Ameloblastic Carcinoma in a 2-Year-Old Child: A Case Report and Review of the Literature.

Ameloblastic carcinoma (AC) is a rare malignant odontogenic tumor in pediatric patients, only 22 cases have been reported in literature since 1932. We present an extremely rare case in which AC occurred in a 2-year-old girl, who had a tumor in the right mandible. Radiographic findings showed a multilocular, poorly defined, and mixed radiolucent-radiopaque lesion in the region of teeth #84 to #85, with bone and tooth root resorption. Computed tomography revealed buccal cortex destruction, tumor infiltration of soft tissue, and enlarged nodes. Incisional biopsy showed histomorphological features of AC. Immunohistochemical analysis exhibited a positive result for Cytokeratin (CK) 19 and overexpression of p53 and Ki67. The patient underwent right hemimandibulectomy and neck dissection. The final pathology was consistent with the initial diagnosis of AC. The patient did not exhibit signs of recurrence or metastasis within 2 years postoperatively. Given the rarity of this disease and the age of the patient, this report constitutes a valuable contribution to the current literature.

Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification.

No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries.

Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection.

Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.