2D Gabor Wavelet Research Articles

Deteksi Pembuluh Darah pada Citra Fundus Retina Menggunakan Gabungan Metode Segementasi Pembuluh Darah Lebar dan Tipis

Organs in the human body provide a lot of knowledge about human body health. One of them is the organ in the human eye, more specifically the retina.Furthermoreeven some accute diseases can be detected through the retina image. One of the diseases that can be detected through retinal images is diabetic retinopathy. This disease can be identified through segmentation to find abnormalities in the retinal blood vessels. These abnormalities include enlarged blood vessels, abnormal branching, and so on. Manual detection of blood vessels on the retina is less accurate, so a comperhensive program is required in order to segment the blood vessels in eye fundus images, more accurately. In this research, some method and algorithms are approached by combining the segmentation process for wide vessels and the segmentation process for thin vessels. There are three stages in this research. First, the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm and 2D Gabor Wavelet are applied as the preprocessing stage. Then, active contour and region growing methods are required as the processing to segment the thin and wide vessels in fundus retina image. And the last stage is postprocessing using the logical operator method or. The trial in this research uses color eye fundus images on the DRIVE dataset consisting of 20 retinal photos. By using this dataset, the average accuracy is 94.32%, sensitivity is 77.09% and specificity is 96.04% in 20 trials.

Single Sample Discriminant Analysis Based on Gabor Transform

To solve the small sample problem of biometric identification, this paper investigates the limiting case of the problem, i.e., the recognition of a single training sample, and proposes a single sample discriminant analysis method based on Gabor wavelet and KPCA-RBF (KPRC) classifier (kernel principal component analysis-radial basis function). The proposed method performs pixel-level fusion of face and palmprint images. Firstly, a face image and a palmprint image were subject to two-dimensional (2D) Gabor wavelet transform. The resulting Gabor face image and Gabor palmprint image were fused on the pixel level into a new fused image. Next, a new classifier called KPCA-RBF was designed to extract nonlinear discriminative features by KPCA, and classify objects with RBF. Based on AR database, FERET database, and palmprint database, the single sample discriminant analysis method was realized based on Gabor transform and KPCA-RBF classifier. Experimental results show that multimodal recognition methods clearly outshine single-modal recognition methods, and the GABOR-KPRC with pixel-level fusion achieves better recognition effect than other fusion methods. It was also demonstrated that Gabor transform and KPRC classifier can effectively improve the fusion effect, whether for pixel-level fusion or decision-level fusion.

An unsupervised approach to improve contrast and segmentation of blood vessels in retinal images using CLAHE, 2D Gabor wavelet, and morphological operations

Retinopathies are the leading cause of eyesight loss, especially among diabetics. Due to the low contrast of blood vessels in fundus images, the visual inspection is a challenging job even for specialists. In this context, this work aims to implement image processing techniques to support contrast enhancement and segmentation of retinal blood vessels. The initial proposal consisted only of green channel separation, contrast limited adaptive histogram equalization, and 2D Gabor wavelet and mathematical morphology. The new proposal includes the edge and mask detection and the vessel enhancement 2D to preserve image’s characteristics. The development and validation of this work, in MatLab® environment, involved 40 images from Digital Retinal Images for Vessel Extraction (DRIVE), 20 images from Structured Analysis of the Retina (STARE), and 45 images from High-Resolution Fundus (HRF) database. In the unsupervised method context, the proposal presented the best performance regarding sensitivity and second place for balanced-accuracy on all databases. A subjective validation involving eleven ophthalmology professionals showed higher levels of acceptance (above 80%) after contrast limited adaptive histogram equalization (CLAHE) and vessel enhancement 2D steps and 75.5% for overall quality system. The main contributions refer to the inclusion of techniques for automatic mask detection, image edge removal, and suppression of vessels background to improve the retinal vessels segmentation process. In addition, this work made a computational interface named “Retinal Lab - A Tool for Fundus Image Analysis” available, which permits the users to adjust the contrast and segmentation of blood vessels in retinal images.

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.

Automatic segmentation of blood vessels in retinal images using 2D Gabor wavelet and sub-image thresholding resulting from image partition

The retina features the only blood vessel network in humans that is visible in a non-invasive imaging method. This, along with uniqueness and stability throughout life in healthy subjects, makes it an ideal target for personal identification methods in biometric systems and also for the screening and diagnosis of diseases. However, retinal images usually present low contrast of the vessels in relation to the retinal background and high level of noise stemming mainly from the acquisition process. This work aims to reduce noise and improve contrast to increase the accuracy of retinal vessel segmentation. 2D Gabor wavelet (GW) is usually employed to reduce noise and improve vessel contrast in relation to the background. In this work, it is proposed that, before the thresholding, the GW output images are partitioned into 20 sub-images in such a way that each can be treated independently. The images used were obtained from two public databases, DRIVE and STARE, and the algorithm was developed in MatLab® environment. The proposed approach reached an accuracy of 96.15%, sensitivity of 73.42%, and specificity of 98.30% in DRIVE. In STARE, the accuracy was 94.87%, sensitivity 71.74%, and specificity 96.93%. The methods proposed by the authors indicate gains in accuracy and specificity in the automatic detection of retinal vessels, in both databases used, when compared with those in the main published works. The accuracy is also higher than the 94.73% in interobserver accuracy previously determined as the gold standard.

