**1. Introduction**

Medical imaging approaches, such as X-ray mammography, ultrasound and magnetic resonance imaging (MRI), play an important role in breast cancer detection [1]. X-ray mammography is the gold-standard method for breast cancer detection, but it has some limitations [2, 3], including harmful radiation, relatively high false-negative rates particularly with patients with dense breast tissue. Ultrasound presents good soft tissue contrast but fails in the presence of bone and air, and the image quality highly depends on operator [4]. MRI allows physicians to evaluate various parts of human body and determine the presence of certain diseases [5], but it is too expensive [6]. Therefore, it is important and necessary to develop a new imaging technique for early breast cancer detection.

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In the late 1970s, Larsen et al. obtained the first microwave image of canine kidney [7]. Since then, MI has been intensively studied by many research groups [8–18], and the research objectives have been moved from imaging of organs to application-specific imaging for various tissues such as breast, joint tissues, blood and soft tissues. MI has been recommended as a safe, low-cost and low health risk alternative to existing medical imaging techniques including X-ray mammography and ultrasound. In the past many years, people paid too much attention to the MI algorithms. Several algorithms have been developed and validated numerically and in laboratory environments but they have not extensively validated in clinical environments. Recent clinical trial results demonstrated that more attention should be paid to the hardware implementation system, especially microwave sensors and sensor arrays, in clinical environments rather than laboratory environments.

This chapter presents the basic ideas of MI including currently available breast imaging methods which have been considered as important approaches for early breast cancer detection. The starting point for the development of MI methods is the formulation of the electromagnetic inverse scattering problem. Inverse scattering-based procedures address the data inversion in several different ways, depending on the target itself or on the imaging configuration and operation conditions. In this chapter, electrical properties of biological tissues, MI approaches and biomedical applications and several proof-of-concept apparatuses, including advantages, challenges and possible solutions, as well as future research directions are addressed.

#### **2. Dielectric properties of biological tissues**

The dielectric properties (DPs, relative permittivity ε*<sup>r</sup>* and conductivity σ) of malignant tissues at the microwave spectrum change significantly compared to the normal tissue and the dielectric contrast can be detected and imaged by applying MI approaches [19]. The DPs of different types of biological tissues are very different due to water content difference, which are strongly nonlinear functions with frequency [20]. Choosing suitable operating frequencies for the MI system is a critical task, and the attenuation of RF signals increases with frequency due to increase in the conductivity, resulting in a lower penetration depth. Several computer models have been developed to investigate biological tissues. Debye and Cole-Cole models are the most commonly used models. The Debye model simulates the frequency dependence of DPs of tissues sufficiently [21]:

$$\varepsilon\_r = \varepsilon\_{\infty} + \frac{\varepsilon\_s + \varepsilon\_{\infty}}{1 + j\omega\tau} - j\frac{\sigma}{\alpha\beta\varepsilon\_0} \tag{1}$$

where ε*∞* means the permittivity value of the tissue, ε<sup>s</sup> is the static permittivity of the tissue, and τ is characteristic relaxation time of the medium.

Cole-Cole model is defined as [22]:

$$
\varepsilon^\*(\omega \,) = \varepsilon\_{\omega} + \frac{\varepsilon\_\* - \varepsilon\_{\omega}}{1 + (j\omega \, r)^{t \cdot a}} \tag{2}
$$

where ε\* is the complex dielectric constant, ε<sup>s</sup> and ε*∞* are static and infinite frequency dielectric constants, ω is the angular frequency and τ is a time constant. The exponent parameter α, which takes a value between 0 and 1, describes different spectral shapes. When α = 0, the Cole-Cole model becomes to the Debye model.

Many research groups have investigated DPs of various biological tissues, including breast, heart, skin, liver, bone and lymph nodes [23–31]. Some factors that make effects on DPs of tissues include water content [20], change in the dielectric relaxation time [30], charging of the cell membrane [31], sodium content [31] and necrosis and inflammation causing breakdown of cell membrane [32].
