Yanhui Guo | St. Thomas University, Florida (original) (raw)

Papers by Yanhui Guo

Research paper thumbnail of A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images

Zenodo (CERN European Organization for Nuclear Research), Nov 1, 2017

Research paper thumbnail of Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification

arXiv (Cornell University), Jul 21, 2018

Melanoma classification is a serious stage to identify the skin disease. It is considered a chall... more Melanoma classification is a serious stage to identify the skin disease. It is considered a challenging process due to the intra-class discrepancy of melanomas, skin lesions' low contrast, and the artifacts in the dermoscopy images, including noise, existence of hair, air bubbles, and the similarity between melanoma and non-melanoma cases. To solve these problems, we propose a novel multiple convolution neural network model (MCNN) to classify different seven disease types in dermoscopic images, where several models were trained separately using an additive sample learning strategy. The MCNN model is trained and tested using the training and validation sets from the International Skin Imaging Collaboration (ISIC 2018), respectively. The receiver operating characteristic (ROC) curve is used to evaluate the performance of the proposed method. The values of AUC (the area under the ROC curve) were used to evaluate the performance of the MCNN.

Research paper thumbnail of Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine

Health information science and systems, 2017

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identific... more Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

Research paper thumbnail of Neutrosophic Hough Transform

viXra, Mar 1, 2018

Hough transform (HT) is a useful tool for both pattern recognition and image processing communiti... more Hough transform (HT) is a useful tool for both pattern recognition and image processing communities. In the view of pattern recognition, it can extract unique features for description of various shapes, such as lines, circles, ellipses, and etc. In the view of image processing, a dozen of applications can be handled with HT, such as lane detection for autonomous cars, blood cell detection in microscope images, and so on. As HT is a straight forward shape detector in a given image, its shape detection ability is low in noisy images. To alleviate its weakness on noisy images and improve its shape detection performance, in this paper, we proposed neutrosophic Hough transform (NHT). As it was proved earlier, neutrosophy theory based image processing applications were successful in noisy environments. To this end, the Hough space is initially transferred into the NS domain by calculating the NS membership triples (T, I, and F). An indeterminacy filtering is constructed where the neighborhood information is used in order to remove the indeterminacy in the spatial neighborhood of neutrosophic Hough space. The potential peaks are detected based on thresholding on the neutrosophic Hough space, and these peak locations are then used to detect the lines in the image domain. Extensive experiments on noisy and noise-free images are performed in order to show the efficiency of the proposed NHT algorithm. We also compared our proposed NHT with traditional HT and fuzzy HT methods on variety of images. The obtained results showed the efficiency of the proposed NHT on noisy images.

Research paper thumbnail of A New Neutrosophic Approach to Image Denoising

A neutrosophic set (NS), a part of neutrosphy theory, studies the origin, nature, and scope of ne... more A neutrosophic set (NS), a part of neutrosphy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image G is transformed into NS domain, which is described using three membership sets: T, I and F. The entropy of the neutrosophic set is defined and employed to evaluate the indeterminancy. A new operation, γmedian-filtering operation, is proposed to decrease the set indeterminancy and remove noise. We have conducted experiments on a variety of noisy images using different type of noise with different levels. The experimental results demonstrate that the proposed approach can remove noise automatically and effectively. Especially, it can process not only noisy images with different levels of noise, but also images with different kinds of noise well without knowing the type of the noise, which is the most difficult task for image denoising.

Research paper thumbnail of A Novel Edge Detection Algorithm Based on Texture Feature Coding

Journal of Intelligent Systems, 2015

A new edge detection technique based on the texture feature coding method (TFCM) is proposed. The... more A new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such...

Research paper thumbnail of Functional Neural Networks for Parametric Image Restoration Problems

arXiv (Cornell University), Dec 6, 2021

Almost every single image restoration problem has a closely related parameter, such as the scale ... more Almost every single image restoration problem has a closely related parameter, such as the scale factor in super-resolution, the noise level in image denoising, and the quality factor in JPEG deblocking. Although recent studies on image restoration problems have achieved great success due to the development of deep neural networks, they handle the parameter involved in an unsophisticated way. Most previous researchers either treat problems with different parameter levels as independent tasks, and train a specific model for each parameter level; or simply ignore the parameter, and train a single model for all parameter levels. The two popular approaches have their own shortcomings. The former is inefficient in computing and the latter is ineffective in performance. In this work, we propose a novel system called functional neural network (FuncNet) to solve a parametric image restoration problem with a single model. Unlike a plain neural network, the smallest conceptual element of our FuncNet is no longer a floating-point variable, but a function of the parameter of the problem. This feature makes it both efficient and effective for a parametric problem. We apply FuncNet to superresolution, image denoising, and JPEG deblocking. The experimental results show the superiority of our FuncNet on all three parametric image restoration tasks over the state of the arts.

