An Adaptive Neuro-Fuzzy Based Region Selection and Authenticating Medical Image Through Watermarking for Secure Communication

One of the important issues in telemedicine field refers to an advanced secure communication. Digital image watermarking is an ideal solution since it protects the electronic patient information’s from unauthorized access. This paper presents a novel blind fragile-based image watermarking scheme in spatial domain that merges Speed Up Robust Features (SURF) descriptor with the well-known Weber Descriptors (WDs) and Arnold algorithm. It provides a good way for enhancing the image quality and time complexity for medical data integrity. Firstly, the watermark image is shuffled using Arnold chaotic map. Secondly, the SURF technique is practiced to Region of Interest (ROI) of the medical image and then the blocks around the SURF points are selected to insert the watermark. Finally, the watermark is encrusted and extracted using WDs. Experimental results show good image fidelity with the shortest execution time to ensure medical images integrity.

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Fuzzy-Based Medical Image Processing

Human-Computer Interaction ◽

10.4018/978-1-4666-8789-9.ch030 ◽

2015 ◽

pp. 666-681

Author(s):

G. R. Sinha

Keyword(s):

Image Processing ◽

Medical Image ◽

Medical Images ◽

Medical Image Analysis ◽

Hybrid Approach ◽

Medical Image Processing ◽

Medical Image Segmentation ◽

Clustering Methods ◽

Neuro Fuzzy ◽

Fuzzy C Means Clustering

Medical Image Processing (MIP) is a set of tools applied over medical images, which consists of several components such as image acquisition, enhancement, segmentation, restoration, etc. The most important component of MIP is medical image segmentation used in Computer-Aided Diagnosis (CAD) systems used for detection of abnormalities in medical images. This chapter presents an overview and the importance of soft computing techniques in solving the problems of medical imaging. The authors highlight the significance of fuzzy-based clustering and similar methods for MIP and its applications. Fuzzy C-Means Clustering Method (FCM) is found the most suitable method among existing clustering methods for medical images. FCM addresses the problem of over-segmentation and helps in improvement of diagnosis accuracy. Application of optimization tool causes the reduction of execution time. A comparison of fuzzy-based methods over conventional methods suggests that neuro-fuzzy system as hybrid approach is an efficient method for medical image analysis.

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Reliable Medical Image Communication in Healthcare IoT

Medical Data Security for Bioengineers - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-5225-7952-6.ch001 ◽

2019 ◽

pp. 1-26 ◽

Cited By ~ 1

Author(s):

Siva Janakiraman ◽

Sundararaman Rajagopalan ◽

Rengarajan Amirtharajan

Keyword(s):

Secure Communication ◽

Medical Image ◽

Personal Information ◽

Medical Diagnostics ◽

Medical Field ◽

Reliable Communication ◽

Pixel Resolution ◽

Image Communication ◽

Iot Applications ◽

Performance Validation

Images have been widely used in the medical field for various diagnostic purposes. In the field of healthcare IoT, secure communication of a medical image concerned with an individual is a crucial task. Embedding patients' personal information as an invisible watermark in their medical images helps to authenticate the ownership identification process. Reliable communication of medical image can be thereby ensured concerning authentication and integrity. Images in DICOM format with a pixel resolution of 8-bit depth are used for medical diagnostics. This chapter deals about the development of a lightweight algorithm to insert patients' identities as an invisible watermark in random edge pixels of DICOM images. This chapter describes the implementation of the proposed lightweight watermarking algorithm on a RISC microcontroller suitable for healthcare IoT applications. Imperceptibility level of the watermarked medical image was analyzed besides its lightweight performance validation on the constrained IoT platform.

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