COMPUTERIZED SEGMENTATION OF SINUS IMAGES

• Main Author: LEE, SAN LAIEN
• Format: Final Year Project
• Language: English
• Published: 2009
• Online Access: http://utpedia.utp.edu.my/id/eprint/3270/1/0001.pdf; http://utpedia.utp.edu.my/id/eprint/3270/

Sinusitis is currently diagnosed with techniques such as endoscopy, ultrasound. X-ray. Computed Tomography (CT) scan and Magnetic Resonance Imaging (MIZI ). Out of these techniques, imaging techniques are less invasive while being able to show blockage of sinus cavities. However, the potential of these techniques have not been fully realized as the images obtained are still bound to misinterpretations. This work attempts to solve this problem by developing an algorithm for the computerized segmentation of sinus images for the detection and grading of' sinusitis. The image enhancement techniques used were median filtering and the Contrast Limited Adapted Histogram Equalisation (CLAI-IE). These techniques applied on input images managed to reduce noise and smoothen the image histogram. Multilevel thresholding algorithms were developed to segment the images into meaningful regions for the detection of sinusitis. These algorithms were able to extract important features from the images. The simulations were performed on images of healthy sinuses and sinuses with sinusitis. The algorithms are found to be able to detect and grade sinusitis. In addition, a 3-D model of the sinuses was constructed from the segmentation to facilitate in surgical planning of sinusitis.