3D Wavelet Transform Research Articles

Non-contact surface roughness evaluation of milled Al and Cu specimens by 1D and 2D wavelet transformation using histogram based linear regression model

Non-contact surface roughness evaluation of milled Al and Cu specimens by 1D and 2D wavelet transformation using histogram based linear regression model

Abstract PO3-07-01: Multiscale Computational Analysis of Patient-Matched Multi-Modal Imaging of Breast Tissue Microenvironment

Abstract Introduction: Mammographic breast density is a strong quantitative cancer risk factor, but its link to underlying tissue and cellular level changes are weakly understood. Understanding the complex restructuring changes in the breast tumor microenvironment and its impact on breast density is integral to improving risk assessment and treatment strategies. A novel approach was developed to stratify mammographic dense breast tissue into areas of active dense tissue and passive dense tissue. Active dense tissue is structurally reorganizing and links to cancer dynamics, whereas passive dense mammographic tissue remains organized.1,2 A complementary wavelet-based analysis technique to measure the multiscale anisotropy of collagen fibers from Second-Harmonic Generation (SHG) imaging demonstrated clinical cancer diagnostic potential in mouse carcinoma and human pancreatic cancer.3,4 This tool is now leveraged for a multi-modal study in which patient-matched H&E tissue biopsies are imaged using both bright-field microscopy and SHG imaging to compare with the patient’s corresponding mammograms. Stratifying the mammographic density into active and passive dense areas, a correlation between biopsy tissue anisotropy and mammographic density subtypes can be calculated to help understand how tumor microenvironment changes affect a patient’s mammographic breast tissue composition. Methods: SHG and brightfield-imaged biopsy slides from 10 patients (five malignant, five benign) collected from Maine Medical Center (Maine, US) were analyzed using the 2D Wavelet Transform Modulus Maxima (WTMM) Anisotropy Method generating a large-scale and a small-scale anisotropy factor as well as a scale-combined anisotropy calculated from their large- and small-scale anisotropy separation. A multi-modal anisotropy score was calculated by adding the imaged area’s H&E combined anisotropy score with its respective SHG combined anisotropy score. This analysis was done on eight randomly selected ductal areas for each imaging modality on a slide and correlated with the measured areas of density subtypes from the whole mammographic view. Linear regression was used for determining correlation between H&E and SHG anisotropy factors as well as with active and passive mammographic density subtype measurements. Results: The malignant patients’ small-scale H&E anisotropy factor positively correlated with their small scale SHG anisotropy factor (R2=0.571). No correlation was found for the benign patients (R2=0.011). The strongest correlations were found between benign patients’ mammographic passive (positive correlation) and active (negative correlation) dense tissues vs the H&E anisotropy scale-combined factor (R2=0.858 and R2=0.961, respectively). The largest malignant patient correlation was a negative relationship between SHG anisotropy scale-combined factor and overall mammographic density (inclusive of both active and passive dense tissue) (R2=0.891). The multi-modal anisotropy score correlated with overall mammographic density for both benign (positive correlation, R2=0.698) and malignant patients (negative correlation, R2=0.773). Combining the multi-modal anisotropy factor from SHG and H&E with the active dense tissue measurement using K-Means clustering, 5/5 malignant and 4/5 benign patients were correctly classified. Conclusion: Density in mammograms correlates with underlying quantitative changes in a patient’s tissue and collagen anisotropy. Combining both H&E and SHG analysis on tissue biopsy with active and passive density metrics from computational mammography leads to the best predictive potential than any one method on their own.

