Three 16S rRNA hybridization probes were developed and tested for genus-specific detection of Bifidobacterium species in the human fecal flora. Variable regions V2, V4, and V8 of the 16S rRNA contained sequences unique to this genus and proved applicable as target sites for oligodeoxynucleotide probes. Determination of the genus specificity of the oligonucleotides was performed by whole-cell hybridization with fluorescein isothiocyanate-labelled probes. To this end, cells were fixed on glass slides, hybridized with the probes, and monitored by videomicroscopy. In combination with image analysis, this allowed quantification of the fluorescence per cell and objective evaluation of hybridization experiments. One of the probes developed was used to determine the population of Bifidobacterium spp. in human fecal samples. A comparison was made with results obtained by cultural methods for enumeration. Since both methods gave similar population estimates, it was concluded that all bifidobacteria in feces were culturable. However, since the total culturable counts were only a fraction of the total microscopic counts, the contribution of bifidobacteria to the total intestinal microflora was overestimated by almost 10-fold when cultural methods were used as the sole method for enumeration.

Abstract-In this paper, we describe a multiscale and multishape morphological method for pattern-based analysis and classification of gray-scale images using connected operators. Compared with existing methods, which use structuring elements, our method has three advantages. First, in our method, the time needed for computing pattern spectra does not depend on the number of scales or shapes used, i.e., the computation time is independent of the dimensions of the pattern spectrum. Second, size and strict shape attributes can be computed, which we use for the construction of joint 2D shape-size pattern spectra. Third, our method is significantly less sensitive to noise and is rotation-invariant. Although rotation invariance can also be approximated by methods using structuring elements at different angles, this tends to be computationally intensive. The classification performance of these methods is discussed using four image sets: Brodatz, COIL-20, COIL-100, and diatoms. The new method obtains better or equal classification performance to the best competitor with a 5 to 9-fold speed gain.

Abstract-Morphological attribute filters have not previously been parallelized mainly because they are both global and nonseparable. We propose a parallel algorithm that achieves efficient parallelism for a large class of attribute filters, including attribute openings, closings, thinnings, and thickenings, based on Salembier's Max-Trees and Min-trees. The image or volume is first partitioned in multiple slices. We then compute the Max-trees of each slice using any sequential Max-Tree algorithm. Subsequently, the Max-trees of the slices can be merged to obtain the Max-tree of the image. A C-implementation yielded good speed-ups on both a 16-processor MIPS 14000 parallel machine and a dual-core Opteron-based machine. It is shown that the speed-up of the parallel algorithm is a direct measure of the gain with respect to the sequential algorithm used. Furthermore, the concurrent algorithm shows a speed gain of up to 72 percent on a single-core processor due to reduced cache thrashing.

The implementation of morphological connected set operators for image filtering and pattern recognition is discussed. Two earlier algorithms based on priority queues and hierarchical queues respectively are compared to a more recent union-find approach. Unlike the earlier algorithms which process regional extrema in the image sequentially, the unionfind method allows simultaneous processing of extrema. In the context of area openings, closings and pattern spectra, the union-find algorithm outperforms the previous methods on almost all natural and synthetic images tested. Finally, extensions to pattern spectra and the more general class of attribute operators are presented for all three algorithms, and memory usages are compared.