Kiwanuka, Fred N.

Wilkinson, Michael H.F.

English

Morphological attribute filters modify images based on properties or attributes of connected components. Usually, attribute filtering is based on a scalar property which has relatively little discriminating power. Vector-attribute filtering allow better description of characteristic features for 2D images. In this paper, we extend vector attribute filtering by incorporating unsupervised pattern recognition, where connected components are clustered based on the similarity of feature vectors. We show that the performance of these new filters is better than those of scalar attribute filters in enhancement of objects in medical volumes

ARTS repository - University of Groningen

   University of GroningenCluster-Based Vector-Attribute Filtering for CT and MRI EnhancementKiwanuka, Fred N.; Wilkinson, Michael H.F.Published in:Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite fromit. Please check the document version below.Document VersionPublisher's PDF, also known as Version of recordPublication date:2012Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Kiwanuka, F. N., & Wilkinson, M. H. F. (2012). Cluster-Based Vector-Attribute Filtering for CT and MRIEnhancement. In Proceedings of the 21st International Conference on Pattern Recognition, ICPR 2012 (pp.3112-3115). IEEE (The Institute of Electrical and Electronics Engineers).CopyrightOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of theauthor(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license.More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne-amendment.Take-down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediatelyand investigate your claim.Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons thenumber of authors shown on this cover page is limited to 10 maximum.Download date: 08-06-2022Cluster-Based Vector-Attribute Filtering for CT and MRI EnhancementFred N. Kiwanuka, Michael H.F. WilkinsonJohann Bernoulli Institute for Mathematics and Computer Science,University of Groningen, The Netherlands(F.N.Kiwanuka),(m.h.f.wilkinson)@rug.nlAbstractMorphological attribute filters modify images basedon properties or attributes of connected components.Usually, attribute filtering is based on a scalar propertywhich has relatively little discriminating power. Vector-attribute filtering allow better description of character-istic features for 2D images. In this paper, we extendvector attribute filtering by incorporating unsupervisedpattern recognition, where connected components areclustered based on the similarity of feature vectors. Weshow that the performance of these new filters is betterthan those of scalar attribute filters in enhancement ofobjects in medical volumes.1. IntroductionThe main aim of connected filters [8, 9] is to extractparticular image features while preserving as much ofthe contour information as possible. These operators actby merging flat zones given some criteria, and filter animage without introducing new contours. A sub-classof connected filters are attribute filters [1, 7]. They al-low filtering based on the properties or attributes of con-nected components in the image. Usually, the attributeused is a scalar value describing either size or shapeproperties of connected components. This works well ifthe desired structures can be separated easily from un-desired structures [2, 3, 6, 11]. However, in many cases,a single scalar has low discriminating power.Recently, vector-attribute filters where proposed [5,10]. These replace the single attribute with an attributevector, which is a feature vector describing each con-nected component. This allows a better discriminationof different classes of objects. These filters are based ondissimilarity measure such as Euclidean distance, com-ponents that are similar to a set of reference shapes canbe preserved or removed. This as been applied when apriori knowledge of a suitable reference shape is known.However, in 3D medical imaging a priori knowledge oftarget object shapes is not readily available.In this research, we develop 3D vector-attribute fil-ters which in do not rely on reference shapes. We adaptunsupervised pattern recognition using k-means whereobject classes are learned based on the similarity of pat-terns. In the following, vector-attribute filtering are de-scribed in Section 2. In Section 3, the implementationis described briefly. Section 4 has the performance eval-uation of vector attribute filters for medical images en-hancement.The conclusions are in Section 5.2. Attribute FiltersIn binary attribute filters [1], connected componentsare extracted from the image. Once extracted, criteriaare applied, which decide to accept or to reject compo-nents based on some property of the component, whichis referred to as the attribute. The attribute criterion inmost cases has the form:Λ(C) = (Attr(C) ≥ λ) (1)with Attr(C) some