Contactless Biometric Based on Palm Vein Recognition Using Wavelet and Local Line Binary Patterns

To support the roadmap for coexistence with Covid-19, contactless biometrics is needed as an individual identity verification technology in daily activities such as control systems, recording attendance at offices/schools/agencies and access rights to a room. An example of contactless biometrics is palm vein-based biometrics. Because it is contactless, this biometric system does not require direct contact between the user and the sensor device, providing several advantages in terms of comfort during acquisition and is more hygienic. In the palm vein recognition system, the palm vein pattern can be considered as a texture feature. Therefore, this study proposes a contactless biometric system based on palm vein recognition using the Local Line Binary Pattern method to extract texture features of palm vein images resulting from the decomposition of the 2D Wavelet Transformation, so as to produce a small texture descriptor that is compatible with the texture characteristics of thin veins. The proposed texture feature extraction method has been tested using the fuzzy k-NN classification method on 600 palm images with a CRR accuracy of 95.0% with a computation time of 0.057 seconds.

Prediction of New-Onset Diabetes after Pancreatectomy with Subspace Clustering based Multi-view Feature Selection.

The pancreas plays an important role in glucose metabolism, and developing diabetes or long-term glucose metabolism disturbance may be a prevalent sequela after pancreatectomy. Nevertheless, relative factors of new-onset diabetes after pancreatectomy stay unclear. Radiomics analysis is potential to identify image markers for disease prediction or prognosis. Meanwhile, combination of imaging and electronic medical record (EMR) showed superior performance than imaging or EMR alone in previous studies. One critical step is to identity predictors from high-dimensional features, and it is even more challenging to select and fuse imaging and EMR features. In this work, we develop a radiomics pipeline to assess postoperative new-onset diabetes risk of patients undergoing distal pancreatectomy. Specifically, we extract multiscale image features with 3D wavelet transformation, and include patients' characteristics, body composition and pancreas volume information as clinical features. Then, we propose a multi-view subspace clustering guided feature selection method (MSCUFS) for the selection and fusion of image and clinical features. Finally, a prediction model is constructed with classical machine learning classifier. Experimental results on an established distal pancreatectomy cohort showed that the SVM model with combined imaging and EMR features demonstrated good discrimination, with an AUC value of 0.824, which improved the model with image features alone by 0.037 AUC. Compared with state-of-the-art feature selection methods, the proposed MSCUFS has superior performance in fusing image and clinical features.

Visual Three-Dimensional Reconstruction Based on Spatiotemporal Analysis Method

To accurately reconstruct the three-dimensional (3D) surface of dynamic objects, we proposed a wrapped phase extraction method for spatiotemporal analysis based on 3D wavelet transform (WT). Our proposed method uses a 2D spatial fringe image combined with the time dimension and forms a 3D image sequence. The encoded fringe image sequence’s wrapped phase information was extracted by 3D WT and complex Morlet wavelet, and we improved the wrapped phase extraction’s accuracy by using the characteristics of spatiotemporal analysis and a multi-scale analysis of 3D WT, then we reconstructed the measured object by wrapped phase unwrapping and phase height transformation. Our simulation experiment results show that our proposed method can further filter the noise in the time dimension, and its accuracy is better than that of the one- (1D) and two-dimensional (2D) WT wrapped phase extraction method and the 3D Fourier transform wrapped phase extraction method because the reconstructed spherical crown’s RMSE value does not exceed 0.25 and the PVE value is less than 0.95. Our results show that the proposed method can be applied to the dynamic 3D reconstruction of a real human thoracic and abdominal surface, which fluctuates slowly with respiration movement, further verifying its effectiveness.

Moving groups across Galactocentric radius with Gaia DR3

ABSTRACT The kinematic plane of stars near the Sun has proven an indispensable tool for untangling the complexities of the structure of our Milky Way (MW). With ever improving data, numerous kinematic ‘moving groups’ of stars have been better characterized, and new ones continue to be discovered. Here we present an improved method for detecting these groups using MGwave, a new open-source 2D wavelet transformation code that we have developed. Our code implements similar techniques to previous wavelet software; however, we include a more robust significance methodology and also allow for the investigation of underdensities, which can eventually provide further information about the MW’s non-axisymmetric features. Applying MGwave to the latest data release from Gaia (DR3), we detect 47 groups of stars with coherent velocities. We reproduce the majority of the previously detected moving groups in addition to identifying three additional significant candidates: one within Arcturus, and two in regions without much substructure at low VR. Finally, we have followed these associations of stars beyond the Solar neighbourhood from Galactocentric radius of 6.5–10 kpc. Most detected groups are extended throughout radius indicating that they are streams of stars possibly due to non-axisymmetric features of the MW.

