Among all the approaches, Digital watermarking is the most widely implemented approach for copyright protection and authentication of data. In this technique, a unique piece of information is known as a watermark. Then the watermark gets into an image, later, to achieve its objective the watermark will be extracted. For the transmission of medical images, digital watermarking schemes are mostly used to ensure that the image has not gone through any unauthorized or illegal modifications during the transmission. Since conventional watermarking schemes alter the pixels in the original image, it is not suited for watermarking medical images. In medical images, permanent modifications may adversely affect the diagnosis process at the receiver side, caused by watermarking, especially when we are using some computer-aided diagnosis tools. This motivated computer scientists to work on reversible watermarking schemes. The reversible watermarking technology makes it possible to recover the required medical image from the watermarked image, while extracting the hidden watermark. So, the reversible watermarking technique does not affect the diagnosis in any way since the recovered image will be equivalent to the original image. This recovered image will be used by the user. The use of reversible watermarking techniques to send patient reports along with medical images is also explored, with the patient reports being embedded in the medical picture itself rather than the watermark. These techniques are commonly known as reversible data hiding techniques. This book chapter gives a brief overview of reversible data hiding techniques, reversible watermarking methods, and the major applications in medical image transmissions. In addition, the chapter addresses contemporary reversible data hiding and reversible watermarking algorithms intended specifically for medical picture transmission. The chapter also discusses some of the obstacles that must be overcome when developing a reversible watermarking system for healthcare applications.

Security in terms of copyright measurement for digital media distribution is the most challenging task. To maintain the digital right in 3D media, a watermarking scheme is proposed for Multi-view Video plus Depth (MVD) representation to sustain against the view synthesis and RST attack. The Singular Value Decomposition (SVD) is carried out on the left and the right video sequences to find view-invariant coefficients for watermark insertion. Motion compensated Discrete Cosine Transform (DCT) based Temporal Filtering (MCDCT-TF) is used in the temporal direction to make the scheme robust against video compression attack. The 2D Discrete Wavelet Transform (2D-DWT) is processed on the temporally filtered low-pass frames as a pre-processing to get to make the SVD coefficients more connected or say correlated in between the 3D view such that robustness can be achieved against RST and view synthesis with minimum visual degradation. A set of experiments is carried out with different 3D video sequences to justify the robustness of the proposed scheme over the RST attack.

Detection of objects is the most popular research topic nowadays. In this regard Convolution neural network gives a direction to achieve the goal. But detection of the Stationary objects on a live camera become more challenging due to the non-rigid movement of the object. Also, most of the time stationary objects appear to be focal loss in the time of detection. So using CNN for those cases will make the scheme fragile. In this paper, Image segmentation and Kalman filter are used to rectify the focal loss to make the scheme more accurate. Here RetinaNet is used for the implementation of a better object detection scheme. As a result, it is observed that the use of RetinaNet makes the stationary object detection more accurate and the results are acceptable compared to the state of the art model.

Cervical cancer remains a major public health challenge in low and middle-income countries including India. However, if detected early, it is preventable and curable. Objective: The present study aimed to ascertain the sociodemographic and clinical profile of cervical cancer patients visiting a tertiary cancer center. Methodology: A retrospective study was carried out at the Delhi State Cancer Institute, New Delhi. The study population included 136 women who were diagnosed with cervical cancer. A pretested data extraction sheet was used as the study tool for collecting information from the inpatient records. Descriptive analysis and chi-square test were performed and the level of significance was set at p<0.05. Results: A total of 136 cervical cancer patients with mean age of 46 ± 9.85 and mean BMI of 23.78 ± 5.03, were studied retrospectively. About 36.8% of patients were aged between 40-49 years and 57.4% were illiterate. While 40.4% of the patients belonged to FIGO stage II, 27.2% had FIGO stage III cancer. Majority (63.2%) of patients were diagnosed with squamous cell carcinoma (SCC), while the rest were adenocarcinoma (25%) and adenosquamous (11.8%). Clinical stage of cancer was found to be significantly associated with educational status (p=0.03) and dietary practices (p=.007). Conclusion: Our study found higher percentage of women with stage II and III cervical lesions and reaffirms the importance of education and healthy diet in early detection and prevention of cervical cancer. Therefore, it is suggested that accelerated population awareness and screening, incorporating digital innovations including vaccination programs are mandatory.