Abstract:
Oral Cancer is the most common form of cancer in India. Poor adult villagers
from remote areas in their 60s and 70s are the usual victims of oral cancer. The
peculiar nature of oral cancer is that it is curable if treated properly at right time.
In India this is particularly deadly due to the extensive use of tobacco coupled with
lack of proper diagnostic facilities. A biopsy done at right time could save many
lives. But it is the ignorance regarding the right time that causes the consequent
la sufferings and unavoidable death sentence. This happens since the symptoms
appear to be very much similar to a normal mouth ulcer and resulting negligence
and complacency. This underscores the need for easier methods to identify this
villain in disguise at an early stage.
Early diagnosis via mass screening initiatives in rural areas with the help of image
classification systems can help to reduce the mortality, medical costs, pain and
sufferings. True color images are the most easily available input to such a system.
The separation of mouth cavity images into cancerous and non-cancerous is a three
stage process involving segmentation (Identification of ROl), feature extraction
and classification. The objectives of this work include:
Study of different methods for segmentation, feature extraction and classification.
Selection of methods suitable for the present purpose.
Application of the selected methods on the database. The test database is
formed by sample images acquired from the Medical College under HIHT,
Dehradun.
Proposing the best system suitable for classification of images into cancerous
and non-cancerous.
