Target region location based on texture analysis and active contour model

The issue of target localization by means of texture analysis is addressed. First the texture feature extraction based on multi-channel Gabor filter decomposition and the rotation invariant representation of Gabor features are analyzed in the view of their ability of classification. After that, a method based on Gabor features and neural network classifier is proposed. The method is composed of two stages, unsupervised texture clustering and target localization. In the first stage, original feature space extracted by Gabor filter banks is applied in training a self organized map classifier and a novel merging scheme is presented to achieve the accuracy of clustering. In the second stage, digital Fourier transform of the original feature vectors are applied in back propagation (BP) network to ensure rotation invariance in localization. In the experiments, the usefulness of the proposed method is demonstrated on texture database and practical barcode localization system as well. The method is also proved rotation invariant and accurate in localizing target texture.

A fault detection and diagnosis method for the hydraulic servo system based on adaptive threshold and self-organizing map (SOM) neural network is proposed in this study. The nonlinear, time-varying, fluid-solid coupling properties of the hydraulic servo system are considered. Fault detection is realized based on a two-stage radial basis function (RBF) neural network model. The first-stage RBF neural network is adopted as a fault observer for the hydraulic servo system; the residual error signal is generated by comparing the estimated observer output with the actual measurements. To overcome the drawback of false alarms when the traditional fixed fault threshold is used, an adaptive threshold producer is established by the second-stage RBF neural network. Fault occurrence is detected by comparing the residual error signal with the adaptive threshold. When a system fault is detected, the SOM neural network is employed to implement fault classification and isolation by analyzing the features of the residual error signal. Three types of common faults are simulated to verify the performance and effectiveness of the proposed method. Experimental results demonstrate that the proposed method based on adaptive threshold and SOM neural network is effective in detecting and isolating the failure of the hydraulic servo system.

To improve the robustness of performance assessment for aileron actuators, a novel performance assessment for aileron actuators using multi-fractal detrended fluctuation analysis (MF-DFA) and self-organizing mapping (SOM) neural network is first proposed. First, a generalized regression neural network (GRNN) is employed to establish a fault observer, then the residue is generated by subtracting the actual actuator system output from the observer output. Second, the residue's feature is extracted by MF-DFA. Third, SOM neural network, trained only by the normal residue's feature, is used to obtain the minimum quantization error (MQE) between the current feature and the normal feature, and the health confidence value (CV) is obtained by normalizing MQE. Lastly, four types of faults, namely electronic amplifier gain abrupt change, electronic amplifier gain gradual change, sensor gain change and cylinder leakage, are applied to validate the effectiveness of the proposed method.

In this work, we propose a solution for the recognition of protein folds using Self-Organizing Map (SOM) neural network and present a comparison between few approaches. We use SOM, Fisher’s Linear Discriminant Analysis (FLD), K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) methods for the recognition of three SCOP folds with six attributes (amino acid composition, predicted secondary structure, hydrophobicity, normalized van der Waals volume, polarity and polarizability). Then we classify the most common 27 SCOP folds using 125 dimensional data formed by the six attributes. This paper has a novelty in the way of applying SOM to these six attributes, and also portrays the capabilities of SOM among the other methods in protein fold classification. Firstly for the threeclass problem, the methods are tested on 120 proteins by applying 10-fold cross-validation technique and 93.33% classification performance is obtained with SOM. Secondly for the 27-class problem SOM is tested on 694 proteins by applying one-versus-others technique and 73.37% classification performance is obtained. Keywords: Protein fold recognition, protein fold classification, neural networks, self-organizing map.

The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Holder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Holder exponents of the data set 1 and the Holder exponents of the radioactive elements concentrations; data set 5: the estimated Holder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Holder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data.

Severe Acute Respiratory Syndrome (SARS) is a highly infectious disease caused by a coronavirus. Screening to detect potential SARS-infected subjects with elevated body temperature plays an important role in preventing the spread of SARS. Thermography is being used with ANN/AI to analyse the data collected from the designated SARS hospital in Singapore, and conclusive results are drawn. The current work evaluates the correlations (and classifications) between facial skin temperatures, including eye range and forehead, to aural temperature using a neural network (NN) approach, namely training backpropagation (BP) and Kohonen self-organizing map (SOM), to confirm the suitability of thermal imagers for human temperature screening. Both BP and SOM can form an opinion about the type of network that is better able to complement thermogram technology in fever diagnosis. This can produce better parameters for reducing the size of the NN classifier, while maintaining good classification accuracy. We observe that BP performs better than SOM NN. Confusion matrix (CM), an alternative display instrument, is able to process a high volume of input data and show the clustered output rapidly and accurately. The current research application will remain an interesting and useful reference for both local and overseas manufacturers of thermal scanners, users and various government and private establishments. As the elevation of body temperature is a common presenting symptom for many illnesses, including infectious diseases such as SARS, thermal imagers are useful and essential tools for mass screening of body temperature. This is true not only for SARS but also during other public health crises where widespread transmission of infection such as the danger of avian flu pandemic is a concern, in particular at places like hospitals and cross-border checkpoints.

