A Strategic Approach for 2D Texture Analysis using DTCWT, SWT and GLCM

The importance of Satellite Aperture Radar (SAR) imagery systems is increasing day-by-day in various field such as earth observation, hi-technology war mechanisms, etc. The images captured by SAR imagery systems are mainly used to detect and classify objects captured in the images. Due to the complexity of the image capturing process, SAR images can be highly noisy – often consisting of multiplicative noise, also known as speckle. To detect or classify objects in SAR images, speckle noise must be removed from images. During despeckling process, the preservation of important information in the SAR images while removing noise such as edge or patterns is a crucial task. Despeckling methods are liable to compromise on edge preservation ability while aspiring for good quality denoised images. Many researchers have proposed wavelet transform based despeckling approaches such as Discrete Wavelet Transform (DWT), Undecimated Wavelet Transform (UDWT), Dual Tree Complex Wavelet Transform (DTCWT). In these approaches, finding the best values of the coefficients plays an important role in yielding excellent denoised images with preserved edges. In this paper, we have proposed a novel optimization framework that optimizes thresholding coefficients of DTCWT despeckling method for SAR images. The proposed optimization framework is based on Fruit Fly Optimization (FOA) algorithm. The approach is a multi-objective optimization algorithm that is used to find maximum values for Peak Signal-to-Noise Ratio (PSNR), Mean Structural Similarity Index (MSSIM) and Equivalent number of look (ENL). The maximum value of MSSIM indicates high edge preservation capacity, whereas maximum PSNR value indicates good quality denoising of images. The maximum ENL value represents a good speckle-noise smoothing capability. We have applied our framework over some classical images as well as over SAR images of MSTAR dataset. In our experiments, we found that our proposed framework results in the excellent PSNR 36.87 dB, 35.4 dB and 37.8 dB, MSSIM values 0.92, 0.93 and 0.92, respectively, in the case of lena image, MSTAR dataset image and TerraSAR-X dataset image.

Alzheimer’s disease (AD) is a neurological, irreversible disorder of the brain that reduces the cognitive skills and memory ability. Alzheimer’s Disease should be diagnosed in initial phase for providing efficacious treatment to the patient. Delay in treatment may lead to the demise of patient. This paper presents a new technique for feature extraction in Alzheimer’s Disease classification. Dual Tree complex wavelet transform is applied to decompose the image into six sub images. Subsequently, Gray Level Co-occurrence Matrix is calculated from six sub images. From these matrices 22 statistical features are obtained for each sub-band image. Further, Genetic Algorithm is used to obtain the relevant and optimum features to improve the classification accuracy. Dual Tree Complex Wavelet Transform shows high directional selectivity, so it can decompose the image from six different directions and provide additional informative features. In addition, Gray Level Co¬occurrence Matrix deals with the image intensity directly, so it provides the information related with the patterns which are mutually occurring. Genetic Algorithm requires less information and provides more solutions, so it extracts optimum features and improves classification accuracy. The proposed method obtained 96.2% accuracy, 96.4% sensitivity and 95.9% specificity. The performance of the proposed work surpasses the existing traditional approaches for AD detection.

Microarray technology has become a power tool in the field of bioinformatics. It is used to measure gene expression levels and similar to any other image capturing processes is prone to noise. There are different kinds of noise, during preparation, hybridization and scanning in microarray images which usually are modeled by Gaussian noise. Since introduction of wavelets in 1970s, many more forms and extensions of this transform have been developed and used, such as stationary wavelet transform (SWT), complex wavelet transform (CWT), curvelet transform (CURV) and contourlet transform (CNT). By developing of more sparse transforms, it is important to have a perspective of how efficient the transforms are in different applications, such as microarray image analysis. In this paper, we compare the efficiency of common sparse transforms including ordinary discrete wavelet transform (DWT), SWT, CWT, CURV, CNT, Contourlet-SD decomposition, steerable pyramid (STP) and shearlet transform (SHR) for microarray image denoising. Therefore after converting microarray image into x-let transform, BayesShrink method, soft and hard thresholding are used to perform denoising of these images. Both local and general thresholds are calculated for each subband in order to evaluate the effect of incorporating intrascale dependency on top of sparsity property in statistical modeling of x-let's coefficients. Our simulation results show that CWT and SHR outperforms the others when using global thresholding and SWT is the preferred transform when using local thresholding. Although STP and SHR have better performance for some criteria like structural similarity (SSIM) index, but CWT is faster.

