8 High-Throughput Detection of Linear Features: Selected Applications...

189

Table 8.3 Features developed specifically to characterize linear structures in the astrocyte cytoskeleton

lineNo	Number of lines detected within cell
lineMean	Mean brightness of lines within cell
lineLength	Length of lines within cell
lineDensity	Density of lines within cell
lineAngle	Mean orientation angle of lines Within cell
lineAngleVar	Variance of orientation angle of lines within cell
lineWidthMean	Mean width of lines within cell
lineWidthMedian	Median width of lines within cell
lineStar	“Starriness” measure based on how much linear structure is
	removed by an opening of a specific radius
lineWeb	“Webbiness” measure based on how much the gaps between
	lines are filled in by a closing of specific radius

linear structures within a cell are generally aligned in a particular direction, whereas a high value indicates that the linear structures are randomly oriented. In addition, the lineDensity measure (the density of these “lines” within cells) also decreased (to 44 ±5% of control, p < 0.05), demonstrating a decrease in the cellular area labelled by GFAP.

8.5.3Separating Adjacent Bacteria Under Phase Contrast Microscopy

In bacterial cultures, cells tend to come in very close proximity to each other, such that the contrast between individual cells is sometimes minute (see Fig. 8.11). This makes bacterial counting and measurement of size and shape difficult. We have found that our linear feature detector was capable of detecting the very weak linear features that separate adjacent bacteria (see Fig. 8.11). By combining this tool with a standard Canny edge detector, a system was put together that permitted counting and segmenting bacteria with over 97% reliability [18]. We expect this new capability to be generally useful for microbial studies and we are planning to extend this work to allow tracking of bacterial cells with a comparable reliability. This work is motivated by the need to understand how bacterial films are able to spread quickly over tissue surfaces and thus cause infections.

8.6 Perspectives and Conclusions

Our approach to linear feature is both conceptually simple and very fast. It is fairly general – independent of any model assumption about linear features. This also means that the approach is not recommended for very noisy images. In the

190	L. Domanski et al.

rare instances where noise was an issue, we found that it was often possible to use a preliminary processing step to increase the contrast of linear features, for example, using the technique of anisotropic filtering [19]. Another promising tool to preprocess linear feature images in this manner is afforded by so-called flexible path openings, which aim to identify paths along which the intensity remains high on average [20].

There are also instances where the contrast mechanism of the optical instrument makes the analysis difficult. This is the case with Differential Interference Contrast (DIC) microscopy. For such images, the Hilbert transform can be used to create images that are suitable for analysis.

In the case of very noisy images, the potential of more global methods, such as “shortest paths” [7], or even “linear paths” [9], has been explored. While useful, these more complex approaches may also represent warnings that the experimenter should go back to the bench to produce better data. With the availability of EMMCD camera and the availability of bright and photostable dyes, such as the AlexaTM dyes, this is often the best course of action.

This is an exciting time for image analysis, with a growing number of applications that can be automated. The samples presented in this chapter only touch the surface, as illustrated by the content of other exciting chapters in this book.

Acknowledgements The authors would like to thank the following people for allowing us to use their images as sample images: Marjo Gotte,¨ Novartis Institutes for BioMedical Research; Dr. Myles Fennell, Wyeth Research, Princeton, NJ, USA; Dr. Xiaokui Zhang, Helicon Therapeutics, Inc., USA; Prof. Pat Doherty, Kings College, London, UK; Dr. Jenny Gunnersen, Prof. Seong-Seng Tan, and Dr. Ross O’Shea Howard Florey Institute, Melbourne; Ass. Prof. Cynthia Whitchurch, UTS, Sydney.

References

1.Palmer, S.E.: Vision science. MIT, MA (1999)

2.Sun, C., Vallotton, P.: Fast linear feature detection using multiple directional non-maximum suppression. J. Microsc. 234, 147–57 (2009)

3.Kirbas, C., Quek, F.: A review of vessel extraction techniques and algorithms. ACM Comput. Surv. 36, 81–121 (2004)

4.Meijering, E.: Neuron tracing in perspective. Cytometry 77, 693–704 (2010)

5.Neubeck, A., Van Gool, L.: Efficient non-maximum suppression. In: Tang, Y.Y., Wang, S.P., Lorette, G., Yeung, D.S., Yan, H. (eds.) Proceedings of 18th International Conference on Pattern Recognition, vol. 3, pp. 850–855. IEEE Computer Soc, Los Alamitos (2006)

6.Soille, P.: Morphological image analysis, 2nd edn. Springer, Heidelberg (2004)

7.Sun, C., Pallottino, S.: Circular shortest path in images. Pattern Recogn. 36, 709–719 (2003)

8.Dorst, L., Smeulders, A.W.M.: Length estimators for digitized contours. Comput. Vis. Graph. Image Process. 40, 311–333 (1987)

9.Lagerstrom, R., Sun, C., Vallotton, P.: Boundary extraction of linear features using dual paths through gradient profiles. Pattern Recogn. Lett. 29, 1753–1757 (2008)

10.Owens, J.D., Luebke, D., Govindaraju, N., Harris, M., Kruger, J., Lefohn, A.E., Purcell, T.J.: A survey of general-purpose computation on graphics hardware. Comput. Graph. Forum 26, 80–113 (2007)

11.Harris, M.: Optimizing parallel reduction in CUDA. NVIDIA SDK white paper (2007)

8 High-Throughput Detection of Linear Features: Selected Applications...

191

12.Hakura, Z.S., Gupta, A.: The design and analysis of a cache architecture for texture mapping. In: 24th Annual International Symposium on Computer Architecture, Conference Proceedings, pp. 108–120. Assoc Computing Machinery, New York (1997)

13.Cox, M., Bhandari, N., Shantz, M.. Multi-level texture caching for 3D graphics hardware. In: Proceedings of the 25th Annual International Symposium on Computer Architecture. IEEE Computer Soc, Los Alamitos, pp. 86–97 (1998)

14.Domanski, L.: Linear feature detection on GPUs. In: International Conference on Digital Image Computing: Techniques and Applications (DICTA), Sydney (2010)

15.Vallotton, P., Lagerstrom, R., Sun, C., Buckley, M., Wang, D., De Silva, M., Tan, S.S., Gunnersen, J.M.: Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision. Cytometry A 71, 889–895 (2007)

16.Conrad, C., Gerlich, D.W.: Automated microscopy for high-content RNAi screening. J. Cell Biol. 188, 453–461 (2010)

17.Lau, C., O’Shea, R., Broberg, B., Bischof, L., Beart, P.: The Rho kinase inhibitor Fasudil up-regulates astrocytic glutamate transport subsequent to actin remodelling in murine cultured astrocytes. https://nsw.owa.csiro.au/pubmed/21309758. Br. J. Pharmacol. 163, 533–545 (2011)

18.Vallotton, P., Sun, C., Wang, D., Ranganathan, P., Turnbull, L. Whitchurch, C.: Segmentation and tracking of individual Pseudomonas aeruginosa bacteria in dense populations of motile cells. In: Image and Vision Computing New Zealand Wellington, New Zealand (2009)

19.Orkisz, M.M., Bresson, C., Magnin, I.E., Champin, O., Douek, P.C.: Improved vessel visualization in MR angiography by nonlinear anisotropic filtering. Magn. Reson. Med. 37, 914–919 (1997)

20.Heijmans, H., Buckley, M., Talbot, H.: Path-based morphological openings. In: ICIP: 2004 International Conference on Image Processing, vol. 1–5, pp. 3085–3088. IEEE, New York (2004)