Reefs and islands of the Chagos Archipelago, Indian Ocean: why it is the world's largest no-take marine protected area.

The Chagos Archipelago was designated a no-take marine protected area (MPA) in 2010; it covers 550 000 km2, with more than 60 000 km2 shallow limestone platform and reefs. This has doubled the global cover of such MPAs.It contains 25-50% of the Indian Ocean reef area remaining in excellent condition, as well as the world's largest contiguous undamaged reef area. It has suffered from warming episodes, but after the most severe mortality event of 1998, coral cover was restored after 10 years.Coral reef fishes are orders of magnitude more abundant than in other Indian Ocean locations, regardless of whether the latter are fished or protected.Coral diseases are extremely low, and no invasive marine species are known.Genetically, Chagos marine species are part of the Western Indian Ocean, and Chagos serves as a 'stepping-stone' in the ocean.The no-take MPA extends to the 200 nm boundary, and. includes 86 unfished seamounts and 243 deep knolls as well as encompassing important pelagic species.On the larger islands, native plants, coconut crabs, bird and turtle colonies were largely destroyed in plantation times, but several smaller islands are in relatively undamaged state.There are now 10 'important bird areas', coconut crab density is high and numbers of green and hawksbill turtles are recovering.Diego Garcia atoll contains a military facility; this atoll contains one Ramsar site and several 'strict nature reserves'. Pollutant monitoring shows it to be the least polluted inhabited atoll in the world. Today, strict environmental regulations are enforced.Shoreline erosion is significant in many places. Its economic cost in the inhabited part of Diego Garcia is very high, but all islands are vulnerable.Chagos is ideally situated for several monitoring programmes, and use is increasingly being made of the archipelago for this purpose.

Design and evaluation of an external filter technique for exposure equalization in mammography.

We are developing an external filter method for equalizing x-ray exposure in the peripheral region of the breast. This method requires the use of only a limited number of custom-built filters for different breast shapes in a given view. This paper describes the design methodology for these external filters. The filter effectiveness was evaluated through a simulation study on 171 mediolateral and 196 craniocaudal view digitized mammograms and through imaging of a breast phantom. The degree of match between the simulated filter and the individual 3-D exposure profiles at the breast periphery was quantified. An analysis was performed to investigate the effect of filter misalignment. The simulation study indicates that the filter is effective in equalizing exposures for more than 80% of the breast images in our database. The tolerance in filter misalignment was estimated to be about +/- 2 mm for the CC view and +/- 1 mm for the MLO view at the image plane. Some misalignment artifacts were demonstrated with simulated filtered mammograms.

Classification of compressed breast shapes for the design of equalization filters in x-ray mammography.

We are developing an external filter method for equalizing the x-ray exposure in mammography. Each filter is specially designed to match the shape of the compressed breast border and to preferentially attenuate the x-ray beam in the peripheral region of the breast. To be practical, this method should require the use of only a limited number of custom built filters. It is hypothesized that this would be possible if compressed breasts can be classified into a finite number of shapes. A study was performed to determine the number of shapes. Based on the parabolic appearance of the outer borders of compressed breasts in mammograms, the borders were fit with the polynomial equations y = ax2 + bx3 and y = ax2 + bx3 + cx4. The goodness-of-fit of these equations was compared. The a,b and a,b,c coefficients were employed in a K-Means clustering procedure to classify 470 CC-view and 484 MLO-view borders into 2-10 clusters. The mean coefficients of the borders within a given cluster defined the "filter" shape, and the individual borders were translated and rotated to best match that filter shape. The average rms differences between the individual borders and the "filter" were computed as were the standard deviations of those differences. The optimally shifted and rotated borders were refit with the above polynomial equations, and plotted for visual evaluation of clustering success. Both polynomial fits were adequate with rms errors of about 2 mm for the 2-coefficient equation, and about 1 mm for the 3-coefficient equation. Although the fits to the original borders were superior for the 3-coefficient equation, the matches to the "filter" borders determined by clustering were not significantly improved. A variety of modified clustering methods were developed and utilized, but none produced major improvements in clustering. Results indicate that 3 or 4 filter shapes may be adequate for each mammographic projection (CC- and MLO-view). To account for the wide variations in exposures observed at the peripheral regions of breasts classified to be of a particular shape, it may be necessary to employ different filters for thin, medium and thick breasts. Even with this added requirement, it should be possible to use a small number of filters as desired.