Pattern recognition on 2D cervical cytological digital images for early detection of cervix cancer

To date, cancer of the uterine cervix is still a leading cause of cancer-related deaths in women in the world. Papanicolau smear test is a well-known screening method of detecting abnormalities in the uterine cervix cells. In Indonesia, Pap smear test is mostly still done conventionally. Due to the small number of skilled and experienced cytologists, the screening procedure becomes time consuming and highly prone to human errors. Coping with these issues, an automated recognition system is developed to enable automatic identification of anomaly in the cervix cells. Recognition of patterns inside a cervix cell is based on the cell morphological features, in terms of size, shape, and color. Therefore, three parameters are employed, i.e. N/C ratio, wavelet approximation coefficients, and color intensity. Based on thorough observation upon the selected parameters, it can be recognized that the cancerous cells follow certain patterns and highly distinguishable from the normal cells.