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

EMITTER International Journal of Engineering Technology

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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)
Date 2019-06-15
Type info:eu-repo/semantics/article
Peer-reviewed Article
Speeded Up Robust Feature (SURF)
Format application/pdf
Source EMITTER International Journal of Engineering Technology; Vol 7, No 1 (2019); 1-13
Language eng

Rights Copyright (c) 2019 EMITTER International Journal of Engineering Technology

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