eprintid: 620
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/06/20
datestamp: 2023-11-09 15:48:46
lastmod: 2023-11-09 15:48:46
status_changed: 2023-11-09 15:22:49
type: article
metadata_visibility: show
creators_name: Iznita Izhar, L.
creators_name: Sagayan Asirvadam, V.
creators_name: Lee, S.N.
title: Segmentation of sinus images for grading of severity of sinusitis
ispublished: pub
keywords: Computed tomography scan; CT scan; Detection and diagnosis; Equalisation; Median filtering; Multilevel thresholding; Noise removal; Otsu method; Region growing, Algorithms; Computerized tomography; Feature extraction; Magnetic resonance imaging; Resonance; Ultrasonic applications, Image enhancement
note: cited By 3; Conference of 1st International Visual Informatics Conference, IVIC 2009 ; Conference Date: 11 November 2009 Through 13 November 2009; Conference Code:79347
abstract: Sinusitis is commonly diagnosed with techniques such as endoscopy, ultrasound, X-ray, Computed Tomography (CT) scan and Magnetic Resonance Imaging (MRI). Out of these techniques, imaging techniques are less invasive while being able to show blockage of sinus cavities. This project attempts to develop a computerize system by developing algorithm for the segmentation of sinus images for the detection of sinusitis. The sinus images were firstly undergo noise removal process by median filtering followed by Contrast Limited Adapted Histogram Equalisation (CLAHE) for image enhancement. Multilevel thresholding algorithm were then applied to segment the enhanced images into meaningful regions for the detection and diagnosis of severity of sinusitis. The multilevel thresholding algorithms based on Otsu method were able to extract three distinct and important features namely bone region, hollow and mucous areas from the images. Simulations were performed on images of healthy sinuses and sinuses with sinusitis. The developed algorithms are found to be able to differentiate and evaluate healthy sinuses and sinuses with sinusitis effectively. © 2009 Springer-Verlag.
date: 2009
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-76549107769&doi=10.1007%2f978-3-642-05036-7_20&partnerID=40&md5=fefa99738095b7e8051acf170945d6be
id_number: 10.1007/978-3-642-05036-7₂₀
full_text_status: none
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 5857 L
place_of_pub: Kuala Lumpur
pagerange: 202-212
refereed: TRUE
isbn: 3642050352; 9783642050350
issn: 03029743
citation:   Iznita Izhar, L. and Sagayan Asirvadam, V. and Lee, S.N.  (2009) Segmentation of sinus images for grading of severity of sinusitis.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5857 L.  pp. 202-212.  ISSN 03029743