Watermarking the Digital Image Using Hybrid Approach | Chapter 7 | Research Developments in Science and Technology Vol. 9

Digital watermarking capabilities can now be improved in various ways, including resilience, imperceptibility, authentication, tamper-proofing, etc. This essay will concentrate on a novel method for optimally robustly incorporating a transparent digital watermark onto an original image (cover content). Depending on the methods used for error control, determine how to extract the high-quality digital watermark from the flagged image. How to protect the watermark from the impacts that the watermarked image's processing produces. How to identify and rebuild the watermarked document's tamper region image based on the cutting-edge watermarking method. We presented a hybrid strategy (which combines the DWT, SVD, and NN) to create a new intelligent model for digital watermarking that will be utilised to safeguard the copyright of the digital content and to find and recover the tampered/forged regions in the watermarked image. The experimental findings demonstrate the usefulness of the suggested system and its superior attack robustness.

Author (s) Details

B. Manjula
Department of Computer Science, Kakatiya University, Warangal, Telangana, India

R. Lakshman Naik
Department of IT, KUCE&T, Kakatiya University, Warangal, Telangana, India.

V. Bapuji
Department of MCA, Vaageswari College of Engineering. Karimnagar, Telangana, India.

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Analysis of Different Filtering Methods for Preprocessing and GLCM Feature Extraction Using Wavelet in Mammogram Images | Chapter 15 | Innovations in Medicine and Medical Research Vol. 3

Breast cancer is a stand-out surrounded by the most widely perceived diseases and has a high rate of mortality around the world, significantly risking the health of the females. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. To assist radiologists, a computer-aided diagnosis (CAD) is enhancing and important medical systems for mammographic lesion analysis. In mammogram images, micro-calcifications are one of the imperative signs for breast cancer detection. Mammographic medical scan may present unwanted noise and CAD systems are very sensitive to noise. Early stage detection for any medical image analysis application like brain tumor detection, breast cancer detection is considered as an important step. Micro calcification is small calcium deposits in the breast region and mammogram images are of low contrast. Thus, in this work, different types of filtering techniques used for noise reduction and image enhancement for medical image processing are analyzed on mini-MIAS mammogram image databases. Anisotropic diffusion with wavelet filtering method shows best results for enhancement and noise removal of the image. This filtered image is segmented; region of interest (ROI) is extracted through global Thresholding technique with discrete wavelet transform (DWT). Gray level cooccurrence matrix is used to extract the important features. Here, seven features are extracted for different categories of micro calcified images like normal, benign, malignant. Results show that from extracted features the values of malignant and micro calcified images are same whereas normal and benign are same. This proposed methodology can help to categorize different classes of images. 

Author(s) Details

Neha N. Ganvir
Department of Electronics Engineering, Sinhgad Institute of Technology and Science (SITS), Narhe, Sinhgad Technical Education Society (STES), Pune, India.  

Dr. D. M. Yadav
Pune University, India.

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