real valued attribute of componentC, and λ the attribute threshold. Examples in 3D arevolume, non-compactness, elongation, etc. [11]. Afterextracting the connected components using connectivityopenings, a trivial filter ψΛ, based on Λ is applied toeach, to determine which components are retained, andwhich to be removed. These are defined asψΛ(C) ={C if Λ(C)is true∅ otherwise(2)The complete attribute filter ψΛ based on criterion Λ isdefined asψΛ(X) =⋃x∈XψΛ(Γx(X)) (3)in which Γx is the connectivity opening, which returnsthe connected component containing point x. This is21st International Conference on Pattern Recognition (ICPR 2012)November 11-15, 2012. Tsukuba, Japan978-4-9906441-0-9 ©2012 ICPR 3112equivalent to taking the union of all connected compo-nents which meet criterion Λ.The simplest way to extend these filters to gray scaleis through threshold decomposition. Conceptually, theprinciple works by thresholding the image at all possi-ble levels then applying the binary filter to each level,and finally stacking the results. The threshold set Th atlevel h can be defined as:Th(f) = {x ∈ E|f(x) ≥ h}. (4)The gray scale variant ψΛ for any increasing binary fil-ter ψΛ is defined asψΛ(f)(x) = sup{h|x ∈ ΨΛ(Th(f))} (5)2.1. Vector attribute FilteringUrbach et al. [10] replaced the single attribute by afeature vector of dimensionality d. Rather than settingd thresholds, they based the criterion on dissimilarity toa reference vector ~r, ideally obtained from some refer-ence shape, e.g., letters from a known font [10]. Naegeland Passat [5] use Mahalanobis distance from the meanof attribute vectors of reference shapes in 2D. In 3D,obtaining reference shapes is much harder.Here we follow a different approach. Ideally, at-tribute vectors of different categories of objects shouldoccupy compact and disjoint regions in d-dimensionalattribute space. Using clustering we automatically or-ganise the huge number of connected components of allthreshold sets into a much smaller number of groups.Instead of setting reference shape and correct distancethreshold, the user now inspects a few clusters.Let C = {C1, C2, . . . , CN}, be set of connected com-ponents of image X where ~τ(Ci) ∈ Rd denotes theassociated attribute vector. As in [10] ~τ is the vec-tor attribute function. Any crisp clustering partitionsRd into k disjoint sets. Partition classes are denotedas Pj ⊂ Rd, j = 1, 2, . . . , k. For a vector function~τ : C → Pj every Ci ∈ C lies in exactly one partitionclass. The cluster criterion Λj becomesΛj(C) = (~τ(C) ∈ Pj) (6)i.e. it returns true if the attribute vector of C lies inpartition Pj . Inserting this into (2), we can draw up thecluster-based vector-attribute filter ψΛj asψΛj (X) =⋃x∈XψΛj (Γx(X)). (7)It is trivial to show this adheres to all the properties ofvector-attribute filters.Though any clustering method could be used, wechose k-means clustering [4] using L2 distance as thesimilarity measure. This is a method of cluster analysiswhich aims to partition N observations into k clustersin which each observation belongs to the cluster withthe nearest mean. The main advantage of k-means isthat its simple and fast(O(Nkd)) which allows it to runon large datasets.3. ImplementationWe implemented vector attribute-filtering in theC/C++ MTdemo package [11]. This uses the Max-Tree [7] data structure to compute and visualize volu-metric data. The Max-Tree is used here due to its ef-ficient attribute computation and filtering process for3D volumes. The filtering process is separated intofour stages: construction, attribute computation, filter-ing and restitution. After building the tree, this auxil-iary data is collected for computing the node attributes.Filtering is implemented by checking whether a node,Ckh , satisfies a given criteria in conjunction with the fil-tering rules [1, 5, 7, 10]. The only changes needed arereplacing scalar attributes with vector attributes, simplyby calling the individual scalar attribute functions sev-eral times, and replacing the filter criterion from simplethresholding to the form of (6).4. Results and DiscussionWe ran tests on a number of 3D medical volumes ofdifferent modalities. To evaluate the performance of thethe different attributes in correctly clustering the differ-ent data sets various combinations of the attributes werecarried out. Determining the number of clusters was adistinct problem and this was interactively determinedby the user. The k that resulted in the best noise sup-pression and region of interest enhancement was used.This is not easy, due to the lack of ground truth. In fu-ture work we will look into automating this.Due to space limitations not all clusters returnedare shown, but rather those clusters that satisfy thefiltering objective of the the data set. The imagesangiolarge, foot, Chest are courtesy of http://www.volvis.org while prostate-stone isa 3D CT data set courtesy of the Department