A Novel Neutrosophic Method for Automatic Seed Point Selection in Thyroid Nodule Images

Haji, S O; Yousif, Raghad Zuhair

doi:10.1155/2019/7632308

Cited by 8 publications

(8 citation statements)

References 35 publications

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“…All considered components are autonomous from each other. A pixel ( P ) of an image in the neutrosophic domain is characterized as P ( T , I , F ) [ 26 – 28 , 30 , 34 ] and belongs to set A in the following way: it is t % true membership function in the bright pixel set, i % indeterminacy membership function in the set, and f % a falsity-membership function in the set, where t varies in T , i varies in I , and f varies in F . There is a valuation for each component in [0, 1].…”

Section: Methodsmentioning

confidence: 99%

“…Neutrosophy is a branch of philosophy, introduced by F. Smarandache in 1980, which generalized dialectics and studied the origin, nature, and scope of neutralities, in addition to their interactions with numerous ideational spectra [ 24 ]. In neutrosophy theory, every event has a definite degree of truth ( T ), falsity ( F ), and indeterminacy ( I ) that have to be considered independently from each other [ 23 , 25 – 28 ]. Therefore, { A } is an idea, theory, event, concept, or entity; {Anti − A } is the opposite of { A }; and the neutrality {Neut − A } means neither { A } nor {Anti − A }, that is, the neutrality between the two extremes [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Wady

Yousif

Hasan

2020

BioMed Research International

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Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Wady

Yousif

Hasan

2020

BioMed Research International

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“…Various methodologies proposed in [25][26][27][28] are used for comparative analysis. In this section, comparative analysis is carried out based on the ROI which is shown in Table 1, further the comparison is shown considering two images Table 1 comprises three column, first column shows the original image given in dataset, second column shows the ground truth of original image, third column is ROI achieved through the existing model [28] and fourth column shows the ROI achieved for Improvised U-Net model. Further through the Table 1 we observed there is huge difference in the ROI structure of existing model and ground truth, this is occurred due to the image quality used in the segmentation process of existing model whereas our model is nearer to the ground truth.…”

Section: Roi Comparisonmentioning

confidence: 99%

Ultrasound image segmentation through deep learning based improvised U-Net

Shenoy

Jatti²

2021

IJEECS

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Thyroid nodule are fluid or solid lump that are formed within human’s gland and most thyroid nodule doesn’t show any symptom or any sign; moreover there are certain percentage of thyroid gland are cancerous and which could lead human into critical situation up to death. Hence, it is one of the important type of cancer and also it is important for detection of cancer. Ultrasound imaging is widely popular and frequently used tool for diagnosing thyroid cancer, however considering the wide application in clinical area such estimating size, shape and position of thyroid cancer. Further, it is important to design automatic and absolute segmentation for better detection and efficient diagnosis based on US-image. Segmentation of thyroid gland from the ultrasound image is quiet challenging task due to inhomogeneous structure and similar existence of intestine. Thyroid nodule can appear anywhere and have any kind of contrast, shape and size, hence segmentation process needs to designed carefully; several researcher have worked in designing the segmentation mechanism, however most of them were either semi-automatic or lack with performance metric, however it was suggested that U-Net possesses great accuracy. Hence, in this paper, we proposed improvised U-Net which focuses on shortcoming of U-Net, the main aim of this research work is to find the probable Region of interest and segment further. Furthermore, we develop High level and low-level feature map to avoid the low-resolution problem and information; later we develop dropout layer for further optimization. Moreover proposed model is evaluated considering the important metrics such as accuracy, Dice Coefficient, AUC, F1-measure and true positive; our proposed model performs better than the existing model.

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“…Neutrosophic method for Thyroid Nodule Images [25] A test pixel that belongs to the suspicious region is termed as seed. The accurate seed choice is required, due to the haphazard growth of cells that might be malignant or benign [26] as the region growing outcome is sensitive to the underlying seeds so for as image segmentation is concerned.…”

Section: B Seed Point Choice For Image Segmentation Usingmentioning

confidence: 99%

An Ideal Technique for Decision-Making Problems for Uncertain Data and Its Application In Medical Science

Arora¹

2019

IJITEE

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For representing and manipulating uncertain information like fuzzy, incomplete, inconsistent or imprecise, Neutrosophic relation database model is a more general platform, in the human decision-making process. Neutrosophic sets can easily handle real world problems. A new correlation method is introduced in this paper to construct similarity measure, by which decision making problem that exist in real world situation can be easily handled in regard of multiple existing criteria’s or incomplete or inconsistent information. The selection of the best option of alternative can be done by ranking all the other options as per similarity measure depending on concept of similarity. Later in this paper, an explanatory example is given of the proposed method and the comparison results are also presented to show the effective output.. The application in certain domains of medical diagnosis problems having multiple criteria’s in decision making are also discussed in the end of the proposed method.

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A Novel Neutrosophic Method for Automatic Seed Point Selection in Thyroid Nodule Images

Cited by 8 publications

References 35 publications

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

Ultrasound image segmentation through deep learning based improvised U-Net

An Ideal Technique for Decision-Making Problems for Uncertain Data and Its Application In Medical Science

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