Gabor Wavelet Method Research Articles

Computer-aided Diagnosis applied to MRI images of Brain Tumor using Spatial Fuzzy Level Set and ANN Classifier

The most vital organs in the human body are the brain, heart, and lungs. Because the brain controls and coordinates the operations of all other organs, normal brain function is vital. Brain tumour is a mass of tissues which interrupts the normal functioning of the brain, if left untreated will lead to the death of the subject. The classification of multiclass brain tumours using spatial fuzzy based level sets and artificial neural network (ANN) techniques is proposed in this paper. In the proposed method, images are preprocessed using Median Filtering technique, the boundaries of the Brain Tumor are obtained using Spatial Fuzzy based Level Set method, features are extracted using Gabor Wavelet and Gray-Level Run Length Matrix (GLRLM) methods. Finally ANN technique is used for the classification of the image into Normal or Benign Tumor or Malignant Tumor. The proposed method was implemented in the MATLAB working platform and achieved classification accuracy of 94%, which is significant compared to state-of-the-art classification techniques. Thus, the proposed method assist in differentiating between benign and malignant brain tumours, enabling doctors to provide adequate treatment.

Hybridization for Classification and Identification of Individuals using Multimodal Biometric Systems

Biometric recognition systems use certain human characteristics such as voice, facial features, fingerprint, iris or hand geometry to identify an individual or verify their identity. These systems have been developed individually for each of these biometric modalities until they achieve remarkable levels of performance. Biometrics is a measure of biological characteristics for the identification or authentication of an individual based on some of its characteristics. Although biometric recognition techniques promise to be very effective, At present, we can not guarantee an excellent identification rate based on a single biometric signature with unimodal biometric systems. Thus the error rates of unimodal biometric systems are relatively high due to all these practical problems, which makes them impractical for the use of critical safety applications. To resolve these problems, a solution is used in the same system in several biometric modalities, called a multimodal biometric system (MBS). MBSs combine different modalities in a unique recognition system. The multimodal fusion allows improving the results obtained by a single biometric characteristic and making the system more robust to noise and interference and more resistant to possible attacks. Fusion may be carried out at the level of signals acquired by the different sensors, of the parameters obtained for each modality, of the scores provided by unimodal experts or of the decision taken by said experts. In the case of fusion, the features obtained from the various biometric methods must be homogenized before the process of fusion is accomplished. This article describes the evolution of a multi-modal biometric identification system depends on 3 biometrics-face, iris & fingerprint. Feature extraction is done using the Gabor Wavelet method and classification is accomplished using the Random Forest classifier. This proposed method is applicable in real-life applications to identify biometric for offices, hospitals, and colleges/universities and so on.

Sistem Deteksi Biometrik Keunikan Wajah Secara Real Time

<table width="605" border="0" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="382"><p>Facial recognition is important for identifying a person's biodata profile. The physical development of students from the time they entered college to graduation has experienced inconspicuous changes but it is sometimes difficult to identify faces one by one. Digital form is becoming a trend to remember more real time. An important part of human physical identification has begun to shift from signature - finger - face selection. The face includes five important senses that are interconnected into an identification device. In this study the focus is on face detection based on color, the application of the Camshift Algorithm and finding the distance between the face sensing points is the result of the Gabor Wavelet method. Training data uses 4-8 second real time video. The hue histogram is basically the same as the RGB histogram, the difference is that the hue histogram uses the Hue value instead of RGB because the hue value represents natural color without regard to lighting. Gabor Wavelet transform is provided to solve filter design problems. The face detection system looks for face points to form a frame-shaped face selection if previously the face has been stored in a database so the system can easily describe biodata. Face selection can be done on live testing data. The selection box detection follows every facial movement.</p></td></tr></tbody></table>

Investigation of severity level of diabetic retinopathy using adaboost classifier algorithm

Diabetics is a severe disease which affects the different parts of the human body. Due to variations of sugar level the blood vessels present in the retina get damaged. This disease is known as Diabetic Retinopathy (DR). Early identification of diabetic retinopathy will prevent vision loss. Exudates, Haemorrhages, Micro aneurysms are the primary symptoms of Diabetic Retinopathy. To diagnose the abnormalities, the ophthalmologists use fundus images which are captured after applyingdrugs inside the patient’s eye. These drugsmay causeinconvenience to the patient’s eyes. So we developed an automatedsystem to detect retinal lesions with help of non-dilated retinal fundus images. In our proposed system pre-processed images are segmented by new technology called region growing method. In this approach after pre-processing step, certain features are extracted by using Gabor Wavelet method. Adaptive boosting classifier is proposed to investigate the abnormality level of the disease. This result is compared with various segmentation techniques. This method achieved sensitivity of 100% and specificity of 98.8% with accuracy of 98.4%.