Background: During the two past decades, medical image fusion has become an essential part ofmodern medicine due to the availability of numerous imaging modalities (e.g., MRI, CT, SPECT,etc.). This paper presents a new medical image fusion algorithm based on PCA and DTCWT,which uses different fusion rules to obtain a new image containing more information than any ofthe input images.Methods: A new image fusion algorithm improves the visual quality of the fused image, based onfeature selection and Principal Component Analysis (PCA) in the Dual-Tree Complex WaveletTransform (DTCWT) domain. It is called Feature Selection with Principal Component Analysisand Dual-Tree Complex Wavelet Transform (FSPCA-DTCWT). Using different fusion rules in asingle algorithm result in correctly reconstructed image (fused image), this combination willproduce a new technique, which employs the advantages of each method separately. The DTCWTpresents good directionality since it considers the edge information in six directions and providesapproximate shift invariant. The main goal of PCA is to extract the most significant characteristics(represented by the wavelet coefficients) in order to improve the spatial resolution. The proposedalgorithm fuses the detailed wavelet coefficients of input images using features selection rule.Results: Several experiments have been conducted over different sets of multimodal medicalimages such as CT/MRI, MRA/T1-MRI. However, due to pages-limit on a paper, only results ofthree sets have been presented. The FSPCA-DTCWT algorithm is compared to recent fusionmethods presented in the literature (eight methods) in terms of visual quality and quantitativelyusing well-known fusion performance metrics (five metrics). Results showed that the proposedalgorithm outperforms the existing ones regarding visual and quantitative evaluations.Conclusion: This paper focuses on medical image fusion of different modalities. A novel imagefusion algorithm based on DTCWT to merge multimodal medical images has been proposed.Experiments have been performed using two different sets of multimodal medical images. Theresults show that the proposed fusion method significantly outperforms the recent fusiontechniques reported in the literature.

Discrete Wavelet Transform (DWT) has demonstrated advantages in image compression due to its time-frequency resolution property. Dual Tree Complex Wavelet Transform (DTCWT) in addition to the advantages of DWT supports additional properties such as directionality and shift invariant. DTCWT has coefficient redundancy of 4:1 or 2 m :1 in m dimensions thus is four time complex than DWT computation. In this work, DTCWT based image compression algorithm is proposed that performs decomposition using DTCWT and reconstruction is carried out using DWT. The DTCWT bands are companded and encoded using entropy encoder and the quantization of the sub bands introduces minimum loss. The proposed algorithm achieves 77% compression with 40 dB PSNR thus suitable for real time image compression. DTCWT filters are appropriately selected to achieve perfect reconstruction.

Lately, 3D imaging techniques have achieved a lot of progress due to recent developments in 3D sensor technologies. This leads to a great interest regarding 3D image feature extraction and classification techniques. As pointed out in literature, one of the most important and discriminative features in images is the textural content. Within this context, we propose a texture feature extraction technique for volumetric images with improved discrimination power. The method could be used in textured volumetric data classification tasks. To achieve this, we fuse two complementary pieces of information, feature vectors derived from Local Binary Patterns (LBP) and the Gray-Level Co-occurrence Matrix-based methods. They provide information regarding the image pattern and the contrast, homogeneity and local anisotropy in the volumetric data, respectively. The performance of the proposed technique was evaluated on a public dataset consisting of volumetric textured images affected by several transformations. The classifiers used are the Support Vector Machine, k-Nearest Neighbours and Random Forest. Our method outperforms other handcrafted 3D or 2D texture feature extraction methods and typical deep-learning networks. The proposed technique improves the discrimination power and achieves promising results even if the number of images per class is relatively small.

