Predicting the Severity of Cervical Cancer Using Image Processing Techniques
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 1
Abstract
Cervical cancer is a malignant disease that develops in the cells of the cervix or on the neck of the uterus. A Pap smear, also called as Pap test, is a procedure to test cervical cancer in women. PAP smear test is an efficient and easy procedure to detect abnormalities in the cervical cells at the earlier stage. We proposed a method for automated diagnosis of cervical cancer by extracting cytoplasm and nuclei from Pap smear images. The background is removed by pre-processing methods like Edge sharpening and Adaptive Histogram Equalization. Fuzzy density based automatic thresholding and Active contours are used for extracting the region of interest which contains the cytoplasm and nuclei. An automatic thresholding selection is done by using fuzzy set theory and fuzzy density model. Fuzzy set theory is used to analyze images and also it provides accurate information of the image. The nucleus to cytoplasm ratio is used to determine the stage of cancer (level of abnormality). The proposed approach is implemented in MATLAB, a high level, interactive environment for data visualization/analysis/computation. This may help the Pathologist in identification of cervical cancer from Pap smear images and helps in early diagnosis.
Authors and Affiliations
Prof. S. Maheswari, R. Revathy, K. Jayasudha, K. Yogalakshmi
Keywords
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