Title :
Dental cyst diagnosis using texture analysis
Author :
Vijayakumari, B. ; Ulaganathan, G. ; Banumathi, A. ; Banu, A.F.S. ; Kayalvizhi, M.
Author_Institution :
Dept. of ECE, Thiagarajar Coll. of Eng., Madurai, India
Abstract :
Dental or oral cysts are fairly a common occurrence in the mouth. There are several common types of dental cysts like periapical cyst, keratocyst, primordial and dentigerous cysts. The most common treatment for cysts is removal of the cyst region. Differentiating odontogenic keratocysts and ameloblastomas from other cystic lesions in the maxillomandibular region is important because of their high recurrence rates. Conventional radiography, CT, and fine-needle aspiration biopsy are limited for differential diagnosis. To assist this process for the dentist, this work focuses an automatic analysis of cyst using the texture information. This work involves three sections. The first section is performance analysis of preprocessing for various cysts images. The second section is extracting gray level co-occurrence matrix for all the cyst patterns. Analyzing different cyst pattern using the texture properties is the third section.
Keywords :
dentistry; feature extraction; image texture; matrix algebra; medical image processing; patient diagnosis; patient treatment; ameloblastomas; automatic cyst analysis; cyst patterns; cyst region; cystic lesions; cysts treatment; dental cyst diagnosis; gray level cooccurrence matrix extraction; high recurrence rates; maxillomandibular region; odontogenic keratocysts differentiation; oral cysts; texture analysis; texture information; texture properties; various cysts image preprocessing; Correlation; Dentistry; Educational institutions; Image reconstruction; Image segmentation; Lighting; Teeth; Dental cysts; block analysis; gray level co-occurrence matrix; preprocessing;
Conference_Titel :
Machine Vision and Image Processing (MVIP), 2012 International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4673-2319-2
DOI :
10.1109/MVIP.2012.6428774