Research paper thumbnail of Comparative study of multiclass classification methods on light microscopic images for hepatic schistosomiasis fibrosis diagnosis

Health Information Science and Systems, 2018

Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of ... more Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of schistosomiasis infection due to the host's granulomatous cell-mediated immune. Irreversible fibrosis results from the progress of the schistosomal hepatopathy. Sensitive diagnosis of this disease is based on the investigation of the microscopy images, liver tissues, and egg identification. Early diagnosis of schistosomiasis at its initial infection stage is vital to avoid egg-induced irreparable pathological reactions. Typically, there are several classification approaches that can be used for liver fibrosis staging. However, it is unclear which approaches can achieve high accuracy for analyzing and intelligently classifying the liver microscopic images. Consequently, this work aims to study the performance of the different machine learning classifiers for accurate fibrosis level staging of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The classifiers include a multi-layer perceptron neural network, a decision tree, discriminant analysis, support vector machine (SVM), nearest neighbor, and the ensemble of classifiers. The statistical features of the microscopic images are extracted from the different fibrosis levels of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The results established that the maximum achieved classification accuracies of value 90% were achieved using the subspace discriminant ensemble, the quadratic SVM, the linear SVM, or the linear discriminant classifiers. However, the linear discriminant classifier can be considered the superior classifier as it realized the best area under the curve of value 0.96 during the classification of the cellular granuloma as well as the fibro-cellular granuloma fibrosis levels.

Research paper thumbnail of Centroid tracking and velocity measurement of white blood cell in video

Health Information Science and Systems, 2018

Automated blood cells tracking system has a vital role as the tracking process reflects the blood... more Automated blood cells tracking system has a vital role as the tracking process reflects the blood cell characteristics and indicates several diseases. Blood cells tracking is challenging due to the non-rigid shapes of the blood cells, and the variability in their videos along with the existence of different moving objects in the blood. To tackle such challenges, we proposed a green star based centroid (GSBC) moving white blood cell (WBC) tracking algorithm to measure its velocity and draw its trajectory. The proposed cell tracking system consists of two stages, namely WBC detection and blob analysis, and fine tuning the tracking process by determine the centroid of the WBC, and mark the centroid for further fine tracking and to exclude the bacteria from the bounding box. Furthermore, the speed and the trajectory of the WBC motion are recorded and plotted. In the experiments, an optical flow technique is compared with the proposed tracking system showing the superiority of the proposed system as the optical flow method failed to track the WBC. The proposed system identified the WBC accurately, while the optical flow identified all other objects lead to its disability to track the WBC.

Research paper thumbnail of A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm

Health information science and systems, 2017

Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesion... more Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection. To this end, green channel decomposition, Gaussian filtering, median filtering, back ground determination, and subtraction operations are applied to input colored fundus images. After pre-processing, a candidate MAs extraction procedure is applied to detect potential regions. A five-stepped procedure is adopted to get the potential MA locations. Finally, deep convolutional neural...

Research paper thumbnail of KNCM: Kernel Neutrosophic c-Means Clustering

Applied Soft Computing, 2017

Data clustering is an important step in data mining and machine learning. It is especially crucia... more 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.

Research paper thumbnail of Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography video

Health information science and systems, 2017

Recently, elastography has become very popular in clinical investigation for thyroid cancer detec... more Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images. Firstly, a thyroid ultrasound image is mapped into neutrosophic set, and an indeterminacy filter is constructed to reduce the indeterminacy of the spatial and intensity information in the image. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algor...