A Novel Computer-Vision Approach Assisted by 2D-Wavelet Transform and Locality Sensitive Discriminant Analysis for Concrete Crack Detection.

This study proposes FastCrackNet, a computationally efficient crack-detection approach. Instead of a computationally costly convolutional neural network (CNN), this technique uses an effective, fully connected network, which is coupled with a 2D-wavelet image transform for analyzing and a locality sensitive discriminant analysis (LSDA) for reducing the number of features. The algorithm described here is used to detect tiny concrete cracks in two noisy adverse conditions and image shadows. By combining wavelet-based feature extraction, feature reduction, and a rapid classifier based on deep learning, this technique surpasses other image classifiers in terms of speed, performance, and resilience. In order to evaluate the accuracy and speed of FastCrackNet, two prominent pre-trained CNN architectures, namely GoogleNet and Xception, are employed. Findings reveal that FastCrackNet has better speed and accuracy than the other models. This study establishes performance and computational thresholds for classifying photos in difficult conditions. In terms of classification efficiency, FastCrackNet outperformed GoogleNet and the Xception model by more than 60 and 80 times, respectively. Furthermore, FastCrackNet's dependability was proved by its robustness and stability in the presence of uncertainties produced by network characteristics and input images, such as input image size, batch size, and input image dimensions.

The Application of Two-Dimensional Continuous Wavelet Transform Based on Active Infrared Thermography for Subsurface Defect Detection in Concrete Structures

The early condition-based assessment of civil infrastructures plays an essential role in extending their service life, preventing undesirable sudden failures, and reducing maintenance and rehabilitation costs. One of the most commonly used and fastest nondestructive testing (NDT) techniques is infrared thermography (IRT), which has emerged as a powerful method for assessing general concrete quality and detecting subsurface damage in structural members. Nevertheless, the accurate detection and classification of localized defects is still a challenging task to achieve. The contribution made by enhancing defect detection using two-dimensional (2D) wavelet transformation (WT) as a post-processing method, however, has received little attention within the field of active IR thermography. In this study, we explored the use of continuous wavelet transform (CWT) to visualize how the wavelet function at different frequencies could enhance the damage features of thermal images. A concrete slab under an applied heat flux was tested experimentally by an IR camera with well-controlled excitation sources. The qualitative visualization of thermograms was translated into quantitative results by extracting, processing, and post-processing the values assigned to the pixels in the thermal images. With the assumption of there being no oriented damage features, an isotropic (non-directional) Mexican hat wavelet was utilized as the mother wavelet. The experimental results showed that the 2D-CWT method achieved strong detection performance in extracting discriminatory features (defective areas) from the acquired thermal images. Compared with raw thermograms, the resultant CWT-transformed images were less affected by the non-uniform heating effect, and the boundaries of the defects contrasted more strongly. The 2D-CWT method demonstrates good sensitivity when an appropriate wavelet type and scale factor are chosen. Due to the desire to detect localized defects, adjusting the scale factor of the wavelet is important to improve the efficiency of detection as lower scale factors provide the finer details of thermal images, whereas higher scale factors provide the general outline of internal defects. The findings of this study represent a further step toward improving thermographic data for more precise defect-detection imaging, and principally for large concrete structures, that can be verified easily using other NDT surveys.

