Brain extraction using the watershed transform from markers

Isolation of the brain from other tissue types in magnetic resonance (MR) images is an important step in many types of neuro-imaging research using both humans and animal subjects. The importance of brain extraction is well appreciated—numerous approaches have been published and the benefits of good extraction methods to subsequent processing are well known. We describe a tool—the marker based watershed scalper (MBWSS)—for isolating the brain in T1-weighted MR images built using filtering and segmentation components from the Insight Toolkit (ITK) framework. The key elements of MBWSS—the watershed transform from markers and aggressive filtering with large kernels—are techniques that have rarely been used in neuroimaging segmentation applications. MBWSS is able to reliably isolate the brain without expensive preprocessing steps, such as registration to an atlas, and is therefore useful as the first stage of processing pipelines. It is an informative example of the level of accuracy achievable without using priors in the form of atlases, shape models or libraries of examples. We validate the MBWSS using a publicly available dataset, a paediatric cohort, an adolescent cohort, intra-surgical scans and demonstrate flexibility of the approach by modifying the method to extract macaque brains

Report on shape analysis and matching and on semantic matching

In GRAVITATE, two disparate specialities will come together in one working platform for the archaeologist: the fields of shape analysis, and of metadata search. These fields are relatively disjoint at the moment, and the research and development challenge of GRAVITATE is precisely to merge them for our chosen tasks. As shown in chapter 7 the small amount of literature that already attempts join 3D geometry and semantics is not related to the cultural heritage domain. Therefore, after the project is done, there should be a clear ‘before-GRAVITATE’ and ‘after-GRAVITATE’ split in how these two aspects of a cultural heritage artefact are treated.This state of the art report (SOTA) is ‘before-GRAVITATE’. Shape analysis and metadata description are described separately, as currently in the literature and we end the report with common recommendations in chapter 8 on possible or plausible cross-connections that suggest themselves. These considerations will be refined for the Roadmap for Research deliverable.Within the project, a jargon is developing in which ‘geometry’ stands for the physical properties of an artefact (not only its shape, but also its colour and material) and ‘metadata’ is used as a general shorthand for the semantic description of the provenance, location, ownership, classification, use etc. of the artefact. As we proceed in the project, we will find a need to refine those broad divisions, and find intermediate classes (such as a semantic description of certain colour patterns), but for now the terminology is convenient – not least because it highlights the interesting area where both aspects meet.On the ‘geometry’ side, the GRAVITATE partners are UVA, Technion, CNR/IMATI; on the metadata side, IT Innovation, British Museum and Cyprus Institute; the latter two of course also playing the role of internal users, and representatives of the Cultural Heritage (CH) data and target user’s group. CNR/IMATI’s experience in shape analysis and similarity will be an important bridge between the two worlds for geometry and metadata. The authorship and styles of this SOTA reflect these specialisms: the first part (chapters 3 and 4) purely by the geometry partners (mostly IMATI and UVA), the second part (chapters 5 and 6) by the metadata partners, especially IT Innovation while the joint overview on 3D geometry and semantics is mainly by IT Innovation and IMATI. The common section on Perspectives was written with the contribution of all

Human-Centered Content-Based Image Retrieval

Retrieval of images that lack a (suitable) annotations cannot be achieved through (traditional) Information Retrieval (IR) techniques. Access through such collections can be achieved through the application of computer vision techniques on the IR problem, which is baptized Content-Based Image Retrieval (CBIR). In contrast with most purely technological approaches, the thesis Human-Centered Content-Based Image Retrieval approaches the problem from a human/user centered perspective. Psychophysical experiments were conducted in which people were asked to categorize colors. The data gathered from these experiments was fed to a Fast Exact Euclidean Distance (FEED) transform (Schouten & Van den Broek, 2004), which enabled the segmentation of color space based on human perception (Van den Broek et al., 2008). This unique color space segementation was exploited for texture analysis and image segmentation, and subsequently for full-featured CBIR. In addition, a unique CBIR-benchmark was developed (Van den Broek et al., 2004, 2005). This benchmark was used to explore what and how several parameters (e.g., color and distance measures) of the CBIR process influence retrieval results. In contrast with other research, users judgements were assigned as metric. The online IR and CBIR system Multimedia for Art Retrieval (M4ART) (URL: http://www.m4art.org) has been (partly) founded on the techniques discussed in this thesis. References: - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2004). The utilization of human color categorization for content-based image retrieval. Proceedings of SPIE (Human Vision and Electronic Imaging), 5292, 351-362. [see also Chapter 7] - Broek, E.L. van den, Kisters, P.M.F., and Vuurpijl, L.G. (2005). Content-Based Image Retrieval Benchmarking: Utilizing Color Categories and Color Distributions. Journal of Imaging Science and Technology, 49(3), 293-301. [see also Chapter 8] - Broek, E.L. van den, Schouten, Th.E., and Kisters, P.M.F. (2008). Modeling Human Color Categorization. Pattern Recognition Letters, 29(8), 1136-1144. [see also Chapter 5] - Schouten, Th.E. and Broek, E.L. van den (2004). Fast Exact Euclidean Distance (FEED) transformation. In J. Kittler, M. Petrou, and M. Nixon (Eds.), Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR 2004), Vol 3, p. 594-597. August 23-26, Cambridge - United Kingdom. [see also Appendix C

