Seeded Region Growing Features Extraction Algorithm; Its Potential Use in Improving Screening for Cervical Cancer

Abstract

Region growing algorithm has successfully been used as a segmentation technique of digital images. The current study went one step further by utilizing the potential use of thresholding the region growing algorithm as features extraction technique. The proposed features extraction algorithm is called seeded region growing features extraction (SRGFE). This algorithm was used to extract four features of cervical cells; size of nucleus, size of cytoplasm, grey level of nucleus and grey level of cytoplasm. Correlation test was applied between data extracted using the proposed SRGFE algorithm with the data extracted manually by cytotechnologists. The high correlation value obtained in the correlation test show that the SRGFE algorithm is suitable and has high capability to be used as an image extraction technique to extract important features of cervical cells. This would assist cytopathologists and cytotechnologists in the cervical cancer screening by providing accurate value of size and grey level of nuclear and cytoplasmic features.

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