Wave Atom Transform Research Articles

Image Fusion using Waveatom Transform for Medical Applications

Image fusion is the process of gathering all the important information of multiple images into a single fused image. It is used in many fields like medical, military, remote sensing etc... Image fusion in a medical field is extensively used by physicians for analysis and treatment, as the fused image contains complementary features present in different medical images obtained from imaging devices. The wave atom transform based medical image fusion is proposed as the potential capabilities of wave atoms have been exploded in many applications like image denoising, fingerprint identification, compression etc. Medical image techniques became important tool for medical diagnosis and treatment. Medical images of different modalities such as MRI, CT, PET, SPECT provide information in a limited domain. In medical resources, MRI and CT images contain structural information whereas PET and SPECT images contain functional Information. Thus, single modality does not provide complete and accurate information, both structural as well as functional information is required. So far, many medical image fusion methods have been developed. The easiest way to perform image fusion is to perform weighted average of input images pixel by pixel. But this method produces side effects such as artifacts & contrast reduction. The proposed method is experimented on various medical images and compared with recent state of fusion methods. Results prove that images obtained from proposed method have better clarity and enhanced information and are practically more helpful for better treatment and quick analysis.

Image Denoising Based on Statistical Nearest Neighbor and Wave Atom Transform

Image Denoising Based on Statistical Nearest Neighbor and Wave Atom Transform

Lightweight and Privacy-Preserving Template Generation for Palm-Vein-Based Human Recognition

The use of human biometrics is becoming widespread and its major application is human recognition for controlling unauthorized access to both digital services and physical localities. However, the practical deployment of human biometrics for recognition poses a number of challenges, such as template storage capacity, computational requirements, and privacy of biometric information. These challenges are important considerations, in addition to performance accuracy, especially for authentication systems with limited resources. In this paper, we propose a wave atom transform (WAT)-based palm-vein recognition scheme. The scheme computes, maintains, and matches palm-vein templates with less computational complexity and less storage requirements under a secure and privacy-preserving environment. First, we extract palm-vein traits in the WAT domain, which offers sparser expansion and better capability to extract texture features. Then, the randomization and quantization are applied to the extracted features to generate a compact, privacy-preserving palm-vein template. We analyze the proposed scheme for its performance and privacy-preservation. The proposed scheme obtains equal error rates (EERs) of 1.98%, 0%, 3.05%, and 1.49% for PolyU, PUT, VERA and our palm-vein datasets, respectively. The extensive experimental results demonstrate comparable matching accuracy of the proposed scheme with a minimum template size and computational time of 280 bytes and 0.43 s, respectively.

Effective EMG denoising using a hybrid model based on WAT and GARCH

Abstract EMG is the recording of electrical activity of the muscles that can be used in diagnosing muscular diseases like myopathy and neuropathy and in generating control signals for operating artificial prosthetic arms and limbs. Various forms of artifacts get introduced into the EMG during the acquisition process that limits its proper analysis and characterization. This paper proposes a novel noise suppression method for EMG signals employing orthogonal Wave Atom Transform (WAT) followed by statistical modelling of wavelet coefficients by using a Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model with maximum a-posteriori (MAP) estimator for signal recovery from noisy EMG signals. The proposed algorithm was evaluated on signals taken from the standard Physionet ATM database that was manually corrupted by different levels of Additive White Gaussian Noise (AWGN) noise. The efficiency of the proposed algorithm is studied using the standard metrics namely, Signal to Noise Ratio (SNR) in dB, and Percent Root Mean Square Deviation (PRD). Results indicate that the hybrid denoising scheme was able to reduce noise from EMG signals more effectively quantified by average SNR of 22.98 dB and PRD of 0.00029, across different datasets, in comparison with the currently employed noise reduction algorithms.

Authenticity and copyright verification of printed images

Perceptual image hashing and digital watermarking are two of the extensively investigated techniques for content authentication and copyright verification of digital images, respectively. One of the significant challenges of perceptual image hashing is a better tradeoff between robustness and discrimination. On the other hand, a major objective of digital watermarking techniques is to provide enhanced robustness and imperceptibility. In this paper, we present extensive experimental analysis, detailed laboratory forensic investigations, and application of the wave atom transform (WAT) based perceptual image hashing and digital watermarking techniques for printed images. Authenticity and copyright of printed images are verified via image hashing and digital watermarking techniques which are resilient against print-scan process distortions. To fulfill the requirement of a good balance between print-scan robustness and discrimination capabilities, we propose a compact, hybrid image hash using lower and middle frequency scale bands in the wave atom coefficients tiling. The hybrid image hash generation strategy is also advantageous due to its computational simplicity and reduced size of the hash value. Furthermore, we propose an enhanced multipurpose scheme which can support multiple applications, such as image authentication and copyright protection, simultaneously, for both digital-only and print-scanned images. Experimental analysis demonstrates the ability of wave atom based image hashing and watermarking schemes to provide authenticity and copyright verification for printed color images.

