relation: https://khub.utp.edu.my/scholars/620/
title: Segmentation of sinus images for grading of severity of sinusitis
creator: Iznita Izhar, L.
creator: Sagayan Asirvadam, V.
creator: Lee, S.N.
description: 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
type: Article
type: PeerReviewed
identifier:   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     
relation: 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
relation: 10.1007/978-3-642-05036-7₂₀
identifier: 10.1007/978-3-642-05036-7₂₀