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Megaplatypus mutatus is an ambrosia beetle that attacks several species oftrees by making galleries in the trunks where its larvae and associated fungidevelop. This damage spoils the wood for commercial use and cause stem breakagein front of strong winds. Due to the insect's cryptic lifestyle, galleryanalyses have usually been studied by destructive methods. However, they alterthe homeostasis of the insect-fungi interaction, modifies the topology of thegallery and, more importantly, does not reveal the 100% complex structure madeby the insect. Therefore, a novel way to study this structure is by imaging thegalleries by means of computerized axial tomography (CT). This method allowsobtaining a three-dimensional representation of the gallery and the pupalchambers to be studied, while the wood and insect sample is not disturbed andgenerates a high amount of data. The isolation of the galleries and pupalchambers from the CT background images is not simple, because there is notenough contrast between the grey levels of the galleries and the marksgenerated by internal components of the trunk itself. In this paper, we presenta robust algorithm that allows automating the isolation of the galleries andpupal chambers from CT trunk images which can be used in a broad spectrum ofimage analysis.

A novel set of algorithms to recognize galleries of ambrosia beetle in computerized axial tomography of trees trunks