The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
The Image Processing Handbook, Fifth Edition (Image Processing Handbook)
An automated cervical pre-cancerous diagnostic system
Artificial Intelligence in Medicine
An Automated Method for Segmentation of Epithelial Cervical Cells in Images of ThinPrep
Journal of Medical Systems
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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 separate the cells in three groups of normal, precancerous and cancerous cells. This separation is done with 100% accuracy due to the unique linear plots. 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.