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

— In this study, a new method has been eliciting for encoding 2D and 3D color images. The DNA strand construction was used as the basis for structuring the method. This method consisted of two main stages, the encryption and decryption stages. As each stage includes several operations to reach the desired goal. In the coding stage, a special table was prepared to show the mechanism of work. It starts with encoding the DNA bases into two binary orders, then two zeros are added to the string to finally consist of four binary bits whose size is parallel to the representation of a set of hexadecimal numbers represented in binary, where the XOR operation is then done between the two values to be the result is completely different from the original code. Then the binary values we obtained are converted to decimal values that are placed in an array with the same size as the image to be encoded. Finally, this last array was processed with the exponential function factor, so the final result is a 100% encoded image. In the decoding stage, another algorithm was built that reflects the work of what preceded it in the encryption stage, where the result was an exact copy of the original image. It is worth noting that standard images of different sizes were used as testing images. The performance evaluation of the method was calculated based on several factors: MSE, peak PSNR, and the time required to perform the encoding and decoding process. The method achieved good results when compared with the results of other methods in terms of quality and time.

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Encryption of Color Image Based on DNA Strand and Exponential Factor

Image segmentation is one of the inevitable parts of the digital image processing and very useful to solve different real life problems. Biomedical image segmentation is a prime domain of application of digital image processing. and automated computer aided diagnostics process has high dependency on it. Automated identification of different regions of an image are often required by the human experts. Moreover, accurate detection and identification of a region of interest is possible using the automated methods. Errors are common for the human experts and can be reduced and faster results can be achieved with the help of automated and intelligent systems. This work proposes a biomedical image segmentation process using Fractional Order Darwinian Particle Swarm Optimization (FODPSO) and thresholding. The efficiency of the proposed method is tested both visually and quantitatively and the results speaks itself about the efficiency of the proposed work.

A Biomedical Image Segmentation Approach Using Fractional Order Darwinian Particle Swarm Optimization and Thresholding

A novel image encryption framework is proposed in this article A new chaotic map and a pseudorandom bit generator are proposed Apart from this, a novel image encryption system is designed based on the proposed map and the proposed pseudorandom bit generator These three are the major contributions of this work that makes a complete cryptosystem The proposed new chaotic map is proposed which will be known as the ‘RCM map’ and its chaotic property is studied based on Devaney’s theory The proposed pseudorandom bit generator is tested using the NIST test suite The proposed method is simple to implement and does not involve any highly complex operations Moreover, the proposed method is completely lossless, and therefore cent percent of data can be recovered from the encrypted image The decryption process is also simple to implement ie just reverse of the encryption procedure A scrambling algorithm is also proposed to further enhance the security of the overall system The simulation, detailed analysis, and comparative studies of the proposed overall image encryption framework will help to understand the strengths and weaknesses of it The experimental results are very promising and show the prospects of chaos theory and its usage in the field of data security

A chaotic framework and its application in image encryption

Visually meaningful image encryption algorithm based on digital signature

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.

Raja Swarnakar

Papers

Data Security Techniques Based on DNA Encryption