Dental caries and the cysts of jaws are frequently occurring pathologies encountered in a dental practice. Imaging of these dental anomalies is done with radiographic examination. Panoramic radiography/ Orthopantomography (OPG) is a common modality to screen patients with an advantage of ease of imaging and reduced exposure to patients. The panoramic images obtained with this equipment are exploited by noise embedded during its acquisition making the detection of this dental caries difficult. Detection and characterization of dental caries and various other maxilla-facial pathologies can be achieved by the application of computer aided image processing algorithms applied on dental panoramic images. This paper presents two distinct image processing algorithms for detection of dental anomalies. The first part of this paper presents a novel approach for detection of dental caries using hybridized negative transformation. The second part of paper presents, statistical texture analysis for the dental images containing cysts along with dental caries. The texture analysis is used when the objects to be segmented based on texture content rather than intensities. The texture of panoramic image is characterized by Gray Level Co-occurrence Matrix (GLCM). The texture features obtained from the GLCM are energy, entropy, homogeneity, contrast and correlation. These texture features can be used to find texture boundaries to obtain segmentation about the region of cysts. Results obtained by both the methods were satisfactory correlating with the diagnosis made by the maxillofacial radiologists.

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