Multiscale anisotropy analysis of second-harmonic generation collagen imaging of human pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers with a minority (< 10%) of patients surviving five years past diagnosis. This could be improved with the development of new imaging modalities for early differentiation of benign and cancerous fibrosis. This study intends to explore the application of a two-photon microscopy technique known as second harmonic generation to PDAC using the 2D Wavelet Transform Modulus Maxima (WTMM) Anisotropy method to quantify collagen organization in fibrotic pancreatic tissue. Forty slides from PDAC patients were obtained and eight images were captured per each tissue category on each slide. Brownian surface motion and white noise images were generated for calibration and testing of a new variable binning approach to the 2D WTMM Anisotropy method. The variable binning method had greater resistance to wavelet scaling effects and white noise images were found to have the lowest anisotropy factor. Cancer and fibrosis had greater anisotropy factors (Fa) at small wavelet scales than normal and normal adjacent tissue. At a larger scale of 21 μm this relationship changed with normal tissue having a higher Fa than all other tissue groups. White noise is the best representative image for isotropy and the 2D WTMM anisotropy method is sensitive to changes induced in collagen by PDAC.

Elimination of Image Saturation Effects on Multifractal Statistics Using the 2D WTMM Method.

Imaging artifacts such as image saturation can restrict the computational analysis of medical images. Multifractal analyses are typically restricted to self-affine, everywhere singular, surfaces. Image saturation regions in these rough surfaces rob them of these core properties, and their exclusion decreases the statistical power of clinical analyses. By adapting the powerful 2D Wavelet Transform Modulus Maxima (WTMM) multifractal method, we developed a strategy where the image can be partitioned according to its localized response to saturated regions. By eliminating the contribution from those saturated regions to the partition function calculations, we show that the estimation of the multifractal statistics can be correctly calculated even with image saturation levels up to 20% (where 20% is the number of saturated pixels over the total number of pixels in the image).

Quantitative mapping of depth profile of fatigue cracks using eddy current pulsed thermography assisted by PCA and 2D wavelet transformation

Fatigue cracks are widely generated in numerous engineering components under alternating loads, whose sudden brittle fracture would seriously shorten the service life and cause significant hazards. A large number of nondestructive testing (NDT) techniques have been utilized to quantitatively evaluate the fatigue cracks, however, most of these methods are difficult to be used for a full characterization of the cracks’ depth profile. The eddy current pulsed thermography (ECPT) as a promising NDT technique shows great potential for fatigue crack evaluation due to its high detection efficiency, large inspection area, and high spatial resolution. In this article, a ferrite yoke was utilized to generate a homogeneous eddy current field, and as such to enhance the detection sensitivity and to provide a wide open-view area for the camera. The hybrid processing strategy combining principal component analysis (PCA) and 2D wavelet transformation was proposed to extract spatial features of the thermographic image sequences. Firstly, numerical results demonstrate that the profiles of slot cracks can be identified directly from the thermographic images in the early heating stage. Afterward, the ECPT experiments on slot cracks with different depth profiles demonstrate the feasibility of the hybrid processing strategy. Then, three fatigue cracks were manufactured through the three-point bending method and detected by ECPT. The results of the hybrid processing method indicate that these three fatigue cracks have elliptical profiles. Finally, the authentic depth profile of a fatigue crack with an elliptical shape is extracted through a destructive experiment. The comparison results between the real depth profile and the profile characterized through the proposed strategy show great consistency and validate the superiority of the proposed strategy.

New Approach to Partial Cryptography Using 2D Wavelet Transform

Computer security has to re-invent itself. New technologies and new applications bring new threats. Especially with the increasing growth of data a communication cryptography and data security are an essential requirements for communication privacy. This paper present a new approach that combines between two technical partial cryptography and 2D wavelet transform. The process consists of two stages: First stage is to compress the image to reach LL2 and LL3. Second stage is to pass the resulting image after compression process to encryption process that uses image that has strong randomness used as a key. The result of this approach is good and helps in solving the problem of keeping image privacy.Key word: Image Compression, Wavelet, Random Key Generated, Partial Cryptography.

