A Pilot Study on Image Analysis Techniques for Extracting Early Uterine Cervix Cancer Cell Features

Abstract

The second most common and preventable form of cancer among women worldwide is cervical cancer in which the signs
for this disease can be detected in the early Pap smear screening of cervical cells. To improve the efficiency of expert diagnosis,
we will need to automate the feature extraction of cervical cancer cells by the means of image processing techniques. This article
employs image processing techniques to get the special features of normal, precancerous and cancerous cell images. We extract
spectral features for cervical cancer cell detection. This article uses the noise decrease filters, OTSU threshold to make it ready for
processing through 2-D Fourier and logarithmic transforms. By drawing the linear plot, we will be able to extract the feature of
normal, precancerous and cancerous cells according to the texture and morphology automatically. These linear plots will be unique
which can classify the cells in three groups normal, precancerous and cancerous cells.The experiment shows that extracted 
unique features for each cell will provide evidences for diagnoses even in cytopathology images in which the nucleus and cytoplasm
 segmentation algorithms suffer from complex overlaying cells.