Research paper thumbnail of A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images

Health Information Science and Systems, 2017

Purpose: Glomerular basement membrane segmentation is an ultimate step in several image processin... more Purpose: Glomerular basement membrane segmentation is an ultimate step in several image processing applications for kidney diseases and abnormalities in microscopic images. However, extracting the glomerular basement membrane (GBM) regions accurately is considered challenging because of the large variants in the microscopic images. The contribution of this work is to propose a computer-aided detection system to provide accurate GBM segmentation. Methods: A novel GBM segmentation algorithm is developed based on neutrsophic set and shearlet transform. Firstly, the shearlet features are extracted from the microscopic image samples using shearlet transform. Afterward, the neutrosophic image is defined using shearlet features, and the indeterminacy on the neutrosophic image is reduced using an α-mean operation. Lastly, the k-means clustering algorithm is applied to segment the neutrsophic image and the GBM is identified using its intensity feature. Results: Three metrics, namely the average distance (AvgDist), the Hausdorff distance (Hdist), and percentage overlap area (POA); were employed to assess the proposed method performance. The results established that the proposed method achieved smaller distance errors and larger POAs. For the tested image, the average of AvgDist, HDist and POA are 1.99204, 4.59535 and 0.67857, respectively. The results established that the cases were segmented accurately using the proposed NS based shearlet transform. Conclusions: The new method utilizing the shearlet features and neutrosophic set improved the accuracy of GBM segmentation. Further study is underway to improve an automated CAD system using the refined segmentation results.

Research paper thumbnail of MemConFuzz: Memory Consumption Guided Fuzzing with Data Flow Analysis

Mathematics

Uncontrolled heap memory consumption, a kind of critical software vulnerability, is utilized by a... more Uncontrolled heap memory consumption, a kind of critical software vulnerability, is utilized by attackers to consume a large amount of heap memory and consequently trigger crashes. There have been few works on the vulnerability fuzzing of heap consumption. Most of them, such as MemLock and PerfFuzz, have failed to consider the influence of data flow. We proposed a heap memory consumption guided fuzzing model named MemConFuzz. It extracts the locations of heap operations and data-dependent functions through static data flow analysis. Based on the data dependency, we proposed a seed selection algorithm in fuzzing to assign more energy to the samples with higher priority scores. The experiment results showed that the MemConFuzz has advantages over AFL, MemLock, and PerfFuzz with more quantity and less time consumption in exploiting the vulnerability of heap memory consumption.

Research paper thumbnail of FastAFLGo: Toward a Directed Greybox Fuzzing

Computers, Materials & Continua

While the size and complexity of software are rapidly increasing, not only is the number of vulne... more While the size and complexity of software are rapidly increasing, not only is the number of vulnerabilities increasing, but their forms are diversifying. Vulnerability has become an important factor in network attack and defense. Therefore, automatic vulnerability discovery has become critical to ensure software security. Fuzzing is one of the most important methods of vulnerability discovery. It is based on the initial input, i.e., a seed, to generate mutated test cases as new inputs of a tested program in the next execution loop. By monitoring the path coverage, fuzzing can choose high-value test cases for inclusion in the new seed set and capture crashes used for triggering vulnerabilities. Although there have been remarkable achievements in terms of the number of discovered vulnerabilities, the reduction of time cost is still inadequate. This paper proposes a fast directed greybox fuzzing model, FastAFLGo. A fast convergence formula of temperature is designed, and the energy scheduling scheme can quickly determine the best seed to make the program execute toward the target basic blocks. Experimental results show that FastAFLGo can discover more vulnerabilities than the traditional fuzzing method in the same execution time.

Research paper thumbnail of An enhanced password authentication scheme for session initiation protocol with perfect forward secrecy

PloS one, 2018

The Session Initiation Protocol (SIP) is an extensive and esteemed communication protocol employe... more The Session Initiation Protocol (SIP) is an extensive and esteemed communication protocol employed to regulate signaling as well as for controlling multimedia communication sessions. Recently, Kumari et al. proposed an improved smart card based authentication scheme for SIP based on Farash's scheme. Farash claimed that his protocol is resistant against various known attacks. But, we observe some accountable flaws in Farash's protocol. We point out that Farash's protocol is prone to key-compromise impersonation attack and is unable to provide pre-verification in the smart card, efficient password change and perfect forward secrecy. To overcome these limitations, in this paper we present an enhanced authentication mechanism based on Kumari et al.'s scheme. We prove that the proposed protocol not only overcomes the issues in Farash's scheme, but it can also resist against all known attacks. We also provide the security analysis of the proposed scheme with the help o...