Classification of water contamination developed by 2-D Gabor wavelet analysis and support vector machine based on fluorescence spectroscopy.

The identification of the specific categories of pollutants in the urban water supply system is necessary. Traditional detection methods are based mainly on common water quality indicators. However, inspecting these water quality indicators is made difficult by issues such as long analysis time, insufficient sensitivity, need for reagents, and generation of waste liquid. These problems hinder high-frequency water detection and monitoring. In this study, three-dimensional (3D) fluorescence spectroscopy is adopted as a monitoring method for water quality. An identification method based on two-dimensional (2D) Gabor wavelets and support vector machine (SVM) multi-classification is also proposed. The Delaunay triangulation method for interpolation is used to pre-process 3D fluorescence spectra and thereby eliminate Rayleigh scattering and Raman scattering. A 2D Gabor wavelet function generated by filters of different scales and rotation angles is proposed to extract the features of the spectra. The block statistics method, based on Gabor feature description, is employed to enhance the efficiency in describing spectra features. Then, multiple SVM classifiers are used in pollutant classification and recognition. By comparing the proposed method with principal component analysis, which is a commonly used feature extraction method, this study finds that the application of 2D Gabor wavelets and block statistics can effectively describe the characteristics of 3D fluorescence spectra. Moreover, 2D Gabor wavelets achieve high classification accuracy, especially for substances with closely positioned or overlapping characteristic peaks.

A Gabor Wavelet Based Approach for Off-Line Recognition of ODIA Handwritten Numerals

Optical Character Recognition is one of the most interesting and highly motivated areas of research, which has been very much ap-preciated in different aspect to the area of digitations world. Here in this paper we have suggested a probabilistic approach for develop-ing recognition system for handwritten Odia numerals. To report a good level of recognition of Odia scripts is quite challenging with respect to other Indian scripts .All the procedure are sequentially enclosed to develop an recognition model and report a successful recognition accuracy. Here we have performed the analysis over to standard handwritten numeral database named as IITBBS Odia Numeral Database, which is collected from IIT Bhubaneswar. In the suggestive recognition system we have adopted a 2D-Gabor wavelet transformation approach for selection of feature vector. Apart from it we have also noted down the dimensional reduction to the obtained feature vector by sustaining to PCA. In order to predict high recognition rate we have followed up by RBF Neural Network classifier. In addition to it we have also evaluate different version of RBF like Gaussian and Polynomial. Performing over 400 samples each of 10 categories (400*10) number of Odia numeral images, we have maintained a well-defined training and testing ratio in the clas-sifier and achieved 98.02%, 96.8%.recognition rate for the reported classifiers.

Automated human skull landmarking with 2D Gabor wavelets

Landmarking of CT scans is an important step in the alignment of skulls that is key in surgery planning, pre-/post-surgery comparisons, and morphometric studies. We present a novel method for automatically locating anatomical landmarks on the surface of cone beam CT-based image models of human skulls using 2D Gabor wavelets and ensemble learning. The algorithm is validated via human inter- and intra-rater comparisons on a set of 39 scans and a skull superimposition experiment with an established surgery planning software (Maxilim). Automatic landmarking results in an accuracy of 1–2 mm for a subset of landmarks around the nose area as compared to a gold standard derived from human raters. These landmarks are located in eye sockets and lower jaw, which is competitive with or surpasses inter-rater variability. The well-performing landmark subsets allow for the automation of skull superimposition in clinical applications. Our approach delivers accurate results, has modest training requirements (training set size of 30–40 items) and is generic, so that landmark sets can be easily expanded or modified to accommodate shifting landmark interests, which are important requirements for the landmarking of larger cohorts.

Detection of retinal blood vessels from ophthalmoscope images using morphological approach

Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0.9541 on DRIVE database and 0.9568, 0.9478 and 0.9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively.

