Cancer is arguably one of the most dangerous diseases and the major causes of death in the modern day. It becomes increasingly harder to treat and cure the disease as it makes progress. Detecting cancer at an early stage can help in preventing it from affecting an organism. However, it is very hard to detect at an early stage. The best possible way to tackle this disease is to first study it at a cellular level. This study aims at identifying various phenotypic traits of these cells in the Dielectrophoresis (DEP) based microfluidic device experimental setup and subsequently classifying the cells from the rest. A general framework for automatic labeling, identifying and classifying the malignant from the dead cells is developed in this work. The framework shows a top-down approach starting from static background subtraction, tracking, automatic labeling, feature extraction and finally classification. The data used in this work are videos of live and dead human prostate cancer (PC-3) cells flowing through the microfluidic device. Previous studies have shown that there are significant differences in morphological attributes between cancerous and non-cancerous cells. We focus mainly on shape, texture and geometry as the prominent attribute in our work and subsequently use them for classification. In this work we obtain good tracking results through optical flow as compared to previous work. For classification, linear classifiers such as logistic regression and linear Support Vector Machine (SVM) showed decent results. The machine learning algorithms use Histogram of Oriented Gradient (HOG) features plus the elliptical features as a combined feature vector. The elliptic features branch out this study to another direction that is useful in calculation of physical properties such as the cell elasticity through video processing and we propose a model for the same for the given setup. Currently, the elasticity of a single cell is calculated using expensive and time consuming procedures such as the atomic force microscopy (AFM). Using our framework, we can potentially obtain elasticity for a batch of cells in much less time. Also, our cell classification algorithm procedure is suitable for real time applications and can be a proposed futuristic concept for selective killing of cells.