@article{scholars620,
            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},
          volume = {5857 L},
             doi = {10.1007/978-3-642-05036-7{$_2$}{$_0$}},
           title = {Segmentation of sinus images for grading of severity of sinusitis},
         address = {Kuala Lumpur},
            year = {2009},
         journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
           pages = {202--212},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-76549107769&doi=10.1007\%2f978-3-642-05036-7\%5f20&partnerID=40&md5=fefa99738095b7e8051acf170945d6be},
        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},
        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. {\^A}{\copyright} 2009 Springer-Verlag.},
          author = {Iznita Izhar, L. and Sagayan Asirvadam, V. and Lee, S. N.},
            issn = {03029743},
            isbn = {3642050352; 9783642050350}
}