Purpose:Variational image segmentation incorporating regularized composite shape prior (RCSP) based on a shape dictionary has demonstrated benefits in robustness and accuracy. However, it still has the risk of local minimum in a non‐convex optimization setting. This study aims to drive the variational segmentation towards global optimum by introducing hyper prior on the composite weights of RCSP, which encode the shape priors’ relevance to the specific segmentation task.Methods:In addition to using a RCSP to regularize the variational segmentation, where the RCSP is constructed by a linear combination of the shape dictionary, this study introduces a hyper prior on the linear weights of this shape composite. More specifically, geometric relevance of each shape in the dictionary to the unknown target segmentation is inferred from image based surrogate metrics. Such relevance value is used as hyper prior and imposed on the linear weights of the composite shapes. The resulted RCSP with hyper prior regularization is incorporated in a unified active contour optimization framework and a variational block‐descent algorithm is derived and implemented.Results:The performance was assessed on corpus callosum segmentation using a brain MR dataset, and compared with typical benchmark approaches. The resulted RCSP demonstrated proper composition of training data w.r.t. their individual geometric relevance. The accuracy of ultimate segmentation estimates yielded statistically significant improvement with mean and medium Dice similarity coefficient (DSC) of (.946, .963) compared to (.902, .919), (.932, .946), and (.944, .964) for ATLAS based scheme, Chan Vese active contour model and an existing RCSP model, respectively.Conclusion:This work has developed a hyper prior to encode shape priors’ geometric relevance for RCSP regularization in a variational segmentation framework, leading to superior segmentation over benchmark approaches.

In this paper, we focus on hardware Trojans inserted into IC (Integrated Circuit) chips during IC design. We propose a hardware Trojan detection technique which is based on the chip temperature characterization by using SOM neural network. This method can achieve a high detection rate for Trojans without gold First, we use a tool called HotSpot to get the steady-state heatmap from running IC. Then, we make use of 2DPCA (Dimensional Principal Component Analysis) to extract the features of the heatmap profile and feed them into a SOM (Self Organizing Map) neural network. Finally, the SOM neural network automatically distinguishes Trojan-free chips from Trojaninfected chips. The experimental results show that our method can achieve high detection rate, with 20% chip PV (Process Variability), for all centralized Trojans which are located at different positions.

The use of neural networks in quality control has been a popular research topic over the last decade. An adaptive self-organizing mapping (SOM) neural network algorithm is proposed to overcome the shortages of traditional neural networks in this paper. In order to improve the classification effectiveness of SOM neural network, this paper designs an improved SOM neural network, which improved the algorithm formula based on input vector, the number setting of competitive layer neurons and the initializing weight vector. And the method is used to classify the product of cement slide shoe bearing in manufacturing process quality control, and experiment results show that the algorithm adapts well the unsupervised learning problems. Keywords-Self-organizing Mapping (SOM), artificial neural network, manufacturing process quality control

Self-Organizing Mapping (SOM) neural network has been widely used in pattern classification, vector quantization, and image compression. We consider the problem of strengthening the reliability of a SOM neural network by the technique of fault immunization of the synaptic links of each neuron which is similar to the concept of biological immunization. Instead of assuming the stuck-at-0 and stuck-at-1 as in those studies, we consider a general case of stuck-at-a, where a is a real value. The only assumption that we consider is only one neuron can be faulty at any time. No restriction on the number of faulty links of the neuron. Let wi,j be the weight of synaptic link j of neuron i obtained after the winner-take-all classification. Weight wi,j is immunized by adding a constant ∊i,j, either positive or negative, to wi,j. A neuron reaches its maximum fault immunization if the value of wi,j + ∊i,j can be either increased or decreased as much as possible without creating any misclassification. Thus, the fault immunization problem is formulated as an optimization problem on finding the value of each ∊i,j. A technique to find the value of wi,j + ∊i,j is proposed and its application to enhance the transmission reliability in image compression area is introduced.

Weave patterns are crucial fabric structure parameters for analyzing and designing woven fabrics. The key to identifying fabric weave patterns is to identify the two types of warp and weft interlacing points. This paper introduces an image segmentation method based on Chan-Vese (C-V) active contour model for classification of interlacing point types as a way to quickly discriminate weave pattern types. Compared to traditional image segmentation methods based on thresholds, regions, and edges, this model relies on global information from homogeneous regions rather than local gradients. It has been experimentally demonstrated that the C-V active contour model, based on global area features, can adaptively segment the distribution areas of warp and weft tissue points in woven fabrics with varying grayscale area feature information. This enables rapid identification of fabric tissue patterns. Compared to the Tamura method, steerable filter method, and Gabor method, the proposed method shows significantly superior segmentation performance, particularly for dark woven fabrics, achieving 100% identification accuracy rate.