Gender Classification System Based on Wave ATOM Transform

Fingerprints are one of the most reliable biometrics used to identify people's characteristics. They have unique patterns and also inexpensive to collect and analyze. The proposed system deals with the classification of gender over fingerprint images based on Wave Atom Transform (WAT). In order to efficiently extract the salient features by for the classification of gender by fingerprint images and then provide the classification results using GMM classifier to evaluate the corresponding features. An efficient method for gender classification from fingerprint images based on WAT is gives the highest accuracy of about 90% at 3rd level of decomposition.

Wave atom transform based image hashing using distributed source coding

To reduce the size of hash code and enhance the security of wave atom transform (WAT) based image authentication system, a low-density parity-check code based distributed source coding (DSC) is employed to compress the hash code. With the help of a legitimately modified image, the compressed hash value could be correctly decoded while it will fail with the help of a maliciously attacked image. Therefore, the employed DSC provides a desired robustness to image authentication. Simulation results indicate that the proposed scheme provides a better performance with less hash code than existing WAT based image hash without using DSC. Moreover, the proposed scheme outperforms the random projection based approach in terms of authentication accuracy and data size.

Object Recognition Based on Wave Atom Transform

This study presents an efficient method for recognizing object in an image based on Wave Atom Trans-form (WAT). Object recognition is achieved by extracting the energies from all coefficients of WAT. The original image is decomposed by using the WAT. All coefficients are considered as features for the classification process. The extracted features are given as an input to the K-Nearest Neighbor (K-NN) classifier to recognize the object. The evaluation of the system is carried on using Columbia Object Image Library Dataset (COIL-100) database. The classification performance of the proposed system is evaluated by using classification rate in percentage, which is achieved by varying the angle between the views.

CLASSIFICATION OF MICROCALCIFICATION BASED ON WAVE ATOM TRANSFORM

Breast cancer is a serious problem for women in the world. It is the most common form of cancer diagnosed in woman, with one in nine women expected to be diagnosed with some form of cancer in their life-time. It is second only to lung cancer in cancer related deaths. Statistics shows that the case of breast cancer in the world is increasing every year. By analyzing a mammogram, pathologists could detect the presence of microcalcification in ones breast. However, microcalcification can be classified into benign and malignant. The later indicates the presence of cancer. Computer-Aided Diagnosis (CADx) designed to help phatologists determine the type of microcalcification in a mammogram. Usually, it's consisting of two steps, feature extraction and classification. In our methodology, we proposed the use of Wave Atom Transform (WAT) as feature extraction technique and Support Vector Machine (SVM) as classifier. Using this methodology, our experimental result achieved good classification accuracy. However, some of the previous researches have shown better results than ours.

Performance evaluation of the wave atom algorithm to classify mammographic images

The most common type of cancer seen in women is breast cancer. To enable recovery from this severe disease, monitoring and early detection must be provided, and related precautions must be taken as a first step. During diagnosis, some cases may be overlooked due to fatigue and eyestrain, because the determination of abnormalities is a repetitive procedure. In this study, a computer-aided diagnosis (CAD) system, using the wave atom transform (WAT) algorithm and support vector machine (SVM), is proposed to evaluate mammography images. During the process, the region of interest (ROI) is defined before applying the method. The system includes a feature extraction approach based on the WAT algorithm. In terms of classification, the process has 2 main stages: the classification of normal/abnormal regions and malignant/benign ones. The proposed system also uses principle component analysis (PCA) for further dimensional reduction and feature selection. A dataset from the Mammographic Image Analysis Society database is employed for testing and measuring the performance of the proposed system. The best success rates in this work are obtained using the coefficients at scales of 1, 2, and 3, by employing SVM with PCA. The maximum classification success rate to define the regions of interest as normal/abnormal is 100%. The success rate of malignant/benign classification is also achieved as 100% in the tests. According to the results, it is observed that these features ensure important support for more comprehensive clinical investigations and the results are very encouraging when mammograms are categorized via WAT, PCA, and SVM.

Quality Improvement on MRI Corrupted with Rician Noise Using Wave Atom Transform

Magnetic resonance imaging is a medical imaging technique that measures the response of atomic nuclei of body tissues to high frequency radio waves when placed in a strong magnetic field and that produces images of the internal organs. Denoising is always a challenging problem in magnetic resonance imaging and important for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. It is well known that the noise in magnetic resonance imaging has a Rician distribution. . In this paper, an improved de-noising technique is proposed on Magnetic Resonance Images highly corrupted with Rician Noise using wave atom shrinkage. General Terms Clinical diagnosis, tissue classification, segmentation.