Steganography is used for secret or covert communication. A graph wavelet transform‐based steganography using graph signal processing (GSP) is presented, which results in better visual quality stego image as well as extracted secret image. In the proposed scheme, graph wavelet transforms of both the cover image and transformed secret image (using Arnold cat map) are taken followed by alpha blending operation. The GSP‐based inverse wavelet transform is performed on the resulting image, to get the stego image. Here, the use of GSP increases the inter‐pixel correlation that results in better visual quality stego and extracted secret image as shown in simulation results. Simulation results show that the proposed scheme is more robust than other existing steganography techniques.

Detection of the presence and absence of bone invasion by the tumor in oral squamous cell carcinoma (OSCC) patients is very significant for their treatment planning and surgical resection. For bone invasion detection, CT scan imaging is the preferred choice of radiologists because of its high sensitivity and specificity. In the present work, deep learning algorithm based model, BID-Net, has been proposed for the automation of bone invasion detection. BID-Net performs the binary classification of CT scan images as the images with bone invasion and images without bone invasion. The proposed BID-Net model has achieved an outstanding accuracy of 93.62. The model is also compared with six Transfer Learning models like VGG16, VGG19, ResNet-50, MobileNetV2, DenseNet-121, ResNet-101 and BID-Net outperformed over the other models. As there exists no previous studies on bone invasion detection using Deep Learning models, so the results of the proposed model have been validated from the experts of practitioner radiologists, S.M.S. hospital, Jaipur, India.

Facial Expression Recognition is a vital topic for research in current scenario which has many applications as machine based HR interviews and human-machine interaction. Facial Expression recognition is applied for identification of person using face of a person. Researchers have proposed many research techniques for facial expression recognition but still accuracy, illumination and occlusion are the research issues which have to improve. Key Research issue of facial expression is improving the accuracy of system which is measured in term of recognition rate. Feature extraction is the main stage on which accuracy depends for facial expression recognition. In this paper we have analyzed different feature extraction technique in frequency domain as Discrete Wavelet Transform, Discrete Cosine Transform feature extraction technique, Gabor filter and different feature reduction technique developed so far and future aspects.

Image compression is a process of reducing size of an images at the time of transmission and at network level we also need some security mechanism for secure data transmission assurance. To provide data security we need a best encryption method for an image so that it does not affect image quality. The image compression schemes are of two types lossy and lossless or combination of both. The both (lossy and lossless) compression schemes can compress and decompress the image but some time compression made some loss of data so it is lossy otherwise lossless. To provide security as we know perform encryption and decryption process, there are two methods for key generation one is symmetric and other asymmetric. If we use same key for encryption and decryption i.e., known as symmetric system otherwise asymmetric cryptography system.In this paper, a detailed review of different Compression and Encryption schemes like CE, EC, Joint compression and Encryption is given and we also analyzed their result on the basis of different parameter like compression ratio, PSNR, NPCR, UACI etc.

Purpose: The technique of fusing or integrating medical images collected from single and various modalities is known as medical image fusion. This is done to improve the quality of the images and combine information from several medical images. The whole procedure aids medical practitioners in gaining correct information from single images. Image fusion is one of the fastest-growing research topics in the medical imaging field. Sparse modeling is a popular signal representation technique used for image fusion with different dictionary learning approaches. We propose a medical image fusion with sparse representation (SR) and block total least-square (BLOTLESS) update dictionary learning. Approach: The domain of dictionary learning is the most significant research domain related to SR. An efficient dictionary increases the effectiveness of sparse modeling. Due to SR being an ongoing interesting research area, the medical image fusion process is done with a modified image fusion framework with recently developed BLOTLESS update dictionary learning. Results: The experimental results are compared for the image fusion process using other state-of-the-art dictionary learning algorithms, such as simultaneous codeword optimization, method of optimal directions, and K-singular value decomposition. The effectiveness of the algorithm is evaluated based on image fusion quantitative parameters. Results show that the BLOTLESS update dictionary algorithm is a promising modification for the sparse-based image fusion with its applicability in the fusion of images related to different diseases. Conclusions: The experiments and results show that the dictionary learning algorithm plays an important role in the sparse-based image fusion general framework. The fusion results also show that the proposed improved image fusion framework for medical images is promising compared with frameworks with other dictionary learning algorithms. As an application, it is also used as a tool for the fusion of different modularities of images related to brain tumor and glioma.