Real Time Catalog of Uniqueness Face Using the CAMShift and Gabor Wavelet Algorithims

Moving image processing uses continuous moving images such as recording live video as a data set. The process of developing a unique database catalog application of student face recognition begins with storing the results of real time recordings of each face. Face tracking uses the CamShift algorithm to identify skin color based on the Hue histogram value. To bring out the special features of a face image that has been convoluted to the kernel, the Gabor Wavelet method is used. 2D Gabor Wavelet kernel which is obtained by modulating 2D sine waves at certain frequencies and orientations with a gaussian envelope. Training data taken from the live video of student activities consists of 30 data sets. Stages start from file conversion - tracking video to multiple frames - RGB-HSV conversion - Calculate Histogram - Face detection and finally face recognition process. If there are similarities with the largest value between querying the face image in the database, the face will be detected. Looping is done to take the face of other students. Video .mp4 is converted to .avi. The results of video conversion are tracked into several frames according to facial movements followed by RGB - HSV conversion, 1-2 minute duration videos produce 40-250 frames. Face recognition successfully identifies moving faces around 80%-90% with skin color detection and 80%-85% with points position detection.

Biometric Patterns Of Eye Iris By Using Hidden Markov Model

Biometrics is an individual recognition system that consists of two kinds of modern and traditional. A biometric system is essentially a personal identification system by determining the authenticity of a physiological characteristic, or a particular behavior that the user possesses. Such biometric technology is defined as an automated method of identifying and authenticating a person’s identity based on the user’s physiological and behavioral characteristics. The purpose of this research is to know the accuracy level of iris pattern identification using Hidden Markov Model with Gabor wavelet method, to know the iris feature extraction process as part of image processing process using Gabor wavelet method. The method used for data processing is by using Hidden Markov Model, Gabor Wavelet and 2D Order Statistic Filter. The accuracy of the iris identification system indicates that using the Hidden Markov Model with the Gabor Wavelet filter provides a lower accuracy value than using the 2D Order Statistic Filter (ORDFILT2). Gabor Wavelet filter gives 91.50% accuracy level while ORDFILT2 Filter gives 98.5% result.

Classification of polycystic ovary based on ultrasound images using competitive neural network

Infertility in the women reproduction system due to inhibition of follicles maturation process causing the number of follicles which is called polycystic ovaries (PCO). PCO detection is still operated manually by a gynecologist by counting the number and size of follicles in the ovaries, so it takes a long time and needs high accuracy. In general, PCO can be detected by calculating stereology or feature extraction and classification. In this paper, we designed a system to classify PCO by using the feature extraction (Gabor Wavelet method) and Competitive Neural Network (CNN). CNN was selected because this method is the combination between Hemming Net and The Max Net so that the data classification can be performed based on the specific characteristics of ultrasound data. Based on the result of system testing, Competitive Neural Network obtained the highest accuracy is 80.84% and the time process is 60.64 seconds (when using 32 feature vectors as well as weight and bias values respectively of 0.03 and 0.002).

An implementation of Elman neural network for polycystic ovary classification based on ultrasound images

Polycystic Ovary Syndrome (PCOS) is a reproduction problem that causes irregular menstruation period. Insulin and androgen hormone have big roles for this problem. This syndrome should be detected shortly, since it is able to cause a more serious disease, such as cardiovascular, diabetes, and obesity. The detection of this syndrome is done by analyzing ovary morphology and hormone test. However, the more economical way of test is by identifying the ovary morphology using ultrasonography. To classify whether one ovary is normal or it has polycystic ovary (PCO) follicle, the analysis will be done manually by a gynecologist. This paper will design a system to detect PCO using Gabor Wavelet method for feature extraction and Elman Neural Network is used to classify PCO and non-PCO. Elman Neural Network is chosen because it contains context layer to recall the previous condition. This paper compared the accuracy and process time of each dataset, then also did testing on elman’s parameters, such as layer delay, hidden layer, and training function. Based on tests done in this paper, the most accurate number is 78.1% with 32 features.

