Cannabis smoking, which has euphoric effects, is consistently increasing in Europe. Smoking cannabis is a rare trigger of acute myocardial infarction (MI) by inducing coronary artery spasm. Some cases who have thrombus formation in acute coronary artery and no serious atherosclerotic lesions have been reported in the literature. These cases had involved the left coronary artery. Although some cases were reported with MI after cannabis smoking, only two case reports with inferior MI after cannabis smoking were reported in the literature. The present report is of a young male patient who was affected by acute inferior MI half an hour after cannabis smoking.
ABSTRACT:Opening new possibilities for research, very high resolution (VHR) imagery acquired by recent commercial satellites and aerial systems requires advanced approaches and techniques that can handle large volume of data with high local variance. Delineation of land use/cover information from VHR images is a hot research topic in remote sensing. In recent years, object-based image analysis (OBIA) has become a popular solution for image analysis tasks as it considers shape, texture and content information associated with the image objects. The most important stage of OBIA is the image segmentation process applied prior to classification. Determination of optimal segmentation parameters is of crucial importance for the performance of the selected classifier. In this study, effectiveness and applicability of the segmentation method in relation to its parameters was analysed using two VHR images, an aerial photo and a Quickbird-2 image. Multi-resolution segmentation technique was employed with its optimal parameters of scale, shape and compactness that were defined after an extensive trail process on the data sets. Nearest neighbour classifier was applied on the segmented images, and then the accuracy assessment was applied. Results show that segmentation parameters have a direct effect on the classification accuracy, and low values of scale-shape combinations produce the highest classification accuracies. Also, compactness parameter was found to be having minimal effect on the construction of image objects, hence it can be set to a constant value in image classification.
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