2D Wavelet Decomposition and F-K Migration for Identifying Fractured Rock Areas Using Ground Penetrating Radar

The quality of the surrounding rock is crucial to the stability of underground caverns, thereby requiring an effective monitoring technology. Ground-penetrating radar (GPR) can reconstruct the subterranean profile by electromagnetic waves, but two significant issues, called clutter and hyperbola tails, affect the signal quality. We propose an approach to identify fractured rocks using 2D Wavelet transform (WT) and F-K migration. F-K migration can handle the hyperbola using Fourier analysis. WT can mitigate clutter, distinguish signal discontinuity, and provide signals with a good time-frequency resolution for F-K migration. In the simulation, the migration result from horizontal detail coefficients highlight the crack locations and reduce the scattering signals. Noise has been separated by 2D WT. Hyperbola tails are decomposed to vertical and diagonal detail coefficients. Similar promising results have been achieved in the field measurement. Therefore, the proposed approach can process GPR signals for identifying fractured rock areas.

A New Blind Watermark Embedding Model: Spiral Updated Rider Optimization Algorithm

Abstract In this era, the exchanging of multimedia content has become much comfortable. Since the distribution and the illegal copying of digital multimedia content have become much easier, an author’s intellectual property copyrights have suffered from the violations that led to many damage to their benefits in many applications. This paper intends to propose a novel digital watermarking model for multimedia copyright protection that is based on 3D wavelet transformation. Initially, histogram-based motion frame extraction is conducted on the color video for which keyframes are extracted based on the extracted motion frames. To embed the watermark in the keyframes, 3D wavelet coefficients are selected followed by applying the spread spectrum technique. To conduct precise and effective embedding, it is planned to introduce fine-tuning aspects in the embedding process. As the intensity factor plays a major role in the embedding process, this paper tunes it rather than selecting randomly. Moreover, the Mersenne-Twister algorithm is known for spreading the power spectrum of watermark data to keep invisible. Hence, it is also intended to tune the seed factor of the respective algorithm to further effectuate the spreading process. These tuning processes can be solved as an optimization problem for which this paper proposes a new hybrid optimization algorithm named Spiral Updated Rider Optimization Algorithm (SU-ROA). SU-ROA is a hybrid variant of the Rider Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). The performance of the proposed work is compared and proved over the state-of-the-art models in terms of both embedding and extracting effectiveness.

Radiomics Analysis and Unsupervised Self-Organizing-Map Technique to Predict Radiation-Induced Pneumonitis in Patients with Lung Cancer

Lung radiation pneumonitis (RP) is one of the major toxicities experienced by lung cancer patients (13%–37%) receiving thoracic radiation therapy (RT) (Kocak Z, et al. Int. J. Radiat. Oncol. Biol. Phys. 2005 and Rodrigues G, et al. Oncol. 2004). Investigation of imaging biomarkers that can predict the incidence of RP can be useful toward reduce the probability of RP development. In this pilot study, we investigated the application of Kohonen Self-Organizing Map (K-SOM; a type of adaptive model that is trained using unsupervised competitive-learning (Kohonen T, Springer 1995)), for analysis of radiomic features extracted from normal lung tissue of non-RP and RP patients. The goal was to build an adaptive model to stratify patients and to reveal dominant characteristics of radiomics features for patient stratification. These characteristics are likely to remain unnoticed using conventional statistical analysis. To perform deep and unsupervised analysis of radiomics features extracted from planning CT images of normal lung tissue for patients with locally advanced, non-small cell lung cancers (NSCLC) to characterize and stratify patients with and without radiation-induced RP. Planning CT images of 41 patients (14 with RP and 27 with no evidence of RP) with stage-III lung cancers, treated with IMRT/3D-CRT, were studied. One hundred sixty eight radiomics features were extracted from the volume of normal lung tissue receiving ≥20 Gy, excluding the ITV, according to the following 8 different classes: Intensity Histogram Based Features (IHBF), Gray Level Run Length (GLRL), Law’s Textural information (LAWS), Discrete Orthonormal Stockwell Transform (DOST), Local Binary Pattern (LBP), 2D-Wavelet Transform (2DWT), 2D-Gabor Filter (2DGF), and Gray Level Co-Occurrence Matrix (GLCM). A K-SOM ([9×9]-neurons) was constructed using 168 radiomic features and was trained and evaluated using random-permutation-sampling method (100-iterations, 67% and 33% for modeling and testing respectively). Regions pertaining to the dominant characteristics of the discriminant features were identified in the K-SOM space and used to calculate stratification power of the classifier. The K-SOM revealed 3 dominant regions associated with the RP status: high-certainty-non-RP, high-certainty-RP, and intermediate/uncertain region. The average of AUC, positive predictive and negative predictive values were 76.20%, %71.65, and 82.12% respectively. Correlation-based dissimilarity analysis (r<0.2, p-value<0.05) ranked and revealed the stability and robustness of the 6 discriminant features as follows: Skewness, Entropy, and Moment (3 and 4) from IHBF, Entropy-CA1 from DOST, and Short-Run-Emphasis from GLRL. The results of this pilot study, albeit subject to confirmation in a larger patient population, suggest a potential role for the use of an unsupervised method for radiomics-based stratification and prediction of radiation-induced pneumonitis in patients with locally advanced NSCLC.

