A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans

Ibrahim, Rabha W.; Hasan, Ali M.; Jalab, Hamid A.

doi:10.1016/j.cmpb.2018.05.031

Cited by 34 publications

(16 citation statements)

References 33 publications

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“…The different types of CT artefacts that come from the image reconstruction process is based on collecting a million independent detector measurements. Any error that may occur in these measurements will affect the scan and result in intensity variations between the consecutive slices of the reconstructed CT scan [ 19 ], therefore, prior to extracting handcrafted and deep features, a set of image processes are used to normalize the intensity and reduce the effect of intensity variations between CT slices [ 20 , 21 , 22 ], and also will speed up the CNN training and improve classification performances. In addition, these processes help to identify the boundaries of a lung from its surrounding thoracic tissue in an axial view of a CT scan as it contains a high number of insignificant pixels [ 23 ].…”

Section: Methodsmentioning

confidence: 99%

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Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Hasan

Al-Jawad

Jalab

et al. 2020

Entropy

Self Cite

136

105

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Many health systems over the world have collapsed due to limited capacity and a dramatic increase of suspected COVID-19 cases. What has emerged is the need for finding an efficient, quick and accurate method to mitigate the overloading of radiologists’ efforts to diagnose the suspected cases. This study presents the combination of deep learning of extracted features with the Q-deformed entropy handcrafted features for discriminating between COVID-19 coronavirus, pneumonia and healthy computed tomography (CT) lung scans. In this study, pre-processing is used to reduce the effect of intensity variations between CT slices. Then histogram thresholding is used to isolate the background of the CT lung scan. Each CT lung scan undergoes a feature extraction which involves deep learning and a Q-deformed entropy algorithm. The obtained features are classified using a long short-term memory (LSTM) neural network classifier. Subsequently, combining all extracted features significantly improves the performance of the LSTM network to precisely discriminate between COVID-19, pneumonia and healthy cases. The maximum achieved accuracy for classifying the collected dataset comprising 321 patients is 99.68%.

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Section: Methodsmentioning

confidence: 99%

“…Then, the current cell C t is updated by adding the output of the input gate, as given in Equation (14) [ 22 ]:

…”

Section: Methodsmentioning

confidence: 99%

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Hasan

Al-Jawad

Jalab

et al. 2020

Entropy

Self Cite

136

105

View full text Add to dashboard Cite

show abstract

“…These modifications may be lead to erroneous segmentation and feature extraction. Therefore, noise removing algorithms have been used to improve image quality [17][18][19][20]. In this study, the two-dimensional Bilateral filtering is used to denoise the MRI images.…”

Section: Image Preprocessingmentioning

confidence: 99%

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

Hasan

2018

IJEEI

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Segmentation of brain tumors in magnetic resonance imaging is a one of the most complex processes in medical image analysis because it requires a combination of data knowledge with domain knowledge to achieve highly results. Such that, the data knowledge refers to homogeneity, continuity, and anatomical texture. While the domain knowledge refers to shapes, location, and size of the tumor to be delineated. Due to recent advances in medical imaging technologies which produce a massive number of cross-sectional slices, this makes a manual segmentation process is a very intensive, timeconsuming and prone to inconsistences. In this study, an automated method for recognizing and segmenting the pathological area in MRI scans has been developed. First the dataset has been pre-processed and prepared by implementing a set of algorithms to standardize all collected samples. A particle swarm optimization is utilized to find the core of pathological area within each MRI slice. Finally, an active contour without edge method is utilized to extract the pathological area in MRI scan. Results reported on the collected dataset includes 50 MRI scans of pathological patients that was provided by Iraqi Center for Research and Magnetic Resonance of Al Imamain Al-Kadhimain Medical City in Iraq. The achieved accuracy of the proposed method was 92% compared with manual delineation.

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“…extremely random trees [8], support vector machines [9], [10], convolutional neural network [11], [12], deep neural networks [13], [14], [15], [16], Gaussian mixture models [17], fuzzy c-means clustering in semi-supervised context [18], [19], active contour models [20], [21], [22], tumor growth model [23]. For earlier solutions the reader is referred to the review paper by Gordillo et al [24].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Spectral Resolution Upon the Accuracy of Brain Tumor Segmentation from Multi-Spectral MRI Data

Győrfi

Fulop

Kovács

et al. 2020

2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Ensemble learning methods are frequently employed for brain tumor segmentation from multi-spectral MRI data. These techniques often require involving several hundreds of computed features for the characterization of the voxels, causing a rise in the necessary storage space by two order of magnitude. Processing such amounts of data also represents a serious computational burden. Under such circumstances it is useful to optimize the feature generation process. This paper proposes to establish the optimal spectral resolution of multispectral MRI data based feature values that allows for the best achievable brain tumor segmentation accuracy without causing unnecessary computational load and storage space waste. Experiments revealed that an 8-bit spectral resolution of the MRI-based feature data is sufficient to obtain the best possible accuracy of ensemble learning methods, while it allows for 50% reduction of the storage space required by the segmentation procedure, compared to the usually deployed featured encoding techniques.

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A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans

Cited by 34 publications

References 33 publications

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs in CT Scans Using Q-Deformed Entropy and Deep Learning Features

A Hybrid Approach of Using Particle Swarm Optimization and Volumetric Active Contour without Edge for Segmenting Brain Tumors in MRI Scan

The Effect of Spectral Resolution Upon the Accuracy of Brain Tumor Segmentation from Multi-Spectral MRI Data

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