Ghost cell odontogenic carcinoma (GCOC) is a malignant odontogenic epithelial tumor which is an exceedingly rare, highly aggressive, rapidly growing, and infiltrative tumor forming the malignant counterpart of long-standing benign cystic lesions coming in the spectrum of calcifying odontogenic cysts. To date, only a few cases have been reported in the medical literature. A case of unusual presentation of GCOC is presented and the clinical, histopathological, and immunohistochemical features are discussed along with a literature review. Our case report further emphasizes the bizarre biological behavior of this tumor and the need for strict long-term surveillance of the patients as metastasis to distant sites has been reported.

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This paper proposes a classification technique using fuzzy inference system (FIS) for non-proliferative diabetic retinopathy (NPDR). The input variables to FIS are the extracted features from the pathologies of NPDR. Abnormalities like exudates and microaneuryms are segmented for feature extraction. The pathological aspects of NPDR leads to fuzzy if-then rules that effectively handles the fuzziness present in some of the features. The role of FIS in NPDR classification replaces the training phase in learning based classification methods. The performance of the proposed fuzzy approach is analyzed for stages of NPDR and diabetic retinopathy classification on various databases. The accuracy of 98.2% is observed for NPDR classification in Messidor database.