Oral Tumor Segmentation and Detection using Clustering and Morphological Process

Oral tumor is one of the most widely recognized tumors growing globally, continuously promoting a high mortality rate. Because early detection and treatment remain effective interventions in improving oral cancer outcomes, developing complementary vision-based technologies that can reveal potential evil high-quality diseases (OPMDs), which carry risk cancer, represent significant opportunities for screening process. This paper proposes morphological algorithm to preserve edge details prominent features dental radiographs. technique, stage identifies using clustering processing. would allow identification these images. Applying pre-processing images leads over-segmentation even though it pre-processed.