Bioinformation Analysis and Processing

[19] Zhang X, Wang X. Multiple-image encryption algorithm based on mixed image element and permutation. Optics and Lasers in Engineering. 2017;**92**:6-16

*Multimedia Information Retrieval*

[28] Dagadu JC, Jianping L. Contextbased watermarking cum chaotic encryption for medical images in telemedicine applications. Multimedia Tools and Applications. 2018;**77**:

[29] Maheshkar S. Region-based hybrid medical image watermarking for secure telemedicine applications. Multimedia Tools and Applications. 2017;**76**(3):

[30] Singh AK, Dave M, Mohan A. Robust and secure multiple watermarking in wavelet domain. Journal of Medical Imaging and Health

Informatics. 2015;**5**(2):406-414

[31] Lian S, Liu Z, Yuan D, Wang H. On the joint audio fingerprinting and decryption scheme. In: IEEE

International Conference on Multimedia and Expo; Hannover. 2008. pp. 261-264

[32] Abhilasha S, Kumar A, Singh S, Prakash G. Robust and secure multiple watermarking for medical images. Wireless Personal Communications.

[33] Obin A, Varghese P. Image watermarking using DCT in selected pixel regions. In: International

Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT). Kanyakumari; 2014. pp. 398-402. DOI : 10.1109/

2017;**92**:1611-1624

ICCICCT.2014.6992994

24289-24312

3617-3647

[20] Zhang X, Wang X. Multiple-image encryption algorithm based on mixed image element and chaos. Computers and Electrical Engineering. 2017;**000**:

[21] Zhu G, Zhang X. Mixed image element encryption based on an elliptic

Electronic Imaging. 2008;**17**(2):023007

[22] Abdalla A, Tamimi A. Algorithm for image mixing and encryption. The International Journal of Multimedia & Its Applications (IJMA). 2013;**5**(2):15-21

[23] Zhou Y, Bao L, Chen C. A new 1D chaotic system for image encryption. Signal Processing. 2014;**97**:172-182

[24] Ren G, Han J, Zhu H, Fu J, Shan M.

Communication. 2016;**11**(5):491-497

[25] Karawia A. Encryption algorithm of multiple-image using mixed image elements and two-dimensional chaotic economic map. Entropy. 2018;**20**:801.

[26] Al-Haj A, Mohammad A. Cryptowatermarking of transmitted medical images. Journal of Digital Imaging. 2017;

[27] Abdel-Nabi H, Al-Haj A. Efficient joint encryption and data hiding algorithm for medical images security. In: 8th International Conference on Information and Communication Systems (ICICS). Irbid, Jordan: IEEE; 4-6 April 2017. pp. 147-152. DOI : 10.1109/IACS.2017.7921962

High security multiple-image encryption using discrete cosine transform and discrete multipleparameters fractional Fourier transform. The Journal of

DOI: 10.3390/e20100801

**30**(1):26-38

**38**

curve cryptosystem. Journal of

1-13

**41**

**1. Introduction**

**Chapter 3**

**Abstract**

Information Extraction

Techniques in Hyperspectral

*Samuel Ortega, Martin Halicek, Himar Fabelo,* 

information extraction techniques in clinical applications.

machine learning, deep learning, image processing

**Keywords:** hyperspectral imaging, biomedical, clinical, information extraction,

Hyperspectral imaging (HSI), also known as imaging spectroscopy, is a technology capable of sampling hundreds of narrow spectral bands across the electromagnetic spectrum through the use of an optical element that disperses the incoming radiation into certain wavelengths [1]. This technology combines the main features of two existing technologies: imaging and spectroscopy, making possible to exploit both the morphological features and the chemical composition of objects captured by a camera. The interaction between electromagnetic radiation and matter is distinctive for each material, therefore by using this technology it is possible to discriminate among different materials [2]. The characteristic spectral curve associated with a certain material is called spectral signature or spectral fingerprint, and through its analysis it

Imaging Biomedical Applications

*Eduardo Quevedo, Baowei Fei and Gustavo Marrero Callico*

Hyperspectral imaging (HSI) is a technology able to measure information about the spectral reflectance or transmission of light from the surface. The spectral data, usually within the ultraviolet and infrared regions of the electromagnetic spectrum, provide information about the interaction between light and different materials within the image. This fact enables the identification of different materials based on such spectral information. In recent years, this technology is being actively explored for clinical applications. One of the most relevant challenges in medical HSI is the information extraction, where image processing methods are used to extract useful information for disease detection and diagnosis. In this chapter, we provide an overview of the information extraction techniques for HSI. First, we introduce the background of HSI, and the main motivations of its usage for medical applications. Second, we present information extraction techniques based on both light propagation models within tissue and machine learning approaches. Then, we survey the usage of such information extraction techniques in HSI biomedical research applications. Finally, we discuss the main advantages and disadvantages of the most commonly used image processing approaches and the current challenges in HSI