Research paper thumbnail of Exploring deep residual network based features for automatic schizophrenia detection from EEG

Physical and Engineering Sciences in Medicine

Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies... more Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results fo...

Research paper thumbnail of Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images

Health Information Science and Systems, 2018

Schistosomiasis is one of the dangerous parasitic diseases that affect the liver tissues leading ... more Schistosomiasis is one of the dangerous parasitic diseases that affect the liver tissues leading to liver fibrosis. Such disease has several levels, which indicate the degree of fibrosis severity. To assess the fibrosis level for diagnosis and treatment, the microscopic images of the liver tissues were examined at their different stages. In the present work, an automated staging method is proposed to classify the statistical extracted features from each fibrosis stage using an ensemble classifier, namely the subspace ensemble using linear discriminant learning scheme. The performance of the subspace/discriminant ensemble classifier was compared to other ensemble combinations, namely the boosted/ trees ensemble, bagged/trees ensemble, subspace/KNN ensemble, and the RUSBoosted/trees ensemble. The simulation results established the superiority of the proposed subspace/discriminant ensemble with 90% accuracy compared to the other ensemble classifiers.

Research paper thumbnail of An effective clustering method based on data indeterminacy in neutrosophic set domain

Engineering Applications of Artificial Intelligence, 2020

In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The m... more In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The main contribution is to use NS to handle boundary and outlier points as challenging points of clustering methods. In the first step, a new definition of data indeterminacy (indeterminacy set) is proposed in NS domain

Research paper thumbnail of Transfer learning based histopathologic image classification for breast cancer detection

Health Information Science and Systems, 2018

Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer pati... more Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer patients die every year due to the late diagnosis and treatment. Thus, in recent years, early breast cancer detection systems based on patient's imagery are in demand. Deep learning attracts many researchers recently and many computer vision applications have come out in various environments. Convolutional neural network (CNN) which is known as deep learning architecture, has achieved impressive results in many applications. CNNs generally suffer from tuning a huge number of parameters which bring a great amount of complexity to the system. In addition, the initialization of the weights of the CNN is another handicap that needs to be handle carefully. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to the problem at hand. AlexNet and Vgg16 models are considered in the presented work for feature extraction and AlexNet is used for further fine-tuning. The obtained features are then classified by support vector machines (SVM). Extensive experiments on a publicly available histopathologic breast cancer dataset are carried out and the accuracy scores are calculated for performance evaluation. The evaluation results show that the transfer learning produced better result than deep feature extraction and SVM classification.

Research paper thumbnail of A Retinal Vessel Detection Approach Based on Shearlet Transform and Indeterminacy Filtering on Fundus Images

Zenodo (CERN European Organization for Nuclear Research), Nov 1, 2017

Research paper thumbnail of Multiple Convolutional Neural Network for Skin Dermoscopic Image Classification

arXiv (Cornell University), Jul 21, 2018

Melanoma classification is a serious stage to identify the skin disease. It is considered a chall... more Melanoma classification is a serious stage to identify the skin disease. It is considered a challenging process due to the intra-class discrepancy of melanomas, skin lesions' low contrast, and the artifacts in the dermoscopy images, including noise, existence of hair, air bubbles, and the similarity between melanoma and non-melanoma cases. To solve these problems, we propose a novel multiple convolution neural network model (MCNN) to classify different seven disease types in dermoscopic images, where several models were trained separately using an additive sample learning strategy. The MCNN model is trained and tested using the training and validation sets from the International Skin Imaging Collaboration (ISIC 2018), respectively. The receiver operating characteristic (ROC) curve is used to evaluate the performance of the proposed method. The values of AUC (the area under the ROC curve) were used to evaluate the performance of the MCNN.

