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  1. A new deformable model based on fractional Wright energy function for tumor segmentation of volumetric brain MRI scans
    Ibrahim RW, Hasan AM, Jalab HA
    Comput Methods Programs Biomed, 2018 Sep;163:21-28.
    PMID: 30119853 DOI: 10.1016/j.cmpb.2018.05.031
    BACKGROUND AND OBJECTIVES: The MRI brain tumors segmentation is challenging due to variations in terms of size, shape, location and features' intensity of the tumor. Active contour has been applied in MRI scan image segmentation due to its ability to produce regions with boundaries. The main difficulty that encounters the active contour segmentation is the boundary tracking which is controlled by minimization of energy function for segmentation. Hence, this study proposes a novel fractional Wright function (FWF) as a minimization of energy technique to improve the performance of active contour without edge method.

    METHOD: In this study, we implement FWF as an energy minimization function to replace the standard gradient-descent method as minimization function in Chan-Vese segmentation technique. The proposed FWF is used to find the boundaries of an object by controlling the inside and outside values of the contour. In this study, the objective evaluation is used to distinguish the differences between the processed segmented images and ground truth using a set of statistical parameters; true positive, true negative, false positive, and false negative.

    RESULTS: The FWF as a minimization of energy was successfully implemented on BRATS 2013 image dataset. The achieved overall average sensitivity score of the brain tumors segmentation was 94.8 ± 4.7%.

    CONCLUSIONS: The results demonstrate that the proposed FWF method minimized the energy function more than the gradient-decent method that was used in the original three-dimensional active contour without edge (3DACWE) method.

  2. MRI Brain Classification Using the Quantum Entropy LBP and Deep-Learning-Based Features
    Hasan AM, Jalab HA, Ibrahim RW, Meziane F, Al-Shamasneh AR, Obaiys SJ
    Entropy (Basel), 2020 Sep 15;22(9).
    PMID: 33286802 DOI: 10.3390/e22091033
    Brain tumor detection at early stages can increase the chances of the patient's recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral part in the diagnosis and treatment processes. In this study, we generalize the concept of entropy difference defined in terms of Marsaglia formula (usually used to describe two different figures, statues, etc.) by using the quantum calculus. Then we employ the result to extend the local binary patterns (LBP) to get the quantum entropy LBP (QELBP). The proposed study consists of two approaches of features extractions of MRI brain scans, namely, the QELBP and the deep learning DL features. The classification of MRI brain scan is improved by exploiting the excellent performance of the QELBP-DL feature extraction of the brain in MRI brain scans. The combining all of the extracted features increase the classification accuracy of long short-term memory network when using it as the brain tumor classifier. The maximum accuracy achieved for classifying a dataset comprising 154 MRI brain scan is 98.80%. The experimental results demonstrate that combining the extracted features improves the performance of MRI brain tumor classification.
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