**1. Introduction**

Cervical cancer is the fourth most prevalent cancer among women worldwide. Human Papillomavirus (HPV) is known to be the main cause of cervical cancer [1]. It is well-known that cervical cancer has long latency period. Pre-malignant abnormalities in cervical cells can take up to a decade to progress to carcinoma. Early diagnosis of pre-cancerous cervical cells and treatment help in halting the progression of this fatal cancer [2]. The five year survival rate for patients suffering from cervical cancer has been documented to be over 60% [3]. In spite of the long latency period for cervical cancer to develop, the mortality rate among cervical cancer patients is high in developing countries due to shortage of skilled clinicians and lack of effective screening tools [4, 5].

The cervix which is the innermost part of uterus is sub-divided between the endo-cervix and ecto-cervix regions. The endo-cervix is composed of glandular cells whereas the ecto-cervix is made up of squamous cells. Transformation zone is the place where these regions adjoin, and where most of the cervical cancer is known to originate [6]. Starting with the transformation zone the cells in the squamous region are typically classified as basal, para-basal, intermediate and superficial respectively. The majority of cells in a typical Pap-smear cell sample used for examination by clinicians are from the intermediate and superficial outer layers. The classification of precancerous cells as low grade and high grade is established through Bethesda system [7, 8] which is based on morphological changes in the cells (particularly the cell nuclei). Traditionally the detection of precancerous cervical cells is primarily performed using cytological screening. The widespread usage of liquid based cytology (LBC) in recent years has made the process of sample preparation and examination more uniform. However, screening methods based on visual inspection can suffer from both inter- and intra-observer variability. Machine learning based approaches have gained attention in this regard [9–13] and standard benchmark datasets of cervical cell images have been created [14, 15]. The goal of machine learning approaches is to make the process of cell classification at least semi-automated and to provide an assisting tool for cyto-pathologists.

The machine learning based studies have focused mainly on the 2D bright-field images of the cervical cells and their nuclei. Since cells are 3D objects, we believe that additional morphological information in the third (depth) dimension of cells, if available, can provide new information and help any image based cell classification. Digital Holographic Microscopy (DHM) is an interferometric imaging technology [16–18] which can fill this gap and provide quantitative phase information that may then be related to the depth dimension of the cells. When a coherent beam of light of wavelength *λ* is transmitted through a cell, the wave-front undergoes a phase change given by:

$$
\phi(\mathbf{x}, \mathbf{y}) = \frac{2\pi}{\lambda} \int dz \,\ n(\mathbf{x}, \mathbf{y}, \mathbf{z}).\tag{1}
$$

Here n(x,y,z) represents the refractive index distribution within the cell relative to its surroundings. It is important to understand that phase provides new nonredundant information that cannot be derived from the usual 2D bright-field images. The phase change *ϕ*ð Þ *x*, *y* cannot be measured directly by a 2D array sensor but may be recorded in the form of an interference fringe pattern. Here the coherent light source is first split into two beams, one of the beams passes through the cell sample and the other reference beam travels through free space before the two beams are recombined to record an interference pattern. As per Eq. (1), the phase function contains information on optical path length (product of refractive index and thickness) through the cell sample at location ð Þ *x*, *y* . While quantitative phase images have been shown to provide interesting new information about cancer cell morphology [19–22], clinically this modality is not yet popular and clinician are not trained to interpret quantitative phase images. We therefore follow a protocol where a focused bright-field image of a cervical cell is recorded along with phase image of the cell in the same focus plane. This way the clinicians can correlate with their traditional knowledge and treat phase images as an additional channel of morphological information. Recently we demonstrated such an approach for unsupervised organization of cervical cell images [23]. In the present imaging study

#### *Cytopathology Using High Resolution Digital Holographic Microscopy DOI: http://dx.doi.org/10.5772/intechopen.96459*

over a much larger sample size with samples collected from different clinical sites, we examine the structural changes in phase images of cervical cell nuclei and highlight their potential importance for cell classification. Even though 2D images are able to distinguish between the major stages of normal cells, the phase images allow one to observe the morphological changes as the cells evolve through these stages. Additionally we examine the structural characteristics of abnormal samples as identified by practicing cyto-pathologists.

Traditional DHM systems are based on single-shot off-axis interference configuration or the multi-shot phase shifting con-figuration. The single-shot off-axis systems are simpler and cheaper to build but the conventional Fourier filtering approach for phase reconstruction in these systems leads to sub-optimal phase resolution. The multi-shot phase shifting configurations offer full resolution but are hardware intensive and require stringent vibration isolation thus making them difficult to employ in clinical settings. In recent years our group has developed optimization based phase reconstruction algorithms [24–27] for single-shot DHM systems which offer the simplicity of hardware without compromising on resolution and quantitative phase accuracy. The full diffraction-limited resolution capability of our system allows us to treat the bright-field and phase images on par (with respect to their lateral resolution). The single-shot operation also reduces the cost of building a DHM system making it more accessible for wider deployment. Based on our imaging study we find that the phase images contain important morphological information associated with different classes of normal as well as abnormal cells. Further this phase information is seen to be robust across the samples from three clinical sites. Also the samples consisted of age group of 17–60 years of the subjects. The cell morphology captured as numerical parameters from the phase images can provide valuable additional information to clinicians over what they usually access with routine bright-field microscopy. Our results suggest that phase imaging can become an important clinical modality, and it should be possible to design phase-based software tools for clinicians to make better informed decisions with this new information. The Chapter is organized as follows. In Section 2 we explain the technique of digital holographic microscopy (DHM) and the nature of quantitative phase images along with our phase reconstruction methodology. Section 3 briefly describes the details of the cell samples used. The results are discussed in Section 4. In Section 4.1 we start by showing images of cervical cells in both bright-field and phase modalities to illustrate morphological changes in cervical cell nuclei. This is followed by PCA analysis of the cell data based on the morphological parameters derived from the cell images in Section 4.2. In Section 4.3 we describe our analysis to understand if the most important phase parameters for normal cells are consistent across different patients from same clinical site, across different clinical sites and between different age groups. Finally in Section 5 we provide concluding remarks.
