Brain tumor segmentation with Vander Lugt correlator based active contour

Essadike, Abdelaziz; Ouabida, Elhoussaine; Bouzid, Abdenbi

doi:10.1016/j.cmpb.2018.04.004

Cited by 29 publications

(5 citation statements)

References 23 publications

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“…Both theory and experiments confirmed the invariance to distortions of the compact integrated setup proposed for the optical correlator, with promising future applications in AI and machine vision. Essadike et al [ 114 ] used for the first time a VLC correlator in a medical application with a novel automatic process of brain tumor segmentation. The tissue regions with abnormalities were automatically detected using a numerical simulation of the VLC, with a tumor filter adapted to all types of brain tumors, with a special focus on the most aggressive one, glioblastoma.…”

Section: Recent Applications and Implementations Of Optical Correlatorsmentioning

confidence: 99%

Optical Correlators for Cryptosystems and Image Recognition: A Review

Drăgulinescu

2023

Sensors

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Optical correlators are efficient optical systems that have gained a wide range of applications both in image recognition and encryption, due to their special properties that benefit from the optoelectronic setup instead of an all-electronic one. This paper presents, to the best of our knowledge, the most extensive review of optical correlators to date. The main types are overviewed, together with their most frequent applications in the newest contributions, ranging from security uses in cryptosystems, to medical and space applications, femtosecond pulse detection and various other image recognition proposals. The paper also includes a comparison between various optical correlators developed recently, highlighting their advantages and weaknesses, to gain a better perspective towards finding the best solutions in any specific domain where these devices might prove highly efficient and useful.

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Section: Recent Applications and Implementations Of Optical Correlatorsmentioning

confidence: 99%

Optical Correlators for Cryptosystems and Image Recognition: A Review

Drăgulinescu

2023

Sensors

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“…Another relatively similar idea is based on the prominent characteristics of brain tumors in medical images, which is to segment brain tumors based on tumor contour by feature extraction of brain tumor boundary information (Bauer et al, 2013). Essadike et al (2018) determined the initial contour by using a tumor filter in analog optics and utilized this initial contour to define the active contour model to determine the tumor boundary. Ma et al (2018) combined random forest and active contour models to automatically infer glioma structure from multimodal volumetric MR images and proposed a new multiscale patch-driven active contour model to refine the results using sparse representation techniques.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolutional Neural Network With a Multi-Scale Attention Feature Fusion Module for Segmentation of Multimodal Brain Tumor

Yang

et al. 2021

Front. Neurosci.

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As a non-invasive, low-cost medical imaging technology, magnetic resonance imaging (MRI) has become an important tool for brain tumor diagnosis. Many scholars have carried out some related researches on MRI brain tumor segmentation based on deep convolutional neural networks, and have achieved good performance. However, due to the large spatial and structural variability of brain tumors and low image contrast, the segmentation of MRI brain tumors is challenging. Deep convolutional neural networks often lead to the loss of low-level details as the network structure deepens, and they cannot effectively utilize the multi-scale feature information. Therefore, a deep convolutional neural network with a multi-scale attention feature fusion module (MAFF-ResUNet) is proposed to address them. The MAFF-ResUNet consists of a U-Net with residual connections and a MAFF module. The combination of residual connections and skip connections fully retain low-level detailed information and improve the global feature extraction capability of the encoding block. Besides, the MAFF module selectively extracts useful information from the multi-scale hybrid feature map based on the attention mechanism to optimize the features of each layer and makes full use of the complementary feature information of different scales. The experimental results on the BraTs 2019 MRI dataset show that the MAFF-ResUNet can learn the edge structure of brain tumors better and achieve high accuracy.

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“…An improved performance utilizing local and global image information for contour detection into a hierarchical region tree [27]. Essadike suggested Van der Lugt correlator-based initial contour to assist an active contour model in extracting tumor boundaries [28].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

et al. 2020

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Brain tumor detection and its analysis are essential in medical diagnosis. The proposed work focuses on segmenting abnormality of axial brain MR DICOM slices, as this format holds the advantage of conserving extensive metadata. The axial slices presume the left and right part of the brain is symmetric by a Line of Symmetry (LOS). A semi-automated system is designed to mine normal and abnormal structures from each brain MR slice in a DICOM study. In this work, Fuzzy clustering (FC) is applied to the DICOM slices to extract various clusters for different k. Then, the best-segmented image that has high inter-class rigidity is obtained using the silhouette fitness function. The clustered boundaries of the tissue classes further enhanced by morphological operations. The FC technique is hybridized with the standard image post-processing techniques such as marker controlled watershed segmentation (MCW), region growing (RG), and distance regularized level sets (DRLS). This procedure is implemented on renowned BRATS challenge dataset of different modalities and a clinical dataset containing axial T2 weighted MR images of a patient. The sequential analysis of the slices is performed using the metadata information present in the DICOM header. The validation of the segmentation procedures against the ground truth images authorizes that the segmented objects of DRLS through FC enhanced brain images attain maximum scores of Jaccard and Dice similarity coefficients. The average Jaccard and dice scores for segmenting tumor part for ten patient studies of the BRATS dataset are 0.79 and 0.88, also for the clinical study 0.78 and 0.86, respectively. Finally, 3D visualization and tumor volume estimation are done using accessible DICOM information.

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Brain tumor segmentation with Vander Lugt correlator based active contour

Cited by 29 publications

References 23 publications

Optical Correlators for Cryptosystems and Image Recognition: A Review

Optical Correlators for Cryptosystems and Image Recognition: A Review

Deep Convolutional Neural Network With a Multi-Scale Attention Feature Fusion Module for Segmentation of Multimodal Brain Tumor

An Efficient Hybrid Fuzzy-Clustering Driven 3D-Modeling of Magnetic Resonance Imagery for Enhanced Brain Tumor Diagnosis

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