Honeybee-based biohybrid system for landmine detection.

Legacy landmines in post-conflict areas are a non-discriminatory lethal hazard and can still be triggered decades after the conflict has ended. Efforts to detect these explosive devices are expensive, time-consuming, and dangerous to humans and animals involved. While methods such as metal detectors and sniffer dogs have successfully been used in humanitarian demining, more tools are required for both site surveying and accurate mine detection. Honeybees have emerged in recent years as efficient bioaccumulation and biomonitoring animals. The system reported here uses two complementary landmine detection methods: passive sampling and active search. Passive sampling aims to confirm the presence of explosive materials in a mine-suspected area by the analysis of explosive material brought back to the colony on honeybee bodies returning from foraging trips. Analysis is performed by light-emitting chemical sensors detecting explosives thermally desorbed from a preconcentrator strip. The active search is intended to be able to pinpoint the place where individual landmines are most likely to be present. Used together, both methods are anticipated to be useful in an end-to-end process for area surveying, suspected hazardous area reduction, and post-clearing internal and external quality control in humanitarian demining.

Biomonitoring for wide area surveying in landmine detection using honeybees and optical sensing.

Humanitarian demining is a worldwide effort and the range of climates and environments prevent any one detection method being suitable for all sites, so more tools are required for safe and efficient explosives sensing. Landmines emit a chemical flux over time, and honeybees can collect the trace residues of explosives (as particles or as vapour) on their body hairs. This capability was exploited using a passive method allowing the honeybees to freely forage in a mined area, where trace explosives present in the environment stuck to the honeybee body, which were subsequently transferred onto an adsorbent material for analysis by a fluorescent polymer sensor. Potential false positive sources were investigated, namely common bee pheromones, the anti-varroa pesticide Amitraz, and the environment around a clean apiary, and no significant response was found to any from the sensor. The mined site gave a substantial response in the optical sensor films, with quenching efficiencies of up to 38%. A model was adapted to estimate the mass of explosives returned to the colony, which may be useful for estimating the number of mines in a given area.

Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms.

This paper presents a review of recent advances in the development of methods for segmentation of the breast boundary and the pectoral muscle in mammograms. Regardless of improvement of imaging technology, accurate segmentation of the breast boundary and detection of the pectoral muscle are still challenging tasks for image processing algorithms. In this paper, we discuss problems related to mammographic image preprocessing and accurate segmentation. We review specific methods that were commonly used in most of the techniques proposed for segmentation of mammograms and discuss their advantages and disadvantages. Comparative analysis of the methods reported on is made difficult by variations in the datasets and procedures of evaluation used by the authors. We attempt to overcome some of these limitations by trying to compare methods which used the same dataset and have some similarities in approaches to the breast boundary segmentation and detection of the pectoral muscle. In this paper, we will address the most often used methods for segmentation such as thresholding, morphology, region growing, active contours, and wavelet filtering. These methods, or their combinations, are the ones most used in the last decade by the majority of work published in this image processing domain.