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Cervix cancer detection based on pattern recognition in cervical cytological slide images

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Cervical cancer is the most common type of cancer in women throughout the world. This cancer is largely preventable through screening during its early stages and appropriate treatment. The effective screening test for detecting pre-malignant cells in uterine cervix before the progress into cancer is called Pap smear. Pap smear using Papanicolau staining method emphasizes the different components of the cells with specific colors. Available cell features like morphology, shape and color of nucleus and cytoplasm are used for the classification of Pap smear image into normal and abnormal cells. However, the screening which is done manually by the cytologist was inefficient and time consuming. It requires high skill and experiences of the cytologists. This requirement reads to a condition that the diagnosis inherently prone to the human error. Coping with this problem, this research is to introduce an automated diagnosis algorithm for early detection of cervix cancer. The diagnosis algorithm is developed to recognize pattern on 2-dimentional digital cervical cytological image produced from Pap smear test. Pattern recognition is applied to variables of cell morphology and color intensity. Afterwards, measurement and identification of cells into normal and abnormal class is done on the basis of parameters color intensity, N/C ratio, and 2D wavelet approximation coefficients.rnThe automated diagnosis algorithm is intended to improve reliability as well as reduce time consumption in the conventional diagnosis of cervix cancer. Therefore, this will produce more accurate, faster and less expensive analysis of Pap smear test, which can be used to provide better health service for public community.

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Availability

B00638

(wh)

Available

Detail Information

Series Title

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Call Number

638

Publisher

: Swiss German University., 2009

Collation

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Language

English

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Classification

NONE

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Edition

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Subject(s)

BM

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