Liver Lesions Detection and Classification in Ultrasound Images Using Gabor Characterization, Edge Detection and Artificial Neural Networks

Introduction: In the last decade one of the main reasons for people mortality and disability is liver diseases. Early detection of these diseases can help adopt appropriate treatment methods. Ultrasound imaging is a non-invasive method for visualizing tissue specification and liver lesions detection which its resolution is lower than CT and MRI images. Precise determination of liver tissue lesions and progression degree of disease is possible with advanced computer techniques such as artificial neural networks (ANN) from medical images. In this paper, a classification-based method is presented to identify and diagnose liver lesions using the Gabor wavelet features and edge detection. In this method, the vector of features from healthy and damaged tissues is trained to the network based on Gabor filters. Then the suspected cases of tissue lesions in various liver diseases are identified by features extraction of entry images. After that, the edge detection technique is implemented and the internal points of the edge are tested as an inputs of a neural network which determine the healthy and unhealthy liver tissues.
 Methods: Image features are extracted and processed by Gabor wavelet. Also the ANN is used to liver disease classification based on the images features. The forward multilayer perceptron neural network is organized with three layers of input, hidden and output. The training of this network is done with back propagation method and all of the data include "healthy tissues" and "damaged tissues" of the liver are collected in a large cellular array. Furthermore, an edge detection technique is used to indicate the points where the intensity of the light changes sharply. The sharp changes in image characteristics are usually representative of important events and changes in environments characteristics.
 Results: The results of the implementation indicate a significant reduction in processing time of liver ultrasound images and also increase the precision and accuracy of liver lesions detection (approximately 5%) among different classified groups of hepatic patients compared with the similar image processing methods. In the proposed method, the total time of operations include feature extraction, image processing, lesions detection and diagnosis of the disease has been decreased by reduction of the number of examined points. In addition, an edge detection technique had been used to diagnose the size of damaged tissues in various liver diseases, which helps improve the early detection of tissue lesions because of reduction of the checking domain of points.
 Conclusion: In this paper, a new method was presented to identify liver tissue lesions. Gabor wavelet method is employed to extract the features of the liver ultrasound images. These wavelets provide the context to understand the images frequency and their analysis in the area of the space, and given their great advantage, which is slow changes in the frequency domain, it is an appropriate filter to extract the image features. Then, the extracted features of the ultrasound images of various liver patients are stored to train a neural network, and finally the image processing method is performed to identify the healthy and damaged tissues and also to diagnose the type of disease. The search scope of problem is minimized as the input of the neural network to find the liver damaged tissue by the edge detection technique which is lead to errors reduction in identifying the tissue damages, increasing the detection speed of these lesions, and diagnosing the disease as well as determining the damage degree of liver.

Vessel extraction in retinal images using automatic thresholding and Gabor Wavelet.

Retinal image analysis has been widely used for early detection and diagnosis of multiple systemic diseases. Accurate vessel extraction in retinal image is a crucial step towards a fully automated diagnosis system. This work affords an efficient unsupervised method for extracting blood vessels from retinal images by combining existing Gabor Wavelet (GW) method with automatic thresholding. Green channel image is extracted from color retinal image and used to produce Gabor feature image using GW. Both green channel image and Gabor feature image undergo vessel-enhancement step in order to highlight blood vessels. Next, the two vessel-enhanced images are transformed to binary images using automatic thresholding before combined to produce the final vessel output. Combining the images results in significant improvement of blood vessel extraction performance compared to using individual image. Effectiveness of the proposed method was proven via comparative analysis with existing methods validated using publicly available database, DRIVE.

