Efficient and Robust Medical Image Watermarking Based on Optimal Subband Tree Structuring and Discrete Fractional Fourier Transform Chacko, Anusha, Chacko, Shanty Computer Assisted Methods in Engineering and Science, 2023 In order to solve the security problems associated with medical information and improve the robustness of watermarking algorithms for medical images, a unique approach to watermarking based on block operations is presented. This study considers the medical images as the cover image, with the watermark logo considered secret information that needs to be protected over the wireless transmission in telemedicine. In the embedding phase, input with the discrete fractional Fourier transform is first applied to the input, and then level 2 wavelet decomposition is carried out to determine the optimal sub-band tree. For each tree node on level 2, the approximated and detailed coefficient is determined through the feature analysis perspective. The novelty of the adopted methodology is its simplified transformation and embedding process. Upon receiving a complex matrix, it separates the real part from imaginary part where block transformation is carried out for embedding the watermark pixels. In the extraction phase, just a reverse operation is performed. The watermarking evaluation is performed by simulating various image processing attacks on watermarked medical images. The simulation outcome demonstrates the effectiveness of that proposed watermarking scheme against various attacks. The proposed watermarking technique is robust under various attacks based on image statistics such as PSNR, BER, and the correlation coefficient.
Liver Tumor Classification Using Optimal Opposition-Based Grey Wolf Optimization Reshma Jose, Shanty Chacko, J. Jayakumar, T. Jarin International Journal of Pattern Recognition and Artificial Intelligence, 2022 Image processing plays a significant role in various fields like military, business, healthcare and science. Ultrasound (US), Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the various image tests used in the treatment of the cancer. Detecting the liver tumor by these tests is a complex process. Hence, in this research work, a novel approach utilizing a deep learning model is used. That is Deep Belief Network (DBN) with Opposition-Based Learning (OBL)-Grey Wolf Optimization (GWO) is used for the classification of liver cancer. This process undergoes five major processes. Initially, in pre-processing the color contrast is improved by Contrast Limited Adaptive Histogram Equalization (CLAHE) and the noise is removed by Wiener Filtering (WF). The liver is segmented by adaptive thresholding following pre-processing. Following that, the kernelizedFuzzy C Means (FCM) method is used to segment the tumor area. The form, color, and texture features are then extracted during the feature extraction process. Finally, these traits are categorized using DBN, and OBL-GWO is employed to enhance system performance. The entire evaluation is done on Liver Tumor Segmentation (LiTS) benchmark dataset. Finally, the performance of the proposed DBN-OBL-GWO is compared to other models and their achievements are proved. The proposed DBN-OBL-GWO achieves a better accuracy of 0.995, precision of 0.948 and false positive rate (FPR) of 0.116, respectively.
Deep learning-based robust medical image watermarking exploiting DCT and Harris hawks optimization Anusha Chacko, Shanty Chacko International Journal of Intelligent Systems, 2022 Image watermarking is an effective way to secure the ownership of digital photographs. This paper proposes a new methodology for integrating a watermark on the basis of various integrative strengths. The image is separated as 8 × 8 pixels blocks that do not overlap. The pixel size for each image block has been determined. For the embedding areas, picture blocks with the highest value have been chosen. Therefore, discrete cosine transformation (DCT) is transformed. The DCT coefficients are chosen in the midfrequency and the average selected DCT blocks are determined using a series of rules to produce various integration strengths. The watermarking bits were merged with the proposed deep learning convolution neural network (DLCNN) through a series of integration standards. The binary watermark has been scrambled by an Arnold transform until it is incorporated for additional stability. During the image carrier, a pattern recognition model depending on DLCNN is utilized to identify and extract the watermark and to recognize the watermark using the Harris hawks optimization (HHO) algorithm. The findings of the tests demonstrated that the system suggested is most imperceptible than the other current systems. The proposed method attains the efficiency watermarked picture with 46 dB peak signal‐to‐noise ratio value. This paper focuses on robust medical image watermarking exploiting DCT by using the HHO algorithm. The watermark lossless compression reduces watermark payload without data loss. In this research work, watermark is the consolidation of DCT and image watermarking secret key. The performance of robust medical image watermarking exploiting DCT with the HHO algorithm is compared with other conventional compression methods. HHO is found better and used to control watermarked image degradation in medical images watermarking. The proposed system also created a high resistance to remove watermarks during many attacks.
Identification of Power Leakage and Protection of Over Voltage in Residential Buildings Chitra S, Jayakumar J, Venkateshkumar P, Shanty Chacko, Sivabalan International Journal of Electrical and Electronics Research, 2022 In many residential buildings the electrical wires of individual houses are laid in the same conduit pipe and some mistakes could be made in identifying similar coloured wires when they are laid in same conduit pipe. Most of the faults are caused by the neutral interconnection in the wiring system. Usually neutral wires are connected to neutral bus within the panel board or switchboard, and are "bonded" to earth ground. In our secondary distribution, tree system of supply is mostly utilized. The voltage of each phase to neutral will be maintained at rated value even during the unbalanced load conditions. If neutral wire connection is poor the voltage at each phase will be different from one another, such an isolated neutral point is called floating neutral and the voltage of the point is always changing. This is the reason for over voltage causing damage to appliance’s which should be protected. In this paper, a smart system that identifies power leakage and provides over voltage protection to the residential building is proposed.