From Extrema Relationships To Image Simplification Using Non-flat Structuring Functions

Image simplification plays a fundamental role in Image Processing to improve results in complex tasks such as segmentation. The field of Mathematical Morphology (MM) itself has established many ways to perform such improvements. In this paper, we present a new approach for image simplification which takes into account erosion and dilation from MM. The proposed method is not self-dual and only single-band signals under a discrete domain are considered. Our main focus is on the creation of concave structuring functions based on a relation between signal extrema. This relation is given by two extrema according to their degree of separation (distance) and the respective heights (contrast). From these features, a total order relation is produced, thus supplying a way to progressively simplify the signal. Some two-dimensional images are considered here to illustrate in practice this simplification behavior. © 2013 Springer-Verlag.7883 LNCS377389Bertrand, G., On the Dynamics (2007) Image Vision Comput., 25 (4), pp. 447-454Beucher, S., Meyer, F., The Morphological Approach to Segmentation: The Watershed Transformation (1993) Mathematical Morphology in Image Processing, , Marcel DekkerVan Den Boomgaard, R., Dorst, L., Makram-Ebeid, S., Schavemaker, J.G.M., Quadratic Structuring Functions in Mathematical Morphology (1996) Mathematical Morphology and Its Applications to Image and Signal Processing, , KluwerDorini, L.B., Leite, N.J., Multiscale Morphological Image Simplification (2008) LNCS, 5197, pp. 413-420. , Ruiz-Shulcloper, J., Kropatsch, W.G. (eds.) CIARP 2008. Springer, HeidelbergGrimaud, M., A New Measure of Contrast: The Dynamics (1992) Proc. SPIE, Image Algebra and Morphological Image Processing III, 1769, pp. 292-305Hagyard, D., Razaz, M., Atkin, P., Analysis of Watershed Algorithms for Greyscale Images (1996) ICIP, 3, pp. 41-44Jackway, P.T., Deriche, M., Scale-Space Properties of the Multiscale Morphological Dilation-Erosion (1996) IEEE Trans. Pattern Anal. Mach. Intell., 18 (1), pp. 38-51Lindeberg, T., Ter Haar Romeny, B.M., Linear Scale-Space: Basic Theory (1994) Geometry-Driven Diffusion in Computer Vision, , KluwerMeyer, F., Levelings, Image Simplification Filter for Segmentation (2004) J. Math. Imaging Vision, pp. 59-72Meyer, F., Serra, J., Contrasts and Activity Lattice (1989) Signal Process, 16 (4), pp. 303-317Pitas, I., Venetsanopoulos, A.N., Order Statistics in Digital Image Processing (1992) P. IEEE, 80 (12), pp. 1893-1921Serra, J., (1982) Image Analysis and Mathematical Morphology, 1. , Academic PressSerra, J., Salembier, P., Connected Operators and Pyramids (1993) Proc. SPIE, Image Algebra and Morphological Image Processing IV, 2030, pp. 65-76Silva, A.G., Lotufo, R.A., Efficient Computation of New Extinction Values from Extended Component Tree (2011) Pattern Recogn. Lett., 32 (1), pp. 79-90Vachier, C., Vincent, L., Valuation of Image Extrema Using Alternating Filters by Reconstruction (1995) Neural, Morphological, and Stochastic Methods in Image and Signal Processing, pp. 94-103Wilson, S.S., Vector Morphology and Iconic Neural Networks (1989) IEEE Trans. Syst. Man Cybern., 19 (6), pp. 1636-164