Palmprint Identification Using Gabor and Wide Principal Line Features

The Self-Organizing Neural Network Map (SOM) may be considered as good tools for classification and clustering process. Therefore, their performance is investigated in this work to make out the main flaw and propose the suitable improvements to overpass their limitations. These latter were extracted by the use of the SOM to classify a set of values with two different dimensions, the computational time cost and the input space normalization were the main deficiencies faced in this process. It's to note that the SOM was used without any kind of supervising or pre-training. The proposed improvement to overpass these deficiencies relies on enhancing the normalization mechanism with more intelligent techniques that can reduce the computational time and regularize the input space to avoid these limitations.

Image processing techniques have been used in processing of potential fields data in order to delineate the edges of the anomalous sources. In this research, firstly the 2D Gabor filter was introduced. Then its operation was investigated on the three magnetized prisms which were at the three different depths and orientations. The effects of body wavelength, standard deviation and body orientation in the Gabor filter on the detection of edge bodies were studied. Investigations showed that the Gabor filter is a low pass filter. Afterwards, the Gabor filter was applied on the aeromagnetic data of Khoram Abad region in the southwest and ground magnetic data from Kashmar region in the northeast of Iran. The results show that the trend of geological structures is NW-SE which is in concordance to those obtained from other filters as well as geological map of the region. 1 2D Gabor filter The boundary of the geological structures can be determined by processing of potential field spectrums .Most filters which have been used for edge detection are based on the different degree of derivatives. The derivatives cause noises and signals amplify together which decrease resolution of edges. Therefore, using filters without any derivative terms is a prime objective. The Gabor filter has not any derivative terms in its mathematical structure. Denis Gabor first introduced the filter for image processing. This filter is linear and has been used for analyzing details of an image or its texture. The characteristics of the Gabor filter is to obtain special frequencies in a certain point or a region. Mostly this filter has been used for analyzing human vision system. The Gabor filter has two parts, real and imaginary in exponential forms which are multiplied with a Gaussian function. The real part has been used for data filtering. The Gabor filter response in spatial domain is defined by convolution of data in the Gabor matrix. The Gabor filter is a low pass filter which attenuates noises while it detects the edges of the deep anomalies in a certain direction. The Gabor filter in mathematics is in the group of filter transformation. On this basis, this filter can process images in different directions and frequencies. The Gabor filter has many applications in image processing such as texture analysis, fingerprint detection, edge detection, document processing and so on. The 2D Gabor filter was also used for identification of the edges of the geological structures and faults. For determination of the geological trends and faults, we must apply the Gabor filter in different directions and frequencies and standard deviations. In this article, we first introduce the Gabor filter and its characteristics. Afterwards this filter is applied on the aeromagnetic data from Khoram Abad and ground magnetic from an iron mine at Kashmar regions in the the southwest and northeast of Iran respectively. 2 The real data We prepared the following maps from the real data in Khoram Abad region: the map of the total magnetic intensity, the reduction to the pole of aeromagnetic map and the map resulted from the application of the Gabor filter on the residual aeromagnetic map of the region. From the latter map, the trend and edges of the geological structures were identified. The other trends may be detected by changing the various parameters of the filter specially its direction. The trend of the geological structures which is revealed by the Gabor filter is northwest- southeast. If the Gabor filter is applied with the other edge filters such as derivative of tilt angle, analytic signal filters, we can identify the boundaries better. The Gabor filter on the data from Kashmar region, delineates the boundary of the iron ore body at the studied area.

Objective: To explore the value of contrast-enhanced CT combined with texture analysis in differentiating pancreatic cancer from mass-forming pancreatitis in pancreatic head. Methods: A retrospective study collected 21 patients with pancreatic head mass-forming pancreatitis confirmed by surgery or biopsy and 47 patients with pancreatic ductal adenocarcinoma confirmed by surgery. The patients visited the Affiliated Hospital of Nanjing University of Chinese Medicine and the First Affiliated Hospital of Wannan Medical College between January 2014 and December 2017. Gender, age and CT findings were collected. The parenchymal phase was selected for texture analysis. The minimum absolute shrinkage and selection operator (LASSO) method was applied for dimensionality reduction.Two independent sample t-tests or Mann-Whitney U test were used for continuous variables based on the Shapiro-Wilks normality test results. Categorical variables were tested by Chi-square or Fisher test. By multivariable regression analysis, CT findings, CT texture analysis, CT findings combined with texture analysis prediction models were established. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic performance of individual indicators and each prediction model. The Delong test was used to compare the area under the curve (AUC) of each model. Results: The CT findings prediction model consisted of CT value of lesion on pancreatic parenchymal phase and pancreatic duct penetrating sign. The texture analysis prediction model consists of root mean square and low grey level run emphasis_angle135. The AUC of them were not statistically different (Z=0.150,P>0.05). The combined predictive model had the better diagnostic performance (AUC 0.944, sensitivity 83.0%, specificity 95.2%, +LR 17.43, -LR 0.18) than CT sign prediction model (Z=2.008, P<0.05) and texture analysis prediction model(Z=2.236, P<0.05) were significantly different. Conclusions: The CT findings model and the texture analysis model have equivalent diagnostic performance in the differentiation of mass-forming pancreatitis and pancreatic cancer. The enhanced CT combined with texture analysis model has the best diagnostic efficiency and can further improve the diagnostic ability.