Bis(dioxaborine) dyes of the A-π-A format (A: acceptor, π: conjugated bridge) were prepared and photophysically characterized. The best performing dyes feature (a) visible-light absorption (>400 nm), (b) high molar absorption coefficients (up to 70000 m cm ), (c) Stokes shifts in the range of ca. 2500-5800 cm , and (d) strong fluorescence emission with quantum yields of up to 0.74. This yields very bright-emitting dyes for one-photon excitation. However, the most intriguing feature of the dyes is their strong two-photon absorption. This was achieved by means of increased π-conjugation in the phenylene or phenylene-thiophene bridges through the variation of the conjugation length and rigidity. This provided two-photon absorption cross sections of up to 2800 GM (1 Goeppert-Mayer (GM)=10 cm s photon ). Considering the mentioned high fluorescence quantum yields, exceptionally bright-emitting A-π-A two-photon absorbing dyes with low molecular mass are obtained. Time-dependent density-functional theory calculations corroborated the experimental results.

In biology, cell counting is a primary measurement and it is usually performed manually using hemocytometers such as Malassez blades. This work is tedious and can be automated using image processing. An algorithm based on Fourier transform filtering and the Hough transform was developed for Malassez blade grid extraction. This facilitates cell segmentation and counting within the grid. For the present work, a set of 137 images with high variability was processed. Grids were accurately detected in 98% of these images.

In biology, hemocytometers such as Malassez slides are widely used and are effective tools for counting cells manually. In a previous work, a robust algorithm was developed for grid extraction in Malassez slide images. This algorithm was evaluated on a set of 135 images and grids were accurately detected in most cases, but there remained failures for the most difficult images. In this work, we present an optimization of this algorithm that allows for 100% grid detection and a 25% improvement in grid positioning accuracy. These improvements make the algorithm fully reliable for grid detection. This optimization also allows complete erasing of the grid without altering the cells, which eases their segmentation.

We present a computational method for pseudo-circular object detection and quantitative characterization in digital images, using the gradient accumulation matrix as a basic tool. This Gradient Accumulation Transform (GAT) was first introduced in 1992 by Kierkegaard and recently used by Kaytanli & Valentine. In the present article, we modify the approach by using the phase coding studied by Cicconet, and by adding a "local contributor list" (LCL) as well as a "used contributor matrix" (UCM), which allow for accurate peak detection and exploitation. These changes help make the GAT algorithm a robust and precise method to automatically detect pseudo-circular objects in a microscopic image. We then present an application of the method to cell counting in microbiological images.