Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods
Dublin Core
Title
Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods
Subject
pap smear; cervical cancer; nucleus; GLCM otsu; GLCM polynomial
Description
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 publicly accessible repository containing 2,346 images. Both methods were compared and
evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was
6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the
nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection
results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on
average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a
reference for further studies in developing new methods to enhance the accuracy of identification.
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 publicly accessible repository containing 2,346 images. Both methods were compared and
evaluated to obtain the most accurate features. The research results showed that the average distance of the Otsu method was
6.6457, which was superior to the Polynomial method with a value of 6.6215. Distance refers to the distance between the
nucleus detected by the Otsu and the Polynomial method. Distance is an important measure to assess how closely the detection
results align with the actual nucleus positions. It indicates that the Polynomial method produces nucleus detections that are on
average closer to the actual nucleus positions compared to the Otsu method. Consequently, this research can serve as a
reference for further studies in developing new methods to enhance the accuracy of identification.
Creator
Dwiza Riana, Jufriadif Na'am, Daniati Uki Eka Saputri, Sri Hadianti, Faruq Aziz, Suryadi Putra
Liawatimena, Alya Shafra Hewiz, Dika Putri Metalica, , Teguh Herwanto
Liawatimena, Alya Shafra Hewiz, Dika Putri Metalica, , Teguh Herwanto
Source
http://jurnal.iaii.or.id
Publisher
Professional Organization Ikatan Ahli Informatika Indonesia (IAII)/Indonesian Informatics Experts Association
Date
December 2023
Contributor
Sri Wahyuni
Rights
ISSN Media Electronic: 2580-076
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Dwiza Riana, Jufriadif Na'am, Daniati Uki Eka Saputri, Sri Hadianti, Faruq Aziz, Suryadi Putra
Liawatimena, Alya Shafra Hewiz, Dika Putri Metalica, , Teguh Herwanto, “Comparison of Segmentation Analysis in Nucleus Detection with GLCM Features using Otsu and Polynomial Methods,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/10133.