Cervical cancer leads to major death disease in women around the world every year. This cancer can be cured if it is initially screened and giving timely treatment to the patients. This paper proposes a novel methodology for screening the cervical cancer using cervigram images. Oriented Local Histogram Technique (OLHT) is applied on the cervical image to enhance the edges and then Dual Tree Complex Wavelet Transform (DT-CWT) is applied on it to obtain multi resolution image. Then, features as wavelet, Grey Level Co-occurrence Matrix (GLCM), moment invariant and Local Binary Pattern (LBP) features are extracted from this transformed multi resolution cervical image. These extracted features are trained and also tested by feed forward back propagation neural network to classify the given cervical image into normal and abnormal. The morphological operations are applied on the abnormal cervical image to detect and segment the cancer region. The performance of the proposed cervical cancer detection system is analyzed in the terms of sensitivity, specificity, accuracy, positive predictive value, negative predictive value, Likelihood Ratio positive, Likelihood ratio negative, precision, false positive rate and false negative rate. The performance measures for the cervical cancer detection system achieves 97.42% of sensitivity, 99.36% of specificity, 98.29% of accuracy, PPV of 97.28%, NPV of 92.17%, LRP of 141.71, LRN of 0.0936, 97.38 % precision, 96.72% FPR and 91.36% NPR. From the simulation results, the proposed methodology outperforms the conventional methodologies for cervical cancer detection and segmentation process.

Watermarking is one of the widely used applications to provide security for the content shared over internet. Internet is a place where the data is not of assured security to the fullest. DTCWT is widely used in all image processing applications over a decade. A novel imperceptible video watermarking scheme is proposed. This method is implemented with the use of Dual Tree Complex Wavelet Transform (DTCWT) and Singular value decomposition (SVD) which helps in proof of ownership. In this method Singular Value Decomposition is applied to the Dual Tree Complex Wavelet Transform (DTCWT) coefficients of both watermark and original image and the singular Eigen values are interchanged. Because of advantages, Shift invariance and Directionality we prefer DTCWT. As SVD decomposes the matrix into 3 matrices U, S, V, we need U, V at the time of extraction. So we use U, V as watermark in audio. Watermark extracted from the audio gives the U, V matrices which makes this method blind watermarking. In order to prove the robustness of the method the results are compared with similar algorithms proposed in this paper but with DWT.

Problem statement: Recently, there has been a huge progress in collection of varied image databases in the form of digital. Most of the users found it difficult to search and retrieve required images in large collections. In order to provide an effective and efficient search engine tool, the system has been implemented. In image retrieval system, there is no methodologies have been considered directly to retrieve the images from databases. Instead of that, various visual features that have been considered indirect to retrieve the images from databases. In this system, one of the visual features such as texture that has been considered indirectly into images to extract the feature of the image. That featured images only have been considered for the retrieval process in order to retrieve exact desired images from the databases. Approach: The aim of this study is to construct an efficient image retrieval tool namely, “Content Based Medical Image Retrieval with Texture Content using Gray Level Co-occurrence Matrix (GLCM) and k-Means Clustering algorithms”. This image retrieval tool is capable of retrieving images based on the texture feature of the image and it takes into account the Pre-processing, feature extraction, Classification and retrieval steps in order to construct an efficient retrieval tool. The main feature of this tool is used of GLCM of the extracting texture pattern of the image and k-means clustering algorithm for image classification in order to improve retrieval efficiency. The proposed image retrieval system consists of three stages i.e., segmentation, texture feature extraction and clustering process. In the segmentation process, preprocessing step to segment the image into blocks is carried out. A reduction in an image region to be processed is carried out in the texture feature extraction process and finally, the extracted image is clustered using the k-means algorithm. The proposed system is employed for domain specific based search engine for medical Images such as CT-Scan, MRI-Scan and X-Ray. Results: For retrieval efficiency calculation, conventional measures namely precision and recall were calculated using 1000 real time medical images (100 in each category) from the MATLAB Workspace database. For selected query images from the MATLAB-Image Processing tool Box-Workspace Database, the proposed tool was tested and the precision and recall results were presented. The result indicates that the tool gives better performance in terms of percentage for all the 1000 real time medical images from which the scalable performance of the system has been proved. Conclusion: This study proposed a model for the Content Based Medical Image Retrieval System by using texture feature in calculating the Gray Level Co Occurrence matrix (GLCM) from which various statistical measures were computed in order to increasing similarities between query image and database images for improving the retrieval performance along with the large scalability of the databases.

In this paper, a novel image denosing scheme is proposed by applying 2D dual-tree complex wavelet transform (DTCWT) to the second bandelet transform. Compared with traditional discrete wavelet transform (DWT), the DTCWT has nearly shift invariant and directionally selective in two and higher dimensions important properties, which are suitable for image denoising. The bandelet transform has offer an asymptotically optimal representation for geometric images, but it lacks translation invariant. Firstly, the DTCWT is employed to obtain subands, and then bandeletization is implemented in each subband. During finding the best geometrical flow and optimal quadtree segment, the cost term of Lagrangian is confirmed by Bayes estimator. At last, Bayes soft-threshold shrinkage denoising in bandelet transform domain is implemented. The experiments show that the proposed denoising method has better results than several DTCWT-based methods for image with rich directional features. The denoised images recovered by the proposed scheme are visually more appealing.

