Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods
Dwiza Riana,
Jufriadif Na'am,
Daniati Uki Eka Saputri Saputri
et al.
Abstract:Pap smear is a digital image generated from the recording of cervical cancer cell preparation. Images generated are susceptible to errors due to the relatively small cell sizes and overlapping cell nuclei. Therefore, accurate Pap smear image analysis is essential to obtain the right information. This research compares nucleus segmentation and detection using Grey Level Co-occurrence Matrix (GLCM) features in two methods: Otsu and Polynomial. The tested data consisted of 400 images sourced from RepoMedUNM, a pu… Show more
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