase information as an edge indicator term. Such an approach is robust to attenuation and intensity inhomogeneity. The proposed method is evaluated using a set of real and synthetic images. Our method achieved a performance of 96% accuracy, with an average execution time of 4.75 seconds. The proposed method is fast, accurate, and does not require training data or prior knowledge. With such experimental results, our approach outperforms 18 state-of-the-art methods.

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Magnetic resonance image-based brain tumour segmentation methods: A systematic review

2022

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Background Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development. Purpose To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score). Statistical tests We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation. Conclusion U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

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Phase congruency map driven brain tumour segmentation

Cited by 4 publications

References 18 publications

Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Segmentation of Brain Tumor in Multimodal MRI using Histogram Differencing & KNN

Automatic three‐dimensional detection and volume estimation of low‐grade gliomas

Magnetic resonance image-based brain tumour segmentation methods: A systematic review

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