An 80 years old, bronchial asthmatic, male was posted for left cataract extraction with intra ocular lens implantation. He was administered peribulbar block/left facial nerve block. There was no sensory or motor block. Thereafter peribulbar block was repeated. Only partial akinesia was achieved, so under intermittent intra venous sedation, the surgery continued for 40 minutes. In the post-operative period, no signs of any residual/delayed block were noted. On specific enquiry, patient gave history of scorpion bite thrice, at the age of 27 years on his right foot, about 8-9 years back and again about 6-7 months back on his right hand. On 4th post-operative day after obtaining informed consent, local infiltration of the skin on the ventral aspect of the forearm, using, 6 mL, 2% lignocaine with adrenaline, was carried out. Confirming the suspicion, there was no sensory block after the injection, confirmed by pin prick method. Peribulbar block produces adequate intra-operative analgesia for cataract extraction. The cause of the failures may be due to technical inability to achieve block. However failure that occurs despite of technically correct injection of the correct drug can be mystifying. As the scorpion venom is known to affect the pumping mechanism of sodium channels in the nerve fibres, which are involved in the mechanism of action of local anaesthetic drugs, it may be responsible for the development of “resistance” to the action of local anaesthetic agents.

This work uses the canopy height model (CHM) based workflow for individual tree crown delineation from LiDAR point cloud data in an urban environment and evaluates its accuracy by using very high-resolution PAN (spatial) and 8-band WorldView-2 imagery. LiDAR point cloud data were used to detect tree features by classifying point elevation values. The workflow includes resampling of LiDAR point cloud to generate a raster surface or digital terrain model, generation of hill-shade image and intensity image, extraction of digital surface model, generation of bare earth digital elevation model and extraction of tree features. Scene dependent extraction criteria were employed to improve the tree feature extraction. LiDAR-based refining/filtering techniques used for bare earth layer extraction were crucial for improving the subsequent tree feature extraction. The PAN-sharpened WV-2 image (with 0.5 m spatial resolution) used to assess the accuracy of LiDAR-based tree features provided an accuracy of 98%. Based on these inferences, we conclude that the LiDAR-based tree feature extraction is a potential application which can be used for understanding vegetation characterization in urban setup.