Mixed-transform based codec for 2D compression of ECG signals

A method for ECG compression, by imaging the record as a 2D array and implementing a transform lossy compression strategy, is advanced. The particularity of the proposed transformation consists in applying a Discrete Wavelet Transform along one of the dimensions and the Discrete Cosine Transform along the other dimension. This renders a transformed array which is characterized by being sparse. Using the MIT-BIH Arrhythmia database, it is demonstrated that the proposed Mixed Transform achieves, in mean value, an improvement in sparsity of up to 3 times with respect to the 2D Wavelet Transform. This feature is beneficial for compression. The resulting codec produces a mean value compression ratio of 39 for a quality corresponding to PRD = 0.6. This PRD is attained for every record in the database without previous filtering.

Public database for validation of follicle detection algorithms on 3D ultrasound images of ovaries

Background and objective: Automated follicle detection in ovarian ultrasound volumes remains a challenging task. An objective comparison of different follicle-detection approaches is only possible when all are tested on the same data. This paper describes the development and structure of the first publicly accessible USOVA3D database of annotated ultrasound volumes with ovarian follicles. Methods: The ovary and all follicles were annotated in each volume by two medical experts. The USOVA3D database is supplemented by a general verification protocol for unbiased assessment of detection algorithms that can be compared and ranked by scoring according to this protocol. This paper also introduces two baseline automated follicle-detection algorithms, the first based on Directional 3D Wavelet Transform (3D DWT) and the second based on Convolutional Neural Networks (CNN). Results: The USOVA3D testing data set was used to verify the variability and reliability of follicle annotations. The intra-rater overall score yielded around 83 (out of a maximum of 100), while both baseline algorithms pointed out just a slightly lower performance, with the 3D DWT-based algorithm being better, with an overall score around 78. Conclusions: On the other hand, the development of the CNN-based algorithm demonstrated that the USOVA3D database contains sufficient data for successful training without overfitting. The inter-rater reliability analysis and the obtained statistical metrics of effectiveness for both baseline algorithms confirmed that the USOVA3D database is a reliable source for developing new automated detection methods.

Abstract P6-06-04: Exploratory computational longitudinal analysis of mammographic microenvironment disruption preceding breast tumorigenesis