Research paper thumbnail of Combined empirical mode decomposition and texture features for skin lesion classification using quadratic support vector machine

Health information science and systems, 2017

Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identific... more Basal cell carcinoma is one of the most common malignant skin lesions. Automated lesion identification and classification using image processing techniques is highly required to reduce the diagnosis errors. In this study, a novel technique is applied to classify skin lesion images into two classes, namely the malignant Basal cell carcinoma and the benign nevus. A hybrid combination of bi-dimensional empirical mode decomposition and gray-level difference method features is proposed after hair removal. The combined features are further classified using quadratic support vector machine (Q-SVM). The proposed system has achieved outstanding performance of 100% accuracy, sensitivity and specificity compared to other support vector machine procedures as well as with different extracted features. Basal Cell Carcinoma is effectively classified using Q-SVM with the proposed combined features.

Research paper thumbnail of Neutrosophic Hough Transform

viXra, Mar 1, 2018

Hough transform (HT) is a useful tool for both pattern recognition and image processing communiti... more Hough transform (HT) is a useful tool for both pattern recognition and image processing communities. In the view of pattern recognition, it can extract unique features for description of various shapes, such as lines, circles, ellipses, and etc. In the view of image processing, a dozen of applications can be handled with HT, such as lane detection for autonomous cars, blood cell detection in microscope images, and so on. As HT is a straight forward shape detector in a given image, its shape detection ability is low in noisy images. To alleviate its weakness on noisy images and improve its shape detection performance, in this paper, we proposed neutrosophic Hough transform (NHT). As it was proved earlier, neutrosophy theory based image processing applications were successful in noisy environments. To this end, the Hough space is initially transferred into the NS domain by calculating the NS membership triples (T, I, and F). An indeterminacy filtering is constructed where the neighborhood information is used in order to remove the indeterminacy in the spatial neighborhood of neutrosophic Hough space. The potential peaks are detected based on thresholding on the neutrosophic Hough space, and these peak locations are then used to detect the lines in the image domain. Extensive experiments on noisy and noise-free images are performed in order to show the efficiency of the proposed NHT algorithm. We also compared our proposed NHT with traditional HT and fuzzy HT methods on variety of images. The obtained results showed the efficiency of the proposed NHT on noisy images.

Research paper thumbnail of A New Neutrosophic Approach to Image Denoising

A neutrosophic set (NS), a part of neutrosphy theory, studies the origin, nature, and scope of ne... more A neutrosophic set (NS), a part of neutrosphy theory, studies the origin, nature, and scope of neutralities, as well as their interactions with different ideational spectra. The neutrosophic set is a general formal framework that has been recently proposed. However, the neutrosophic set needs to be specified from a technical point of view. Now, we apply the neutrosophic set into image domain and define some concepts and operators for image denoising. The image G is transformed into NS domain, which is described using three membership sets: T, I and F. The entropy of the neutrosophic set is defined and employed to evaluate the indeterminancy. A new operation, γmedian-filtering operation, is proposed to decrease the set indeterminancy and remove noise. We have conducted experiments on a variety of noisy images using different type of noise with different levels. The experimental results demonstrate that the proposed approach can remove noise automatically and effectively. Especially, it can process not only noisy images with different levels of noise, but also images with different kinds of noise well without knowing the type of the noise, which is the most difficult task for image denoising.

Research paper thumbnail of A Novel Edge Detection Algorithm Based on Texture Feature Coding

Journal of Intelligent Systems, 2015

A new edge detection technique based on the texture feature coding method (TFCM) is proposed. The... more A new edge detection technique based on the texture feature coding method (TFCM) is proposed. The TFCM is a texture analysis scheme that is generally used in texture-based image segmentation and classification applications. The TFCM transforms an input image into a texture feature image whose pixel values represent the texture information of the pixel in the original image. Then, on the basis of the transformed image, several features are calculated as texture descriptors. In this article, the TFCM is employed differently to construct an edge detector. In particular, the texture feature number (TFN) of the TFCM is considered. In other words, the TFN image is used for subsequent processes. After obtaining the TFN image, a simple thresholding scheme is employed for obtaining the coarse edge image. Finally, an edge-thinning procedure is used to obtain the tuned edges. We conducted several experiments on a variety of images and compared the results with the popular existing methods such...

