@article {47260,
	title = {Adaptive and background-aware GAL4 expression enhancement of co-registered confocal microscopy images.},
	journal = {Neuroinformatics},
	volume = {14},
	year = {2016},
	month = {2016 Jan 7},
	pages = {221-33},
	abstract = {<p>GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions.</p>
},
	issn = {1559-0089},
	doi = {10.1007/s12021-015-9289-y},
	author = {Trapp, Martin and Schulze, Florian and Novikov, Alexey A and Tirian, Laszlo and J Dickson, Barry and B{\"u}hler, Katja}
}
@article {6825,
	title = {Structure-based neuron retrieval across Drosophila brains.},
	journal = {Neuroinformatics},
	volume = {12},
	year = {2014},
	month = {2014 Jan 21},
	pages = {423-34},
	abstract = {<p>Comparing local neural structures across large sets of examples is crucial when studying gene functions, and their effect in the Drosophila brain. The current practice of aligning brain volume data to a joint reference frame is based on the neuropil. However, even after alignment neurons exhibit residual location and shape variability that, together with image noise, hamper direct quantitative comparison and retrieval of similar structures on an intensity basis. In this paper, we propose and evaluate an image-based retrieval method for neurons, relying on local appearance, which can cope with spatial variability across the population. For an object of interest marked in a query case, the method ranks cases drawn from a large data set based on local neuron appearance in confocal microscopy data. The approach is based on capturing the orientation of neurons based on structure tensors and expanding this field via Gradient Vector Flow. During retrieval, the algorithm compares fields across cases, and calculates a corresponding ranking of most similar cases with regard to the local structure of interest. Experimental results demonstrate that the similarity measure and ranking mechanisms yield high precision and recall in realistic search scenarios.</p>
},
	issn = {1559-0089},
	doi = {10.1007/s12021-014-9219-4},
	author = {Ganglberger, Florian and Schulze, Florian and Tirian, Laszlo and Novikov, Alexey and Dickson, Barry and B{\"u}hler, Katja and Langs, Georg}
}
