Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
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Conf Proc IEEE Eng Med Biol Soc · Jul 2017
Characterization of dental pathologies using digital panoramic X-ray images based on texture analysis.
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 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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Conf Proc IEEE Eng Med Biol Soc · Jul 2017
Ex vivo animal-model assessment of a non-invasive system for loss of resistance detection during epidural blockade.
During recent decades epidural analgesia has gained widespread recognition in many applications. In this complex procedure, anaesthetist uses a specific needle to inject anesthetic into the epidural space. It is crucial the appropriate insertion of the needle through inhomogeneous tissues placed between the skin and the epidural space to minimize anesthetic-related complications (e.g., nausea, headache, and dural puncture). ⋯ The system reached the saturation condition during the needle insertion; this feature is critical to avoid false positive during the procedure. However, it was not easy to detect the entrance within the epidural space due to its small volume in the animal model. Lastly, the practitioner found real the model, and performed the procedures in a conventional manner because the system did not influence his actions.
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Conf Proc IEEE Eng Med Biol Soc · Jul 2017
Comparative StudyComparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.
Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. ⋯ The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.