Research paper thumbnail of Functional Neural Networks for Parametric Image Restoration Problems

arXiv (Cornell University), Dec 6, 2021

Almost every single image restoration problem has a closely related parameter, such as the scale ... more Almost every single image restoration problem has a closely related parameter, such as the scale factor in super-resolution, the noise level in image denoising, and the quality factor in JPEG deblocking. Although recent studies on image restoration problems have achieved great success due to the development of deep neural networks, they handle the parameter involved in an unsophisticated way. Most previous researchers either treat problems with different parameter levels as independent tasks, and train a specific model for each parameter level; or simply ignore the parameter, and train a single model for all parameter levels. The two popular approaches have their own shortcomings. The former is inefficient in computing and the latter is ineffective in performance. In this work, we propose a novel system called functional neural network (FuncNet) to solve a parametric image restoration problem with a single model. Unlike a plain neural network, the smallest conceptual element of our FuncNet is no longer a floating-point variable, but a function of the parameter of the problem. This feature makes it both efficient and effective for a parametric problem. We apply FuncNet to superresolution, image denoising, and JPEG deblocking. The experimental results show the superiority of our FuncNet on all three parametric image restoration tasks over the state of the arts.

Research paper thumbnail of Comparative study of multiclass classification methods on light microscopic images for hepatic schistosomiasis fibrosis diagnosis

Health Information Science and Systems, 2018

Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of ... more Hepatic schistosomiasis is a prolonged disease resulting mainly from the solvable egg antigen of schistosomiasis infection due to the host's granulomatous cell-mediated immune. Irreversible fibrosis results from the progress of the schistosomal hepatopathy. Sensitive diagnosis of this disease is based on the investigation of the microscopy images, liver tissues, and egg identification. Early diagnosis of schistosomiasis at its initial infection stage is vital to avoid egg-induced irreparable pathological reactions. Typically, there are several classification approaches that can be used for liver fibrosis staging. However, it is unclear which approaches can achieve high accuracy for analyzing and intelligently classifying the liver microscopic images. Consequently, this work aims to study the performance of the different machine learning classifiers for accurate fibrosis level staging of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The classifiers include a multi-layer perceptron neural network, a decision tree, discriminant analysis, support vector machine (SVM), nearest neighbor, and the ensemble of classifiers. The statistical features of the microscopic images are extracted from the different fibrosis levels of granuloma, namely cellular, fibrocellular and fibrotic granulomas as well as the normal samples. The results established that the maximum achieved classification accuracies of value 90% were achieved using the subspace discriminant ensemble, the quadratic SVM, the linear SVM, or the linear discriminant classifiers. However, the linear discriminant classifier can be considered the superior classifier as it realized the best area under the curve of value 0.96 during the classification of the cellular granuloma as well as the fibro-cellular granuloma fibrosis levels.

Research paper thumbnail of Centroid tracking and velocity measurement of white blood cell in video

Health Information Science and Systems, 2018

Automated blood cells tracking system has a vital role as the tracking process reflects the blood... more Automated blood cells tracking system has a vital role as the tracking process reflects the blood cell characteristics and indicates several diseases. Blood cells tracking is challenging due to the non-rigid shapes of the blood cells, and the variability in their videos along with the existence of different moving objects in the blood. To tackle such challenges, we proposed a green star based centroid (GSBC) moving white blood cell (WBC) tracking algorithm to measure its velocity and draw its trajectory. The proposed cell tracking system consists of two stages, namely WBC detection and blob analysis, and fine tuning the tracking process by determine the centroid of the WBC, and mark the centroid for further fine tracking and to exclude the bacteria from the bounding box. Furthermore, the speed and the trajectory of the WBC motion are recorded and plotted. In the experiments, an optical flow technique is compared with the proposed tracking system showing the superiority of the proposed system as the optical flow method failed to track the WBC. The proposed system identified the WBC accurately, while the optical flow identified all other objects lead to its disability to track the WBC.

Research paper thumbnail of A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm

Health information science and systems, 2017

Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesion... more Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection. To this end, green channel decomposition, Gaussian filtering, median filtering, back ground determination, and subtraction operations are applied to input colored fundus images. After pre-processing, a candidate MAs extraction procedure is applied to detect potential regions. A five-stepped procedure is adopted to get the potential MA locations. Finally, deep convolutional neural...

Research paper thumbnail of KNCM: Kernel Neutrosophic c-Means Clustering

Applied Soft Computing, 2017

Data clustering is an important step in data mining and machine learning. It is especially crucia... more 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.

