Yanhui Guo

A novel breast ultrasound image segmentation algorithm based on neutrosophic similarity score and level set

Breast ultrasound (BUS) image segmentation is a challenging task due to the speckle noise, poor quality of the ultrasound images and size and location of the breast lesions. In this paper, we propose a new BUS image segmentation algorithm based on neutrosophic similarity score (NSS) and level set algorithm. At first, the input BUS image is transferred to the NS domain via three membership subsets T, I and F, and then, a similarity score NSS is defined and employed to measure the belonging degree to the true tumor region. Finally, the level set method is used to segment the tumor from the background tissue region in the NSS image. Experiments have been conducted on a variety of clinical BUS images. Several measurements are used to evaluate and compare the proposed methods performance. The experimental results demonstrate that the proposed method is able to segment the BUS images effectively and accurately.

A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering

This paper presents a novel image segmentation algorithm based on neutrosophic c-means clustering and indeterminacy filtering method. Firstly, the image is transformed into neutrosophic set domain. Then, a new filter, indeterminacy filter is designed according to the indeterminacy value on the neutrosophic image, and the neighborhood information is utilized to remove the indeterminacy in the spatial neighborhood. Neutrosophic c-means clustering is then used to cluster the pixels into different groups, which has advantages to describe the indeterminacy in the intensity. The indeterminacy filter is employed again to remove the indeterminacy in the intensity. Finally, the segmentation results are obtained according to the refined membership in the clustering after indeterminacy filtering operation. A variety of experiments are performed to evaluate the performance of the proposed method, and a newly published method neutrosophic similarity clustering (NSC) segmentation algorithm is utilized to compare with the proposed method quantitatively. The experimental results show that the proposed algorithm has better performances in quantitatively and qualitatively.

Multi-category EEG signal classification developing time-frequency texture features based Fisher Vector encoding method

Classification of electroencephalogram (EEG) signals plays an important role in the diagnosis and treatment of brain diseases in the biomedical field. Here, we introduce a different multi-category EEG signal processing technique, namely time-frequency (T-F) image representation of Gray Level Co-occurrence Matrix (GLCM) descriptors and Fisher Vector (FV) encoding for automatic classification of EEG signals. Firstly the EEG signals are converted into T-F representation by using spectrograms of Short Time Fourier Transform (STFT), which are used to obtain the T-F images. The obtained T-F images are then converted into 8-bits gray-scale images and then are divided into five sub-images corresponding to the frequency-bands of the rhythms. Then, the GLCM texture descriptors are employed to extract distinctive features which are fed into the FV encoding. Finally obtained features are fed to extreme learning machine (ELM) classifier as input for identifying abnormalities from EEG signals. The proposed method was applied to epileptic and sleep stages EEG datasets. The experimental outcomes are promising on both databases. It can be anticipated that upon its implementation in real-time practice, the proposed scheme will assist the researchers and physicians to advance the existing methods for detecting neurological diseases from EEG signals.

A hybrid method based on time–frequency images for classification of alcohol and control EEG signals

Classification of alcoholic electroencephalogram (EEG) signals is a challenging job in biomedical research for diagnosis and treatment of brain diseases of alcoholic people. The aim of this study was to introduce a robust method that can automatically identify alcoholic EEG signals based on time–frequency (T–F) image information as they convey key characteristics of EEG signals. In this paper, we propose a new hybrid method to classify automatically the alcoholic and control EEG signals. The proposed scheme is based on time–frequency images, texture image feature extraction and nonnegative least squares classifier (NNLS). In T–F analysis, the spectrogram of the short-time Fourier transform is considered. The obtained T–F images are then converted into 8-bit grayscale images. Co-occurrence of the histograms of oriented gradients (CoHOG) and Eig(Hess)-CoHOG features are extracted from T–F images. Finally, obtained features are fed into NNLS classifier as input for classify alcoholic and control EEG signals. To verify the effectiveness of the proposed approach, we replace the NNLS classifier by artificial neural networks, k-nearest neighbor, linear discriminant analysis and support vector machine classifier separately, with the same features. Experimental outcomes along with comparative evaluations with the state-of-the-art algorithms manifest that the proposed method outperforms competing algorithms. The experimental outcomes are promising, and it can be anticipated that upon its implementation in clinical practice, the proposed scheme will alleviate the onus of the physicians and expedite neurological diseases diagnosis and research.

NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier

k-nearest neighbors (k-NN), which is known to be a simple and efficient approach, is a non-parametric supervised classifier. It aims to determine the class label of an unknown sample by its k-nearest neighbors that are stored in a training set. The k-nearest neighbors are determined based on some distance functions. Although k-NN produces successful results, there have been some extensions for improving its precision. The neutrosophic set (NS) defines three memberships namely T, I and F. T, I, and F shows the truth membership degree, the false membership degree, and the indeterminacy membership degree, respectively. In this paper, the NS memberships are adopted to improve the classification performance of the k-NN classifier. A new straightforward k-NN approach is proposed based on NS theory. It calculates the NS memberships based on a supervised neutrosophic c-means (NCM) algorithm. A final belonging membership U is calculated from the NS triples as U = T + I − F . A similar final voting scheme as given in fuzzy k-NN is considered for class label determination. Extensive experiments are conducted to evaluate the proposed method’s performance. To this end, several toy and real-world datasets are used. We further compare the proposed method with k-NN, fuzzy k-NN, and two weighted k-NN schemes. The results are encouraging and the improvement is obvious.

A novel 3D skeleton algorithm based on neutrosophic cost function

This paper proposed a novel algorithm to extract the skeleton for the objects on three dimensional images with or without noise.Neutrosophic cost function is proposed based on neutrosophic set.Neutrosophic cost function is employed to define the cost between each point on skeleton. A skeleton provides a synthetic and thin representation of three dimensional objects, and is useful for shape description and recognition. In this paper, a novel 3D skeleton algorithm is proposed based on neutrosophic cost function. Firstly, the distance transform is used to a 3D volume, and the distance matrix is obtained for each voxel in the volume. The ridge points are identified based on their distance transform values and are used as the candidates for the skeleton. Then, a novel cost function, namely neutrosophic cost function (NCF) is proposed based on neutrosophic set, and is utilized to define the cost between each ridge points. Finally, a shortest path finding algorithm is used to identify the optimum path in the 3D volume with least cost, in which the costs of paths are calculated using the new defined NCF. The optimum path is treated as the skeleton of the 3D volume. A variety of experiments have been conducted on different 3D volume. The experimental results demonstrate the better performance of the proposed method. It can identify the skeleton for different volumes with high accuracy. In addition, the proposed method is robust to the noise on the volume. This advantage will lead it to wide application in the skeleton detection applications in the real world.

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

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.

DeepEMGNet: An Application for Efficient Discrimination of ALS and Normal EMG Signals

This paper proposes a deep learning application for efficient classification of amyotrophic lateral sclerosis (ALS) and normal Electromyogram (EMG) signals. EMG signals are helpful in analyzing of the neuromuscular diseases like ALS. ALS is a well-known brain disease, which progressively degenerates the motor neurons. Most of the previous works about EMG signal classification covers a dozen of basic signal processing methodologies such as statistical signal processing, wavelet analysis, and empirical mode decomposition (EMD). In this work, a different application is implemented which is based on time-frequency (TF) representation of EMG signals and convolutional neural networks (CNN). Short Time Fourier Transform (STFT) is considered for TF representation. Two convolution layers, two pooling layer, a fully connected layer and a lost function layer is considered in CNN architecture. The efficiency of the proposed implementation is tested on publicly available EMG dataset. The dataset contains 89 ALS and 133 normal EMG signals with 24 kHz sampling frequency. Experimental results show 96.69% accuracy. The obtained results are also compared with other methods, which show the superiority of the proposed method.

A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images

A fundus image is an effective tool for ophthalmologists studying eye diseases. Retinal vessel detection is a significant task in the identification of retinal disease regions. This study presents a retinal vessel detection approach using shearlet transform and indeterminacy filtering. The fundus image’s green channel is mapped in the neutrosophic domain via shearlet transform. The neutrosophic domain images are then filtered with an indeterminacy filter to reduce the indeterminacy information. A neural network classifier is employed to identify the pixels whose inputs are the features in neutrosophic images. The proposed approach is tested on two datasets, and a receiver operating characteristic curve and the area under the curve are employed to evaluate experimental results quantitatively. The area under the curve values are 0.9476 and 0.9469 for each dataset respectively, and 0.9439 for both datasets. The comparison with the other algorithms also illustrates that the proposed method yields the highest evaluation measurement value and demonstrates the efficiency and accuracy of the proposed method.

KNCM: Kernel Neutrosophic c-Means Clustering

Abstract Data clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as ‘neutrosophic c-means’ (NCM) was proposed in order to alleviate the limitations of the popular fuzzy c-means (FCM) clustering algorithm by introducing a new objective function which contains two types of rejection. The ambiguity rejection which concerned patterns lying near the cluster boundaries, and the distance rejection was dealing with patterns that are far away from the clusters. In this paper, we extend the idea of NCM for nonlinear-shaped data clustering by incorporating the kernel function into NCM. The new clustering algorithm is called Kernel Neutrosophic c-Means (KNCM), and has been evaluated through extensive experiments. Nonlinear-shaped toy datasets, real datasets and images were used in the experiments for demonstrating the efficiency of the proposed method. A comparison between Kernel FCM (KFCM) and KNCM was also accomplished in order to visualize the performance of both methods. According to the obtained results, the proposed KNCM produced better results than KFCM.