Biomedical image analysis methods are gradually shifting towards computer-aided solutions from manual investigations to save time and improve the quality of the diagnosis. Deep learning-assisted biomedical image analysis is one of the major and active research areas. Several researchers are working in this domain because deep learning-assisted computer-aided diagnostic solutions are well known for their efficiency. In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented. This chapter can be helpful for the researchers to understand the recent developments and drawbacks of the present systems. The discussion is made from the perspective of the computer vision, pattern recognition, and artificial intelligence. This chapter can help to get future research directions to exploit the blessings of deep learning techniques for biomedical image analysis.

An Overview of Biomedical Image Analysis From the Deep Learning Perspective

Image segmentation has been an active topic of research for many years. Edges characterize boundaries, and therefore, detection of edges is a problem of fundamental importance in image processing. Edge detection in images significantly reduces the amount of data and filters out useless information while preserving the important structural properties in an image. Edges carry significant information about the image structure and shape, which is useful in various applications related with computer vision. In many applications, the edge detection is used as a pre-processing step. Edge detection is highly beneficial in automated cell counting, structural analysis of the image, automated object detection, shape analysis, optical character recognition, etc. Different filters are developed to find the gradients and detect edges. In this chapter, a new filter (kernel) is proposed, and the compass operator is applied on it to detect edges more efficiently. The results are compared with some of the previously proposed filters both qualitatively and quantitatively.

An Advanced Approach to Detect Edges of Digital Images for Image Segmentation

Security of the digital data is one of the major concerns of the today’s world. There are several methods for digital data security that can be found in the literature. Biological sequences have some features that make it worthy for the digital data security processes. In this work, DNA encryption and its different approaches are discussed to give a brief overview on the data security methods based on DNA encryption. This work can be highly beneficial for future research on DNA encryption and can be applied on different domains.

Data Security Techniques Based on DNA Encryption

Biomedical image analysis is an integral part of the modern healthcare industry and has a huge impact on the modern world. Automated computer-aided systems are highly beneficial for fast, accurate and efficient diagnosis of the biomedical images. Remote healthcare systems allow doctors and patients to perform their jobs from separate geographic locations. Moreover, expert opinion about a patient can be obtained from a doctor who is in a different country or in some distant location within stipulated amount of time. Remote healthcare systems require digital biomedical images to be transferred over the network. But several security threats are associated with the transmission of the biomedical images. Privacy of the patients must be preserved by keeping the images safe from any unauthorized access. Moreover, the contents of the biomedical images must be preserved efficiently so that no one can tamper it. Data tampering can produce drastic results in many cases. In this work, a method for biomedical image security has been proposed. DNA encryption method is one of the emerging methods in the field of cryptography. A secure and lossless encryption method is developed in this work. Various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.

Biomedical Image Security Using Matrix Manipulation and DNA Encryption

DeoxyriboNucleic Acid (DNA) encryption is a new encryption method that appeared along with the research of DNA nanotechnology in recent years. Due to the complexity of biology in DNA nanotechnology, DNA encryption brings in an additional difficulty in deciphering and, thus, can enhance information security. As a new approach in DNA nanotechnology, DNA strand displacement has particular advantages such as being enzyme free and self-assembly. However, the existing research on DNA-strand-displacement-based encryption has mostly stayed at a theoretical or simulation stage. To this end, this paper proposes a new DNA-strand-displacement-based encryption framework. This encryption framework involves three main strategies. The first strategy was a tri-phase conversion from plaintext to DNA sequences according to a Huffman-coding-based transformation rule, which enhances the concealment of the information. The second strategy was the development of DNA strand displacement molecular modules, which produce the initial key for information encryption. The third strategy was a cyclic-shift-based operation to extend the initial key long enough, and thus increase the deciphering difficulty. The results of simulation and biological experiments demonstrated the feasibility of our scheme for encryption. The approach was further validated in terms of the key sensitivity, key space, and statistic characteristic. Our encryption framework provides a potential way to realize DNA-strand-displacement-based encryption via biological experiments and promotes the research on DNA-strand-displacement-based encryption.

/pdf/an-operational-dna-strand-displacement-encryption-approach-rhtqme7x.pdf

An Operational DNA Strand Displacement Encryption Approach

Computer aided diagnostic is one of the most active research area and has huge impact on the health care industry. With the advent of intelligent methods, biomedical data processing becomes easier and less error prone. Moreover, remote health care is also possible using the IoT infrastructure. However, data security over the network is always considered as a challenge. Biomedical data are generally sensitive to external disturbances and small manipulation in the data may cause huge difference in the ultimate result. Wrong diagnosis can be life threatening in some scenarios or can be severe in almost every instance. Therefore, biomedical data security is one of the major challenge and necessary for remote health care. In this work, some application of cryptographic methods have been discussed which are used to protect biomedical images from unwanted modifications and access. This work can be highly beneficial in future research in the field of biomedical image protection as well as multimedia data security.

Reshmi Debnath

Papers

A Study on the Applications of the Biomedical Image Encryption Methods for Secured Computer Aided Diagnostics