Research paper thumbnail of Using neutrosophic graph cut segmentation algorithm for qualified rendering image selection in thyroid elastography video

Health information science and systems, 2017

Recently, elastography has become very popular in clinical investigation for thyroid cancer detec... more Recently, elastography has become very popular in clinical investigation for thyroid cancer detection and diagnosis. In elastogram, the stress results of the thyroid are displayed using pseudo colors. Due to variation of the rendering results in different frames, it is difficult for radiologists to manually select the qualified frame image quickly and efficiently. The purpose of this study is to find the qualified rendering result in the thyroid elastogram. This paper employs an efficient thyroid ultrasound image segmentation algorithm based on neutrosophic graph cut to find the qualified rendering images. Firstly, a thyroid ultrasound image is mapped into neutrosophic set, and an indeterminacy filter is constructed to reduce the indeterminacy of the spatial and intensity information in the image. A graph is defined on the image and the weight for each pixel is represented using the value after indeterminacy filtering. The segmentation results are obtained using a maximum-flow algor...

Research paper thumbnail of A novel glomerular basement membrane segmentation using neutrsophic set and shearlet transform on microscopic images

Health Information Science and Systems, 2017

Purpose: Glomerular basement membrane segmentation is an ultimate step in several image processin... more Purpose: Glomerular basement membrane segmentation is an ultimate step in several image processing applications for kidney diseases and abnormalities in microscopic images. However, extracting the glomerular basement membrane (GBM) regions accurately is considered challenging because of the large variants in the microscopic images. The contribution of this work is to propose a computer-aided detection system to provide accurate GBM segmentation. Methods: A novel GBM segmentation algorithm is developed based on neutrsophic set and shearlet transform. Firstly, the shearlet features are extracted from the microscopic image samples using shearlet transform. Afterward, the neutrosophic image is defined using shearlet features, and the indeterminacy on the neutrosophic image is reduced using an α-mean operation. Lastly, the k-means clustering algorithm is applied to segment the neutrsophic image and the GBM is identified using its intensity feature. Results: Three metrics, namely the average distance (AvgDist), the Hausdorff distance (Hdist), and percentage overlap area (POA); were employed to assess the proposed method performance. The results established that the proposed method achieved smaller distance errors and larger POAs. For the tested image, the average of AvgDist, HDist and POA are 1.99204, 4.59535 and 0.67857, respectively. The results established that the cases were segmented accurately using the proposed NS based shearlet transform. Conclusions: The new method utilizing the shearlet features and neutrosophic set improved the accuracy of GBM segmentation. Further study is underway to improve an automated CAD system using the refined segmentation results.

Research paper thumbnail of MemConFuzz: Memory Consumption Guided Fuzzing with Data Flow Analysis

Mathematics

Uncontrolled heap memory consumption, a kind of critical software vulnerability, is utilized by a... more Uncontrolled heap memory consumption, a kind of critical software vulnerability, is utilized by attackers to consume a large amount of heap memory and consequently trigger crashes. There have been few works on the vulnerability fuzzing of heap consumption. Most of them, such as MemLock and PerfFuzz, have failed to consider the influence of data flow. We proposed a heap memory consumption guided fuzzing model named MemConFuzz. It extracts the locations of heap operations and data-dependent functions through static data flow analysis. Based on the data dependency, we proposed a seed selection algorithm in fuzzing to assign more energy to the samples with higher priority scores. The experiment results showed that the MemConFuzz has advantages over AFL, MemLock, and PerfFuzz with more quantity and less time consumption in exploiting the vulnerability of heap memory consumption.

Research paper thumbnail of FastAFLGo: Toward a Directed Greybox Fuzzing

Computers, Materials & Continua

While the size and complexity of software are rapidly increasing, not only is the number of vulne... more While the size and complexity of software are rapidly increasing, not only is the number of vulnerabilities increasing, but their forms are diversifying. Vulnerability has become an important factor in network attack and defense. Therefore, automatic vulnerability discovery has become critical to ensure software security. Fuzzing is one of the most important methods of vulnerability discovery. It is based on the initial input, i.e., a seed, to generate mutated test cases as new inputs of a tested program in the next execution loop. By monitoring the path coverage, fuzzing can choose high-value test cases for inclusion in the new seed set and capture crashes used for triggering vulnerabilities. Although there have been remarkable achievements in terms of the number of discovered vulnerabilities, the reduction of time cost is still inadequate. This paper proposes a fast directed greybox fuzzing model, FastAFLGo. A fast convergence formula of temperature is designed, and the energy scheduling scheme can quickly determine the best seed to make the program execute toward the target basic blocks. Experimental results show that FastAFLGo can discover more vulnerabilities than the traditional fuzzing method in the same execution time.