Fusing Facial Texture Features for Face Recognition

Aiming at taking full advantage of facial information both in low-frequency and high-frequency regions and further improving face recognition rate, this paper constructs a robust nonsubsampled contourlet transform local binary patterns (NSCTLBP) feature and proposes a face recognition method fusing NSCTLBP and Gabor features. Firstly, face image is decomposed by NSCT, and the LBP values of NSCT high-frequency subbands are computed to construct NSCTLBP features. Meanwhile, convolution of 2D-Gabor wavelet with face image is performed to extract Gabor texture feature in low-frequency. Secondly, Euclidean distance and eigenvalue-weighted cosine (EWC) distance are adopted to explore the similarity measurement of NSCTLBP and Gabor features respectively. Finally, the face images are matched according to the weighted similarity of NSCTLBP feature and Gabor feature collaboratively. Experimental results on Yale and ORL databases show that the proposed method has better performances than that based on NSCT feature, NSCTLBP feature and Gabor feature separately against illumination, expression, and angle variations and glasses occlusion.

C6-P-09Image enhancement of electron microscope images with Gabor wavelets-based filtering

In general, an electron microscopic image is processed with some enhancement tools, such as integration types, various kinds of filtering and so on, because of the low sn ratio and low image contrast. Since, it is essentially desirable to suppress the electron dose, such an image enhancement method is significant. The Fourier filtering especially for enhancement of a crystal lattice image is well known as one among those methods. In this type of filtering (conventional case), diffraction spots and streaks appearing in a pattern of Fourier transform are extracted and the other spectra are filtered. However, because Fourier transform of an image reflects all localized lattice information, artificial lattice images are mixed in the filtered image. Fourier filtering cannot enhance local lattice images only. On the other hand, Gabor wavelets-based filtering is an alternative method to resolve the problem. It is well-known that the response characteristics of the cortical neurons are best modeled with a family of self-similar 2D Gabor wavelets [ 1 ]. We think that 2D Gabor wavelets are suitable to analyse the frequency characteristics of local lattice images. It seems that there are few applications of the Gabor wavelets filtering to electron microscopic images. Then, we have applied the Gabor wavelets filtering to some high-resolution transmission electron microscope images of locally crystalized specimens. As a result, local lattice image enhancements were achieved avoiding the artificial generation of the Fourier spectral lattice images.

An Automatic 3D Facial Landmarking Algorithm Using 2D Gabor Wavelets.

In this paper, we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets. Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data. Extracted features include texture, relief map, and transformations thereof. We extend an established 2D landmarking method for simultaneous evaluation of these data. The method is validated by performing landmarking experiments on two data sets using 21 landmarks and compared with an active shape model implementation. On average, landmarking error for our method was 1.9 mm, whereas the active shape model resulted in an average landmarking error of 2.3 mm. A second study investigating facial shape heritability in related individuals concludes that automatic landmarking is on par with manual landmarking for some landmarks. Our algorithm can be trained in 30 min to automatically landmark 3D facial data sets of any size, and allows for fast and robust landmarking of 3D faces.

Detection and Classification Brain Tumor of Magnetic Resonance Imaging Using 2D Gabor Wavelet Transform and Artificial Neural Network: A Review

Detection and Classification Brain Tumor of Magnetic Resonance Imaging Using 2D Gabor Wavelet Transform and Artificial Neural Network: A Review

Damage characterization in plates using singularity of scale mode shapes

Damage is a prevailing physical phenomenon in in-service structures; accumulation of damage can cause catastrophic structural failure. For damage identification in plates, the concept of scale mode shape with fractal singularity is formulated based on 2D Gabor wavelet transform incorporating fractal dimension analysis of measured mode shapes. With this concept, a scale fractal complexity spectrum is created to reveal mode shape singularities by eliminating noise and interference. The singularity manifests the abnormality of the mode shape, clearly indicating damage. This study develops a philosophy of fusing wavelets and fractals to detect singularities of physical fields in noisy conditions.

Retinal vessels segmentation based on level set and region growing

Retinal vessels play an important role in the diagnostic procedure of retinopathy. Accurate segmentation of retinal vessels is crucial for pathological analysis. In this paper, we propose a new retinal vessel segmentation method based on level set and region growing. Firstly, a retinal vessel image is preprocessed by the contrast-limited adaptive histogram equalization and a 2D Gabor wavelet to enhance the vessels. Then, an anisotropic diffusion filter is used to smooth the image and preserve vessel boundaries. Finally, the region growing method and a region-based active contour model with level set implementation are applied to extract retinal vessels, and their results are combined to achieve the final segmentation. Comparisons are conducted on the publicly available DRIVE and STARE databases using three different measurements. Experimental results show that the proposed method reaches an average accuracy of 94.77% on the DRIVE database and 95.09% on the STARE database.