Face recognition technology development with Gabor, PCA and SVM methodology under illumination normalization condition

Face recognition is a challenging research field in computer sciences, numerous studies have been proposed by many researchers. However, there have been no effective solutions reported for full illumination variation of face images in the facial recognition research field. In this paper, we propose a methodology to solve the problem of full illumination variation by the combination of histogram equalization (HE) and Gaussian low-pass filter (GLPF). In order to process illumination normalization, feature extraction is applied with consideration of both Gabor wavelet and principal component analysis methods. Next, a Support Vector Machine classifier is used for face classification. In the experiments, illustration performance was compared with our proposed approach and the conventional approaches with three different kinds of face databases. Experimental results show that our proposed illumination normalization approach (HE_GLPF) performs better than the conventional illumination normalization approaches, in face images with the full illumination variation problem.

Experimental comparison of approaches for feature extraction of facial attributes

In this paper, we compare the effectiveness of widely used approaches for representation of facial features in face images. Feature extraction is performed on face images for representation of four facial attributes, namely gender, age, race, and expression, by using discrete wavelet transform (DWT), Gabor wavelet, scale-invariant feature transform, local binary pattern (LBP), and Eigenfaces. After feature extraction and dimension reduction, demographic and expression classification is performed to identify the most discriminating techniques for representation of facial features. Extensive experiments are performed using publicly available face databases, namely Yale, Face95 Essex, and Cohn-Kanade (CK+) databases. Experimental results show that DWT, LBP, and Gabor wavelet methods are robust to variations of illumination, facial expression, and geometric transformations. Experimental results also show that race and expression are more difficult to predict than gender and age.

In Vivo Collagen Remodeling in the Vascular Wall of Decellularized Stented Tissue-Engineered Heart Valves.

Decellularized tissue-engineered heart valves (TEHVs) are under investigation as alternative for current heart valve prostheses with the potential to rapidly repopulate with cells within the body. Ideally, these valves are stented for transapical or minimally invasive delivery. It is unclear if and how the matrix of these valves remodels under in vivo hemodynamic loading conditions and in the presence of a stent. Here, we study the evolution of collagen orientation and tissue maturation in the wall of stented decellularized TEHVs with time after implantation. In a previous study, stented TEHVs based on rapidly degrading scaffolds were cultured in bioreactors, decellularized, and transapically implanted as pulmonary valve replacement in sheep. In the present study, collagen (re)orientation in the initially isotropic valvular wall was assessed using a fluorescent collagen probe combined with confocal imaging and image analysis of explanted tissue at 8, 16, and 24 weeks following implantation. Collagen tortuosity or waviness in the explants, as a measure of matrix maturity, was quantified using a Gabor wavelet method and compared with tortuosity in native sheep vascular wall tissue. Results indicate that on the luminal side of the valvular wall, fibers became aligned in circumferential direction, while tortuosity increased with implantation time, showing striking similarities with the native collagen structure after 24 weeks. On the outside of the wall, where the engineered tissue touches the stent, collagen fibers in the vicinity of the struts aligned along the struts, whereas collagen fibers in between struts were randomly oriented. Immunohistochemistry was performed to evaluate the presence of elastin and collagen type I and III. After 8 weeks, collagen types I and III were mostly present at the luminal side of the wall, whereas at 16 and 24 weeks, a homogenous distribution of collagen I and III was observed throughout the wall. Elastin was mostly expressed at the luminal side after 24 weeks. Biochemical assays showed that the amount of DNA (as a measure of cell number) increased significantly after 8 and 24 weeks, glycosaminoglycans increased significantly after 8, 16, and 24 weeks, and hydroxyproline, as a measure of collagen amount, increased significantly after 24 weeks compared to the controls. The collagen matrix in the wall of decellularized TEHVs shows clear structural remodeling and maturation with time. While collagen orientation rapidly remodels toward a native anisotropic architecture on the luminal side of the engineered valvular wall, it is dominated and guided by stent geometry on the outer side of the wall. Collagen tortuosity was increased with implantation time and was accompanied by an increase in elastin, especially on the luminal side of the vessel.

