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Proceedings ArticleDOI

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

TL;DR: In this work, some application of cryptographic methods have been discussed which are used to protect biomedical images from unwanted modifications and access and can be highly beneficial in future research in the field of biomedical image protection as well as multimedia data security.
Abstract: 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.
Citations
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Book ChapterDOI
01 Jan 2020
TL;DR: In this chapter, a comprehensive overview of the deep learning-assisted biomedical image analysis methods is presented and can be helpful for the researchers to understand the recent developments and drawbacks of the present systems.
Abstract: 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.

28 citations

Book ChapterDOI
01 Jan 2020
TL;DR: This chapter proposes a new filter (kernel), and the compass operator is applied on it to detect edges more efficiently, and the results are compared with some of the previously proposed filters both qualitatively and quantitatively.
Abstract: 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.

24 citations

Book ChapterDOI
17 Aug 2019
TL;DR: 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.
Abstract: 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.

20 citations

Book ChapterDOI
17 Aug 2019
TL;DR: A secure and lossless encryption method is developed in this work and various numerical parameters are used to evaluate the performance of the proposed method which proves the effectiveness of the algorithm.
Abstract: 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.

15 citations

Journal ArticleDOI
TL;DR: The proposed 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.
Abstract: 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.

11 citations

References
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Proceedings ArticleDOI
01 Aug 2017
TL;DR: Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features, which has been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure.
Abstract: Automated, efficient and accurate classification of skin diseases using digital images of skin is very important for bio-medical image analysis. Various techniques have already been developed by many researchers. In this work, a technique based on meta-heuristic supported artificial neural network has been proposed to classify images. Here 3 common skin diseases have been considered namely angioma, basal cell carcinoma and lentigo simplex. Images have been obtained from International Skin Imaging Collaboration (ISIC) dataset. A popular multi objective optimization method called Non-dominated Sorting Genetic Algorithm — II is employed to train the ANN (NNNSGA-II). Different feature have been extracted to train the classifier. A comparison has been made with the proposed model and two other popular meta-heuristic based classifier namely NN-PSO (ANN trained with Particle Swarm Optimization) and NN-GA (ANN trained with Genetic algorithm). The results have been evaluated using various performances measuring metrics such as accuracy, precision, recall and F-measure. Experimental results clearly show the superiority of the proposed NN-NSGA-II model with different features.

39 citations


"A Study on the Applications of the ..." refers background in this paper

  • ...Therefore integrity of the biomedical images is necessary to produce reliable results [10]–[13]....

    [...]

Journal ArticleDOI
TL;DR: An algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem and is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop.
Abstract: The Job-Shop Scheduling Problem (JSSP) is a well-known and one of the challenging combinatorial optimization problems and falls in the NP-complete problem class. This paper presents an algorithm based on integrating Genetic Algorithms and Simulated Annealing methods to solve the Job Shop Scheduling problem. The procedure is an approximation algorithm for the optimization problem i.e. obtaining the minimum makespan in a job shop. The proposed algorithm is based on Genetic algorithm and simulated annealing. SA is an iterative well known improvement to combinatorial optimization problems. The procedure considers the acceptance of cost-increasing solutions with a nonzero probability to overcome the local minima. The problem studied in this research paper moves around the allocation of different operation to the machine and sequencing of those operations under some specific sequence constraint.

39 citations


"A Study on the Applications of the ..." refers methods in this paper

  • ...Several metaheuristic optimization algorithms [15]–[20] and hybrid metaheuristics [21] are fused with these methods to enhance the efficiency....

    [...]

Proceedings ArticleDOI
01 Oct 2017
TL;DR: Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model in terms of testing phase confusion matrix based performance measuring metrics.
Abstract: The current work proposes a neural based detection method of two different skin diseases using skin imaging. Skin images of two diseases namely Basel Cell Carcinoma and Skin Angioma are utilized. SIFT feature extractor has been employed followed by a clustering phase on feature space in order to reduce the number of features suitable for neural based models. The extracted bag-of-features modified dataset is used to train metaheuristic supported hybrid Artificial Neural Networks to classify the skin images in order to detect the diseases under study. A well-known multi objective optimization technique called Non-dominated Sorting Genetic Algorithm — II is used to train the ANN (NN-NSGA-II). The proposed model is further compared with two other well-known metaheuristic based classifier namely NN-PSO (ANN trained with PSO) and NN-CS (ANN trained with Cuckoo Search) in terms of testing phase confusion matrix based performance measuring metrics such as accuracy, precision, recall and F-measure. Experimental results indicated towards the superiority of the proposed bag-of-features enabled NN-NSGA-II model.

36 citations

Journal ArticleDOI
TL;DR: A chaotic medical image encryption algorithm based on bit-plane decomposition that combines the image encryption and chaotic system to improve the security and can increase the security of key space and image effectively.
Abstract: Due to the rapid rise of telemedicine, a lot of patients’ information will be transmitted through the Internet. However, the patients’ information is related to personal privacy, therefore, patients’ information needs to be encrypted when transmited and stored. Medical image encryption is a part of it. Due to the informative fine features of medical images, a common image encryption algorithm is no longer applied. Common encryption algorithm has a single theory based on chaos image encryption algorithm, other encryption algorithms are based on information entropy. However, the images processed with these cipher text encryption algorithm are cyclical, the outline is clear and the anti-tamper capability is not strong. In view of the bit being the smallest measure unit of pixel, in order to overcome the weakness from above algorithm, and take the advantage of the chaotic system, this paper will present a chaotic medical image encryption algorithm based on bit-plane decomposition. The paper combines the image encryption and chaotic system to improve the security. This way, it can increase the security of key space and image effectively. The histogram, pixel correlation, number of pixels change rate (NPCR) and other experimental results show that the algorithm satisfies the desired effect.

32 citations


"A Study on the Applications of the ..." refers methods in this paper

  • ...Another image encryption scheme based on hyper chaotic systems and Logistic-Sine map is proposed in [36]....

    [...]

Proceedings ArticleDOI
01 Aug 2017
TL;DR: This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation and a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation.
Abstract: This work describes an method for biomedical image enhancement using modified Cuckoo Search Algorithm with some Morphological Operation. In recent years, various digital image processing techniques are developed. Computer Vision, machine interfaces, manufacturing industry, data compression for storage, vehicle tracking and many more are some of the domains of digital image processing application. In most of the cases, digital biomedical images contains various types of noise, artifacts etc. and are not useful for direct applications. Before using it in any process, the input image has to be gone through some preprocessing stages; such preprocessing is generally called as image enhancement. In this work, a new technique has been proposed to enhance biomedical images using modified cuckoo search algorithm and morphological operation. Presence of noise and other unwanted objects generates distortion in an image and it will affect the ultimate result of the process. In case of biomedical images, accuracy of the results is very important. It may also decrease the discernibility of many features inside the images. It can affect the classification accuracy. In this work, this issue has been targeted and improved by obtaining better contrast value after converting the color image into grayscale image. The basic property of the cuckoo search algorithm is that the amplitudes of its components are capable to objectively describe the contribution of the gray levels to the formation of image information for the best contrast value of a digital image. The proposed method modified the conventional cuckoo search method by employing the McCulloch's method for levy flight generation. After computing the best contrast value, morphological operation has been applied. In morphological operation based phase, the intensity parameters are tuned for quality enhancement. Experimental results illustrate the effectiveness of this work.

32 citations