A Method for Emphasizing Reflection Waves from Buried Objects by Using Ground‐Penetrating Radar

SUMMARYGround‐penetrating radar (GPR) is a useful tool for performing subsurface imaging by using radar pulses. In a previous paper we proposed a method for denoising GPR signals by using the 2D Gabor wavelet transform. In this paper we present a new method for emphasizing GPR reflected waves from buried objects. We evaluate the results of time–frequency analysis of the reflected waves on the basis of the Markov Chain Monte Carlo (MCMC) and Infinite Gaussian Mixture Model (IGMM) methods. Our methods are found to be effective as preprocessing for detecting the positions of buried metal pipes. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(10): 22–36, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11484

A Denoising Method for Detecting Reflected Waves from Buried Objects by Ground‐Penetrating Radar

SUMMARYGround‐penetrating radar is a tool for imaging the subsurface with radar pulses. Since a variety of media including buried objects give different dielectric constants, the positions of the buried objects can be detected on the basis of variations in the reflected return signals. This paper presents a denoising method based on the 2D Gabor wavelet transform method to solve pending problems in extracting the signals reflected from buried objects. The validity of our method is demonstrated by comparing it with the f‐k filtering method. © 2013 Wiley Periodicals, Inc. Electron Comm Jpn, 96(9): 1–13, 2013; Published online in Wiley Online Library (wileyonlinelibrary.com). DOI 10.1002/ecj.11517

Prediction of liver cirrhosis based on multiresolution texture descriptors from B-mode ultrasound

A computer aided diagnostic system to characterise normal and cirrhotic liver by multiresolution texture descriptors is proposed in this paper. The study is carried out in 120 segmented regions of interest extracted from 31 clinically acquired B-mode liver ultrasound images. Mean and standard deviation multiresolution texture descriptors derived by using 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform are considered for analysis and exhaustive search with J3 criterion of class separability is used for feature selection. The performance of subset of five most discriminative texture descriptors obtained from 2D-discrete wavelet transform, 2D-wavelet packet transform and 2D-Gabor wavelet transform is compared by using a support vector machine classifier. It is observed that only five mean multiresolution texture descriptors obtained from 2D-Gabor wavelet transform at selective scale and orientations provide highest classification accuracy of 98.33% and sensitivity of 100% by using a support vector machine classifier. The promising results indicate that the selective frequency and orientation properties of Gabor filters are extremely useful for providing multiscale texture description.

Method to eliminate the zero spectra in Fourier transform profilometry based on a cost function

To increase the accuracy, speed, and robustness of 3D measurements in Fourier transform profilometry (FTP), this paper introduces a cost function according to the intrinsic features of the amplitude and frequency modulated (AF/M) signal and proposes two new algorithms to eliminate the background components of the fringe pattern based on the proposed cost function. Finally, the standard Fourier transform (FT) is used to calculate the phase of the pattern, which no longer contains background components. The two proposed methods are both data-driven and require no parameter adjustments in advance. Theoretical analysis and 80 experimental results show that the proposed cost function is valid. The results of more than 80 experiments with different types of fringe patterns, different carrier frequencies, and different noise variances with frequency overlap and sudden phase variation show that the proposed two methods are more accurate and robust than the 2D Gabor wavelet transform, the 2D Fan wavelet transform, and the 1D complex Morlet wavelet transform profilometry, and they are approximately 70 times faster than the 1D complex Morlet wavelet transform profilometry.

Accurate two-dimensional cardiac strain calculation using adaptive windowed Fourier transform and Gabor wavelet transform

Cardiac strain calculated from tagged magnetic resonance (MR) images provides clinicians information about abnormalities of heart-wall motion in patients. It is important to develop an accurate method to determine the cardiac strain efficiently. An adaptive windowed harmonic phase (AWHARP) method is proposed for cardiac strain calculation. AWHARP is based on adaptive windowed Fourier transform (AWFT) and 2D Gabor wavelet transform (2D-GWT). The AWFT provides a spatially varying representation of the signal spectra, which allows the harmonic phase (HARP) image to be extracted with high accuracy. Instantaneous spatial frequencies are calculated using 2D-GWT, and the widths of the adaptive windows are then determined according to the instantaneous spatial frequencies for multi-resolution analysis of phase extraction. The proposed method was studied using simulated images and patients' MR images. Both single tagged images (SPAMM) and subtracted tagged images (CSPAMM) were generated using our simulation method, and their results calculated using AWHARP and HARP methods were compared. Normal and pathological tagged MR images were also processed to evaluate the performance of our method. Our experimental results show that the accuracies of phase and strain images calculated using the AWHARP method are higher than that calculated using the HARP method especially for large tag line deformation. The improvement in accuracies can be up to 3.2 strain (E1) and 17.3 calculation from MR images reveals that the cardiac strain in the end-systolic state is significantly reduced for patients with hypertrophic cardiomyopathy (HCM) compared to that of healthy subjects. The proposed AWHARP is an accurate and efficient method for cardiac strain estimation from MR images. This new algorithm can help clinicians to detect left ventricle dysfunctions and myocardial diseases with accurate cardiac strain analysis.