Brain Image Segmentation Based on Firefly Algorithm Combined with K-means Clustering

During the past few decades digital images have become an important part of numerous scientific fields. Digital images used in medicine enabled tremendous progress in the diagnostics, treatment determination process as well as in monitoring patient recovery. Detection of brain tumors represents one...

Full description

Saved in:
Bibliographic Details
Published in:Studies in Informatics and Control 2019, Vol.28 (2), p.167
Main Authors: CAPOR HROSIK, Romana, TUBA, Eva, DOLICANIN, Edin, JOVANOVIC, Raka, TUBA, Milan
Format: Article
Language:eng ; fre
Subjects:
Algorithms
Brain cancer
Metastasis
Neuroimaging
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:During the past few decades digital images have become an important part of numerous scientific fields. Digital images used in medicine enabled tremendous progress in the diagnostics, treatment determination process as well as in monitoring patient recovery. Detection of brain tumors represents one of the active research fields and an algorithm for brain image segmentation was developed with an aim to emphasize four different primary brain tumors: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma and sarcoma from PET, MRI and SPECT images. The proposed image segmentation method is based on the firefly algorithm whose solutions are improved by the k-means clustering algorithm when Otsu’s criterion was used as the fitness function. The proposed combined algorithm was tested on commonly used images from Harvard Whole Brain Atlas and the results were compared to other method from literature. The method proposed in this paper achieved better segmentation considering standard segmentation quality metrics such as normalized root square mean error, peak signal to noise and structural similarity index metric.
ISSN:1220-1766
1841-429X
DOI:10.24846/v28i2y201905