Abstract Despite an aggressive emphasis on screening mammography, there has been no change in the number of initial metastatic breast cancer diagnosis since 1975. Even more alarming is the lack of effective screening to differentiate dormant cancerous pathology that may spontaneously ignite into an aggressive metastatic cancer. In this context, screening mammography is ineffective and biased towards a reactive diagnosis, when cancer is already present. We seek to develop a predictive algorithm that is sensitive to currently undetectable alterations of the microenvironment that precede tumorigenesis. Histological features such as tissue disruption and loss of tissue homeostasis, which are critical early indicators of breast cancer, unfortunately fall well outside the detection abilities of a trained radiologist or current computer-aided detection/diagnostic methods. In a recent publication using a powerful wavelet-based multifractal image analysis method, we extracted quantitative metrics for tissue disruption from standard mammography, and inferred a correlation with loss of tissue homeostasis. The 2D Wavelet-Transform Modulus Maxima (WTMM) method was used to quantify density fluctuations from mammographic breast tissue via a statistical roughness exponent called the Hurst exponent (H). The two mammographic tissue radiological states, i.e., fatty and dense, are quantitatively characterized with this Hurst exponent: H&amp;gt;1/2 (correlated spatial density fluctuations) for dense tissue; and H&amp;lt;1/2 (anti-correlated spatial density fluctuations) for fatty tissue. However, tissue regions that are neither dense nor fatty, with H~1/2 (uncorrelated spatial density fluctuations), were found preferably in patients with tumorous breasts vs. normal breasts (p-value = 0.0006) [Marin et al. Medical Physics, 2017]. The underlying physical processes associated with a H=1/2 signature are those of randomness and disorganization. Indeed, incoherent angular motion and the consequential adoption of randomized cellular motility are associated with malignant growth in breast tissue. We hypothesize that this H~1/2 signature accompanies or even preceeds tumorigenesis. In an exploratory analysis aimed at verifying this hypothesis, we studied, for 22 breast cancer patients, the progression of healthy breast tissue microenvironment towards a disrupted state by performing a computational analysis of longitudinal sequences of mammograms multiple years prior to, and leading to diagnostic. Preliminary results indicate a measurable change in mammographic density fluctuations (and therefore, potentially loss of tissue homeostasis), between 1 and 3 years prior to radiologic detection / diagnostic. An eventual larger-scale clinical validation of such an analysis could lead to the routine implementation of predictive algorithms based on the multifractal characterization of tissue disruption that precedes tumorigenesis. Such predictive diagnostics empowers both patient and clinician to collaboratively assess all ramification of the disease and select optimal therapeutics. Citation Format: Andre Khalil, Brian Toner. Exploratory computational longitudinal analysis of mammographic microenvironment disruption preceding breast tumorigenesis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P6-06-04.

Face Recognition Using Fuzzy Minimal Structure Oscillation in the Wavelet Domain

This paper proposes a novel technique using Fuzzy Minimal Structure Oscillation in the Wavelet domain. This pathway consists of three parts: convert color face images into red, green and blue (RGB) face images, then apply 2D wavelet transformation (Haar) for dimension reduction, by removing abundances and preserving the original features of the image and lastly apply Fuzzy Minimal Oscillation to distinguish unknown face images from a set of known images. In this paper introduced a new algorithm for face recognition formed and tested using MATLAB 7.9 software.

Accurate and reliable reversible data hiding using sequential encoding techniques

SummaryReversible data hiding facilitates images to be authenticated and by eliminating the digital watermark. Histogram Shifting Based Reversible Data Hiding (HSRDH) is presented in the previous research, which aims to decrease the performance with low quality when recreating the images. However, embedding patches could be hacked where it does not concentrate on the security threats. This issue is resolved using Sequential Encoding Based Reversible Data Hiding (SEB‐RDH) method. Here, initially input image is transformed into Haar wavelet using discrete wavelet transform. This wavelet is decomposed by utilizing 1D wavelet transformation; Haar wavelet is processed, and after that, by means of utilizing 2D wavelet transformation, the result of 1D wavelet transformation is decomposed once more. Horizontal coefficients are separated and embedded with the normalized input image. This type of data hiding enlarged the compression size and acts as a guard from the hackers who could not hack the protected data contents. This technique guarantees the enlarged reconstruction image accurateness in the receiver side. It is implemented in Matlab simulation environment, and it is confirmed that the SEB‐RDH displays 15.8% improved PSNR rate after that the previous technique HSRDH and 26.5% improved MSE rate compared with the previous HSRDH technique.