This paper introduces a novel DT-CWT feature-based Two-dimensional Inverse FDA (2DIFDA) by integrating the Dual-Tree Complex Wavelet Transform (DT-CWT) of face images and 2DIFDA method for face recognition. The DT-CWT has approximate shift invariance, good directional selectivity and can provide effective feature representation for face images. In the proposed method, DT-CWT is first used to extract the face image features at different scales and orientations. 2DIFDA is then applied for feature selection and dimensionality reduction in the DT-CWT feature space. Experimental results on ORL and FERET face databases demonstrate the feasibility of the new method.

A novel hashing scheme for Depth-image-based-rendering 3D images

Biometric technology is considered as more effective technology for identification and recognition of individuals in most secure environments. Face recognition is a technique based on physiological biometric characteristics. In this paper, we propose Face Recognition using DT-CWT and LBP Features (FRDL) for different databases. The original face image is resized to uniform dimensions of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> × 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sup> . The five levels Dual-Tree Complex Wavelet Transform (DT-CWT) is applied on face image to obtain DT-CWT coefficients. The matrix of DT-CWT coefficients is segmented in to 3×3 matrixes. The Local Binary Pattern (LBP) algorithm is applied on each 3×3 matrix to get final features. The Euclidean Distance (ED) is used to compare features of test face image with data base images. It is observed that the values of False Rejection Rate (FRR), False Acceptance Rate (FAR) and Total Success Rate (TSR) are better in the proposed model compare to existing method.

In this paper, a novel algorithm which integrates the RGB color histogram and texture features for content based image retrieval. A new set of two- dimensional (2-D) M-band dual tree complex wavelet transform (M_band_DT_CWT) and rotated M_band_DT_CWT are designed to improve the texture retrieval performance. Unlike the standard dual tree complex wavelet transform (DT_CWT), which gives a logarithmic frequency resolution, the M-band decomposition gives a mixture of a logarithmic and linear frequency resolution. Most texture image retrieval systems are still incapable of providing retrieval result with high retrieval accuracy and less computational complexity. To address this problem, we propose a novel approach for image retrieval using M_band_DT_CWT and rotated M_band_DT_CWT (M_band_DT_RCWT) by computing the energy, standard deviation and their combination on each subband of the decomposed image. To check the retrieval performance, two texture databases are used. Further, it is mentioned that the databases used are Brodatz gray scale database and MIT VisTex Color database. The retrieval efficiency and accuracy using proposed features is found to be superior to other existing methods. Index Terms—Color; M-band wavelets; Feature Extraction; M-band dual tree complex wavelets; Image Retrieval.

Introduction: The electrocardiogram (ECG) signal is important for early diagnosis of heart abnormalities. Type 2 diabetic individuals’ ECG signals provide pertinent data about their heart and are one of the most important diagnostic techniques used by doctors to identify Cardiovascular Disease (CVD). Bidirectional Recurrent Neural Network (RNN) classifies the features linked to normal and abnormal stage ECG signal. Aim: To analyse ECG signals of type 2 diabetic patients for early prediction of CVDs using feature extraction and bidirectional RNN based classification. Materials and Methods: This was a secondary data-based modelling study at Shri Ramasamy Memorial University Sikkim, India from December 2020 to January 2022. Different noises were removed by hybrid preprocessing filter made up of a Median and Savitzky-Golay filter. Undecimated Dual Tree Complex Wavelet Transform (UDTCWT) along with Detrended fluctuation (DA) analysis and Empirical Orthogonal Function (EOF) analysis were then used to extract features. These features were classified with Bidirectional RNN. Results: The proposed method was tested on the MIT-BIH, Physionet and DICARDIA databases, and the findings showed that it achieves an average accuracy of 97.6% when compared to the conventional techniques. Conclusion: The proposed method proves to be the most effective way for detecting anomalies in ECG signals in both the early and pathological stages. This method is also effective to diagnose the early intervention of cardiovascular symptoms.