A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Mohan, Jitendra; Krishnaveni, V.; Guo, Yanhui

doi:10.2478/msr-2013-0027

Cited by 31 publications

(21 citation statements)

References 33 publications

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“…Every event in the neutrosophy set (NS) has a certain degree of truth (T), indeterminacy (I), and falsity (F), which are independent from each other. Previously reported studies have demonstrated the role of NS in image processing [26,27].…”

Section: Neutrosophic Imagesmentioning

confidence: 99%

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

2018

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This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach.

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Section: Neutrosophic Imagesmentioning

confidence: 99%

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

2018

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“…In this paper, a new hybrid algorithm for binarization of degraded Arabic manuscript image is proposed. It combines the famous adaptive algorithm of Sauvola's (8) and a NS binarization algorithm of [9] into a hybrid one. Neutrosophic set (NS) approach is quite new and have been useful for various image processing tasks such as segmentation, thresholding, and denoising [7].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Binarization Technique for Images of Historical Arabic Manuscripts

Hassanien¹,

Abdelfattah²,

Amin³

et al. 2015

SIC

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Abstract:In this paper, a novel binarization approach based on neutrosophic sets and sauvola's approach is presented. This approach is used for historical Arabic manuscript images which have problems with types of noise. The input RGB image is changed into the NS domain, which is shown using three subsets, namely, the percentage of indeterminacy in a subset, the percentage of falsity in a subset and the percentage of truth in a subset. The entropy in NS is used for evaluating the indeterminacy with the most important operation "λ mean" operation in order to minimize indeterminacy which can be used to reduce noise. Finally, the manuscript is binarized using an adaptive thresholding technique. The main advantage of the proposed approach is that it preserves weak connections and provides smooth and continuous strokes. The performance of the proposed approach is evaluated both objectively and subjectively against standard databases and manually collected data base. The proposed method gives high results compared with other famous binarization approaches.

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“…Memberships are calculated based on the relative distance (Euclidean distance) of the object o j to the centroids using Equation (5). After the memberships of all objects have been found, the centroids of the clusters are calculated using Equation (4). The process stops when the centroids from the previous iteration are identical to those generated in the current iteration [6].…”

Section: Fuzzy C-means Clustering (Fcm)mentioning

confidence: 99%

“…(any color except white and black). According to this theory every idea <A> tends to be neutralized and balanced by <Anti-A> and <Non-A> ideas [4].…”

Section: Introductionmentioning

confidence: 99%

Neutrosophic Sets and Fuzzy C-Means Clustering for Improving CT Liver Image Segmentation

Anter

Hassanien

ElSoud

et al. 2014

Advances in Intelligent Systems and Computing

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Abstract. In this paper, an improved segmentation approach based on Neutrosophic sets (NS) and fuzzy c-mean clustering (FCM) is proposed. An application of abdominal CT imaging has been chosen and segmentation approach has been applied to see their ability and accuracy to segment abdominal CT images. The abdominal CT image is transformed into NS domain, which is described using three subsets namely; the percentage of truth in a subset T , the percentage of indeterminacy in a subset I, and the percentage of falsity in a subset F . The entropy in NS is defined and employed to evaluate the indeterminacy. Threshold for NS image is adapted using Fuzzy C-mean algorithm. Finally, abdominal CT image is segmented and liver parenchyma is selected using connected component algorithm. The proposed approach denoted as NS-FCM and compared with FCM using Jaccard Index and Dice Coefficient. The experimental results demonstrate that the proposed approach is less sensitive to noise and performs better on nonuniform CT images.

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A New Neutrosophic Approach of Wiener Filtering for MRI Denoising

Cited by 31 publications

References 33 publications

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

A Novel Hybrid Binarization Technique for Images of Historical Arabic Manuscripts

Neutrosophic Sets and Fuzzy C-Means Clustering for Improving CT Liver Image Segmentation

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