Automatic classification of woven fabric structure based on texture feature and PNN

In today’s textile industry, the classification of woven fabrics is usually manual which requires considerable human efforts and a long time. With the rapid development of computer vision, the automatic and efficient methods for woven fabric classification are desperately needed. This paper proposes an automatic and real-time classification method to analyze three woven fabrics: plain, twill and satin weave. The methodology involves two approaches to extract texture features, that is, gray-level co-occurrence matrix (GLCM) and Gabor wavelet. Then, principal component analysis (PCA) is utilized to deal with the texture feature vectors to gain minimize redundancy and maximize principal component feature vectors. Finally, in the classification phase, probabilistic neural network (PNN) is applied to classify three basic woven fabrics. With strong realtime, fault-tolerance and non-linear classification capability, PNN can be a promising tool for classification of woven fabrics. The experimental results show that PNN classifier with faster training speed can classify woven fabrics accurately and efficiently. Besides, compared with GLCM method and Gabor wavelet method, the fusion of the two feature vectors obtains the best classification result (95 %).

A Comparison and Evaluation of Computerized Methods for Blood Vessel Enhancement and Segmentation in Retinal Images

Diabetic retinopathy is caused by complications of diabetes, which can eventually lead to blindness. As new blood vessels form at the back of the eye as a part of diabetic retinopathy (DR), they can bleed and blur vision. Detection of these new vessels and their structure in retinal images is very important for diagnosis of diabetic retinopathy. In this paper two different techniques have been compared. First technique uses Gaussian filtering for preprocessing, LoG filtering for enhancement and adaptive thresholding for segmentation purpose. Second technique uses unsharp masking for preprocessing, Gabor wavelet for enhancement and global thresholding for segmentation. The performance of these systems is evaluated on publicly available DRIVE and STARE databases of manually labeled images. Experimental results show that Gabor wavelet method gives best results for vessel enhancement and global threshold gives good results for vessel segmentation in retinal images. Index Terms—Blood vessel, DIABETIC retinopathy (DR), retinal images, unsharp masking, gabor wavelet transform, adaptive thresholding, log filtering, enhancement, segmentation.

Molecular Image Segmentation Based on Improved Fuzzy Clustering

Segmentation of molecular images is a difficult task due to the low signal-to-noise ratio of images. A novel two-dimensional fuzzy C-means (2DFCM) algorithm is proposed for the molecular image segmentation. The 2DFCM algorithm is composed of three stages. The first stage is the noise suppression by utilizing a method combining a Gaussian noise filter and anisotropic diffusion techniques. The second stage is the texture energy characterization using a Gabor wavelet method. The third stage is introducing spatial constraints provided by the denoising data and the textural information into the two-dimensional fuzzy clustering. The incorporation of intensity and textural information allows the 2DFCM algorithm to produce satisfactory segmentation results for images corrupted by noise (outliers) and intensity variations. The 2DFCM can achieve 0.96 ± 0.03 segmentation accuracy for synthetic images under different imaging conditions. Experimental results on a real molecular image also show the effectiveness of the proposed algorithm.

Evaluation of feature extraction methods for classification of liver abnormalities in ultrasound images

Image analysis techniques have played an important role in several medical applications. In this paper, the classification of ultrasonic liver images is studied by using texture features extracted from Laws' method, Autocorrelation method, Edge frequency methods, Gabor Wavelet method and Co-occurrence probability method. Then the best features from different methods are combined to improve the classification. The features from these methods are used to classify four sets of ultrasonic liver images – Normal, Cyst, Benign and Malignant, and how well they suit in classifying the abnormalities is reported. A Neural Network classifier is employed to evaluate the performance of these features based on their recognition ability.

Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition

This paper introduces a novel Gabor-Fisher (1936) classifier (GFC) for face recognition. The GFC method, which is robust to changes in illumination and facial expression, applies the enhanced Fisher linear discriminant model (EFM) to an augmented Gabor feature vector derived from the Gabor wavelet representation of face images. The novelty of this paper comes from 1) the derivation of an augmented Gabor feature vector, whose dimensionality is further reduced using the EFM by considering both data compression and recognition (generalization) performance; 2) the development of a Gabor-Fisher classifier for multi-class problems; and 3) extensive performance evaluation studies. In particular, we performed comparative studies of different similarity measures applied to various classifiers. We also performed comparative experimental studies of various face recognition schemes, including our novel GFC method, the Gabor wavelet method, the eigenfaces method, the Fisherfaces method, the EFM method, the combination of Gabor and the eigenfaces method, and the combination of Gabor and the Fisherfaces method. The feasibility of the new GFC method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects, which were acquired under variable illumination and facial expressions. The novel GFC method achieves 100% accuracy on face recognition using only 62 features.