Liver cancer detection based on various sustainable segmentation techniques for CT images Reshma Jose, Shanty Chacko International Journal of Environmental Technology and Management, 2022 Liver cancer remains the most common cause of cancer death worldwide; in recent decades, the epidemiology has improved. Commonly, endoscopic stomach biopsy is performed for early detection of liver cancer to minimise mortality. Picture segmentation is a key technique for comprehension and intensification of the medical image. The purpose of this study was to create a sustainable computer-aided estimating system to determine the risk of liver cancer development, achieved through image processing on a CT image. Initially, the image is enhanced by using anisotropic diffusion filtering with unsharp masking (ADF-USM) technique, and the computer-aided estimating method was developed based on fuzzy C-means clustering, Otsu's, region-dependent active contour and superpixel segmentation dependent iterative clustering (SSBIC). This sustainable approach will allow for the effective selection of high-risk liver cancer populations. The performed sustainable CAD device acts as an assistant to the radiologists, helping to identify the area of cancer in the CT scaffold images, take biopsies from those areas and make a better diagnosis.
Regression Based Predictive Machine Learning Model for Pervasive Data Analysis in Power Systems Dr. K. Sasikala, Dr. J. Jayakumar, Dr. A. Senthil Kumar, Dr. Shanty Chacko, Dr. Hephzibah Jose Queen International Journal of Electrical and Electronics Research, 2022 The main aim of this paper is to highlight the benefits of Machine Learning in the power system applications. The regression-based machine learning model is used in this paper for predicting the power system analysis and Economic analysis results. In this paper, Predictive ML models for two modified IEEE 14-bus and IEEE-30 bus systems, integrated with renewable energy sources and reactive power compensative devices are proposed and developed with features that include an hour of the day, solar irradiation, wind velocity, dynamic grid price, and system load. An hour-wise input database for the model development is generated from monthly average data and hour-wise daily curves with normally distributed standard deviations. A very significant Validation technique (K Fold cross validation technique) is explained. Correlation between Input and output variable using spearman’s correlation analysis using Heat maps. Followed by the Multiple Linear Regression based Training and testing of the Modified IEEE 14 and IEEE30 Bus systems for base load case, 10% and 20% load increment with the 5-fold cross validation is also presented. Comparative analysis is performed to find the best fit ML Model for our research.
Sustainable method of automatic detection of tumor using super pixel segmentation Reshma Jose, Shanty Chacko, Jarin T. Aip Conference Proceedings, 2021 Liver cancer is the leading cause of cancer-related death worldwide. Since the radiologist's ability to diagnose liver cancer at an early stage is zero, the prognosis is poor. According to numerous investigations performed so far, the nodule segmentation algorithms are clearly ineffective. As a result, for specific pulmonary nodule segmentation, this study made use of the advanced optimization tool and centralized super pixels segmentation based iterative clustering (SSBIC). To remove noise from the images, start by using ADF and unsharp masking enhancement techniques. In order to predict abnormal liver tissue, an enhanced nodule image sequence is subjected to the Super pixel Segmentation Based Iterative Clustering (SSBIC) algorithm. Finally, to photograph liver nodules, a deep learning-based Advanced GWO with CNN (AGWO-ONN) and an Advanced GWO with ONN (AGWO-ONN) are used (AGWO-CNN).For nodule slice order, the average segmentation time is 1.06s. The classification accuracy of the Advanced GWO with ONN (AGWO-ONN) method is 97 percent, while the classification accuracy of the Advanced GWO with CNN (AGWO-CNN) method is 97.6 percent.
Fpga‐based implementation of floating point processing element for the design of efficient fir filters Tintu Mary John, Shanty Chacko Iet Computers and Digital Techniques, 2021 Numerous applications based on very large scale intergration (VLSI) architecture suffer from large size components that lead to an error in the design of the filter during the stages of floating point arithmetic. Hence, it is necessary to change the architectural model that increases the design complexity and the time delayeffect. The issue encountered in the VLSI architectures for finiteimpulse response (FIR) filteris the increased number of components, especially delay elements. For the VLSI architecture reconfigured with reduced register usage, this article provides the floating point processing element (FPPE) implementation with Cross ‐ Folded Shifting. The proposed FIR filter system reduces the number of components in the circuit which increases the complexity and high delay rate in the logical operation. The system has a comparatively reduced delay rate and power consumption. Hence, an efficient fast architecture based on the FPPE method is developed in this paper.
