Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma

Mohammed, Mazin Abed; Ghani, Mohd Khanapi Abd; Hamed, Raed I.; Ibrahim, Dheyaa Ahmed; Abdullah, Mohamad Khir

doi:10.1016/j.jocs.2017.03.026

Cited by 47 publications

(31 citation statements)

References 22 publications

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“…[9][10][11] The underlying principle is that the arrangement of local intensity gradients or edge directions can be used to describe the shape and appearance of a local object, regardless of accurate data about equivalent gradient or edge positions. [9][10][11] The underlying principle is that the arrangement of local intensity gradients or edge directions can be used to describe the shape and appearance of a local object, regardless of accurate data about equivalent gradient or edge positions.…”

Section: A-feature Extractionmentioning

confidence: 99%

“…This method is designed to quantify gradient orientation occurrences in localized image sections. [9][10][11] The underlying principle is that the arrangement of local intensity gradients or edge directions can be used to describe the shape and appearance of a local object, regardless of accurate data about equivalent gradient or edge positions. The practical application involves separating the image window to minute regions commonly referred as cells.…”

Section: A-feature Extractionmentioning

confidence: 99%

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Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Arunkumar

Mohammed

Mostafa

et al. 2018

Concurrency and Computation

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124

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The accuracy of brain tumor diagnosis based on medical images is greatly affected by the segmentation process. The segmentation determines the tumor shape, location, size, and texture. In this study, we proposed a new segmentation approach for brain tissues using MR images. The method includes three computer vision fiction strategies which are enhancing images, segmenting images, and filtering out non ROI based on the texture and HOG features. A fully automatic model-based trainable segmentation and classification approach for MRI brain tumour using artificial neural networks to precisely identifying the location of the ROI. Therefore, the filtering out non ROI process have used in view of histogram investigation to avert the non ROI and select the correct object in brain MRI. However, identification the tumor kind utilizing the texture features. A total of 200 MRI cases are utilized for the comparing between automatic and manual segmentation procedure. The outcomes analysis shows that the fully automatic model-based trainable segmentation over performs the manual method and the brain identification utilizing the ROI texture features. The recorded identification precision is 92.14%, with 89 sensitivity and 94 specificity.

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Section: A-feature Extractionmentioning

confidence: 99%

Section: A-feature Extractionmentioning

confidence: 99%

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Arunkumar

Mohammed

Mostafa

et al. 2018

Concurrency and Computation

Self Cite

124

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“…Nowadays, risk-classification tools have been developing including machine learning, artificial neural network, and nomogram and etc. (19)(20)(21)(22). These techniques are now more and more used for NPC patients in helping clinical decisionmaking.…”

Section: Introductionmentioning

confidence: 99%

Nomogram Predicting the Benefits of Adding Concurrent Chemotherapy to Intensity-Modulated Radiotherapy After Induction Chemotherapy in Stages II–IVb Nasopharyngeal Carcinoma

Liu

Sun

et al. 2020

Front. Oncol.

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“…For example, [ 6 ] proposed a semiautomatic workflow, including masking, thresholding, and seed growing, to segment NPC from both T2-w and CET1-w from 7 patients to help radiation therapy. [ 7 ] proposed an automatic NPC segmentation method based on region growing and clustering and used neural networks to classify suspicious regions. [ 8 ] proposed to use a genetic algorithm for selecting the informative features and the support vector machine for classifying NPC.…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences

Wang

Han

et al. 2020

Computational and Mathematical Methods in Medicine

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Nasopharyngeal carcinoma (NPC) is the most common malignant tumor of the nasopharynx. The delicate nature of the nasopharyngeal structures means that noninvasive magnetic resonance imaging (MRI) is the preferred diagnostic technique for NPC. However, NPC is a typically infiltrative tumor, usually with a small volume, and thus, it remains challenging to discriminate it from tightly connected surrounding tissues. To address this issue, this study proposes a voxel-wise discriminate method for locating and segmenting NPC from normal tissues in MRI sequences. The located NPC is refined to obtain its accurate segmentation results by an original multiviewed collaborative dictionary classification (CODL) model. The proposed CODL reconstructs a latent intact space and equips it with discriminative power for the collective multiview analysis task. Experiments on synthetic data demonstrate that CODL is capable of finding a discriminative space for multiview orthogonal data. We then evaluated the method on real NPC. Experimental results show that CODL could accurately discriminate and localize NPCs of different volumes. This method achieved superior performances in segmenting NPC compared with benchmark methods. Robust segmentation results show that CODL can effectively assist clinicians in locating NPC.

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Artificial neural networks for automatic segmentation and identification of nasopharyngeal carcinoma

Cited by 47 publications

References 22 publications

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

Nomogram Predicting the Benefits of Adding Concurrent Chemotherapy to Intensity-Modulated Radiotherapy After Induction Chemotherapy in Stages II–IVb Nasopharyngeal Carcinoma

A Collaborative Dictionary Learning Model for Nasopharyngeal Carcinoma Segmentation on Multimodalities MR Sequences

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