Research paper thumbnail of An enhanced password authentication scheme for session initiation protocol with perfect forward secrecy

PloS one, 2018

The Session Initiation Protocol (SIP) is an extensive and esteemed communication protocol employe... more The Session Initiation Protocol (SIP) is an extensive and esteemed communication protocol employed to regulate signaling as well as for controlling multimedia communication sessions. Recently, Kumari et al. proposed an improved smart card based authentication scheme for SIP based on Farash's scheme. Farash claimed that his protocol is resistant against various known attacks. But, we observe some accountable flaws in Farash's protocol. We point out that Farash's protocol is prone to key-compromise impersonation attack and is unable to provide pre-verification in the smart card, efficient password change and perfect forward secrecy. To overcome these limitations, in this paper we present an enhanced authentication mechanism based on Kumari et al.'s scheme. We prove that the proposed protocol not only overcomes the issues in Farash's scheme, but it can also resist against all known attacks. We also provide the security analysis of the proposed scheme with the help o...

Research paper thumbnail of Exploring deep residual network based features for automatic schizophrenia detection from EEG

Physical and Engineering Sciences in Medicine

Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies... more Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results fo...

Research paper thumbnail of Ensemble of subspace discriminant classifiers for schistosomal liver fibrosis staging in mice microscopic images

Health Information Science and Systems, 2018

Schistosomiasis is one of the dangerous parasitic diseases that affect the liver tissues leading ... more Schistosomiasis is one of the dangerous parasitic diseases that affect the liver tissues leading to liver fibrosis. Such disease has several levels, which indicate the degree of fibrosis severity. To assess the fibrosis level for diagnosis and treatment, the microscopic images of the liver tissues were examined at their different stages. In the present work, an automated staging method is proposed to classify the statistical extracted features from each fibrosis stage using an ensemble classifier, namely the subspace ensemble using linear discriminant learning scheme. The performance of the subspace/discriminant ensemble classifier was compared to other ensemble combinations, namely the boosted/ trees ensemble, bagged/trees ensemble, subspace/KNN ensemble, and the RUSBoosted/trees ensemble. The simulation results established the superiority of the proposed subspace/discriminant ensemble with 90% accuracy compared to the other ensemble classifiers.

Research paper thumbnail of An effective clustering method based on data indeterminacy in neutrosophic set domain

Engineering Applications of Artificial Intelligence, 2020

In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The m... more In this work, a new clustering algorithm is proposed based on neutrosophic set (NS) theory. The main contribution is to use NS to handle boundary and outlier points as challenging points of clustering methods. In the first step, a new definition of data indeterminacy (indeterminacy set) is proposed in NS domain

Research paper thumbnail of Transfer learning based histopathologic image classification for breast cancer detection

Health Information Science and Systems, 2018

Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer pati... more Breast cancer is one of the leading cancer type among women in worldwide. Many breast cancer patients die every year due to the late diagnosis and treatment. Thus, in recent years, early breast cancer detection systems based on patient's imagery are in demand. Deep learning attracts many researchers recently and many computer vision applications have come out in various environments. Convolutional neural network (CNN) which is known as deep learning architecture, has achieved impressive results in many applications. CNNs generally suffer from tuning a huge number of parameters which bring a great amount of complexity to the system. In addition, the initialization of the weights of the CNN is another handicap that needs to be handle carefully. In this paper, transfer learning and deep feature extraction methods are used which adapt a pre-trained CNN model to the problem at hand. AlexNet and Vgg16 models are considered in the presented work for feature extraction and AlexNet is used for further fine-tuning. The obtained features are then classified by support vector machines (SVM). Extensive experiments on a publicly available histopathologic breast cancer dataset are carried out and the accuracy scores are calculated for performance evaluation. The evaluation results show that the transfer learning produced better result than deep feature extraction and SVM classification.