Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF)

EMITTER International Journal of Engineering Technology

View Publication Info
 
 
Field Value
 
Title Nuclei Detection and Classification System Based On Speeded Up Robust Feature (SURF)
 
Creator Amalina, Neneng Nur
Ramadhani, Kurniawan Nur
Sthevanie, Febryanti
 
Subject Informatics & Computer

Computer Vision & Image Processing; AI & Pattern Recognition
 
Description Tumors contain a high degree of cellular heterogeneity. Various type of cells infiltrate the organs rapidly due to uncontrollable cell division and the evolution of those cells. The heterogeneous cell type and its quantity in infiltrated organs determine the level maglinancy of the tumor. Therefore, the analysis of those cells through their nuclei is needed for better understanding of tumor and also specify its proper treatment. In this paper, Speeded Up Robust Feature (SURF) is implemented to build a system that can detect the centroid position of nuclei on histopathology image of colon cancer. Feature extraction of each nuclei is also generated by system to classify the nuclei into two types, inflammatory nuclei and non-inflammatory nuclei. There are three classifiers that are used to classify the nuclei as performance comparison, those are k-Nearest Neighbor (k-NN), Random Forest (RF), and State Vector Machine (SVM). Based on the experimental result, the highest F1 score for nuclei detection is 0.722 with Determinant of Hessian (DoH) thresholding = 50 as parameter. For classification of nuclei, Random Forest classifier produces F1 score of 0.527, it is the highest score as compared to the other classifier.
 
Publisher Politeknik Elektronika Negeri Surabaya (PENS)
 
Contributor
 
Date 2019-06-15
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
Speeded Up Robust Feature (SURF)
 
Format application/pdf
 
Identifier http://emitter.pens.ac.id/index.php/emitter/article/view/288
10.24003/emitter.v7i1.288
 
Source EMITTER International Journal of Engineering Technology; Vol 7, No 1 (2019); 1-13
2443-1168
2355-391X
10.24003/emitter.v7i1
 
Language eng
 
Relation http://emitter.pens.ac.id/index.php/emitter/article/view/288/128
http://emitter.pens.ac.id/index.php/emitter/article/downloadSuppFile/288/26
 
Coverage


 
Rights Copyright (c) 2019 EMITTER International Journal of Engineering Technology
http://creativecommons.org/licenses/by-nc-sa/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library