of Radi-ology and Medical Imaging, University General Hospi-tal of Alexandroupolis, Greece. We compare the newmethod to scalar, threshold based attribute filtering, be-cause no reference vectors could easily be drawn up toimplement the form of [10].Angiolarge: The aim is to filter and enhance bloodvessels while suppressing unwanted tissue represent-3113ing noise. Scalar-attribute filtering for 3D medical im-ages based on size attributes perform very poorly, notonly failing to enhance blood vessels but also ampli-fying noise, shown in Fig. 1 (c). A volume attribute(λ = 9000) simply amplifies noise on this data set. Thisapplies for all size attributes. However, when a cluster-ing attribute filter is applied on the volume attribute, theperformance is much better, as seen in Fig. 1(e). Theperformance of the other size attributes is also improvedby using vector attributes. Its important to note that theperformance improvement is irrespective of the increasein the number of attributes used. Shape based attributeslike non-compactness always perform well on this dataset even when used in scalar-attribute filters.Prostate-stone: We want to filter out the prostatestone while suppressing all other features. Scalar-attribute filters are able to isolate the stone but they arenever successful in suppressing the noise, see Fig. 1(d).The problem has been eradicated in [2] [3] by filteringusing 2 attributes successively. First, a radial momentfilter is applied to obtain Fig. 1(d), then a volume filter isapplied to remove the remaining noise. However, usingvector attributes, the result in Fig. 1(f) is obtained in asingle step. This result was obtained using k-means andthe non-compactness attribute with k = 17. Higher di-mension of the vector up to 5 attributes was capable ofisolating the stone in a single step.Foot: Here we aim to enhance the bones but sup-press the tissue. Scalar filters struggle with this task. InFig. 2(c), the non-compactness attribute enhances thebones but with noise still visible. However, the vectorattribute combination of exact surface area [3], fast sur-face area [6] and volume perfectly enhances the bonesand suppresses noise as seen in Fig. 2(e).Female Chest: The objective here is to enhancethe skeleton while suppressing the tissue. From Fig. 2the performance of regular attribute filter is seen inFig. 2(d), the radial moment (β = 3) attribute is ableto enhance the skeleton but other unwanted tissue stillremains. However, a vector attribute filter of any com-bination of size attributes enhances the skeleton withoutleaving unwanted tissue, see Fig. 2(f).Timings: Using a Core 2 Duo E8400 at 2.0 GHz,2GB RAM machine, we ran timings for the computa-tion of the algorithm for vector attributes up to 6 at-tributes for different medical images of varying sizesand gray scale levels. The timings include the com-putation of the attributes and the clusters. Even forvery large data sets like mrt16_angiowith 1,554,454nodes for 6 attributes for k = 23: it takes 17 seconds forsize based attributes and 58 seconds for shape attributes.This is faster than most users can select an optimal set-ting for a single attribute.(a) (b)(c) (d)(e) (f)Figure 1. Left Column: (a) original (c) fil-tered with volume(λ = 9000) (e) vector at-tribute filter by the same attribute usingk = 8. Right Column: (b) original (d)filtered with radial moment (β = 5, λ =0.00256) (f) vector attribute filter by thesame attribute using k = 17.5. Discussion and ConclusionsIn this paper we presented methods for computingvector attribute filtering in 3D, using unsupervised pat-tern classification where features are selected or re-jected based on feature vectors rather than a single prop-erty. We have shown that the unsupervised clustering ofscalar or vector attributes obtain as good or better resultsin enhancing structures in medical images and noisesuppression, than scalar-attribute filtering by manualthreshold selection. These filters have much flexibil-ity in selecting features of interest. The computationalcost is modest, especially when taking the time used formanual optimisation of thresholds into account. Manualselection of clusters is simpler than threshold selection,as only a discrete number of options can be selected.The performance of the combination of size basedattributes was very good on data set that involved sep-arating hard tissue from soft tissue that is the CT scansfoot, chest knee, prostate-stone. While the combina-tion of shape based attributes perform better on enhanc-3114(a) (b)(c) (d)(e) (f)Figure 2. Foot/Female-chest: (a) originalhead; (c) filtered with non-compactness(λ = 2.6); (e) vector-attribute filter us-ing k = 9; (b) original chest; (d) filteredwith radial moment (β = 3, λ = 0.034); (f)vector-attribute filter using k = 12.ing and noise suppression on data sets that emphasisesoft tissue contrast, such as MRI volumes angiolarge,mrt16 angio, mrt16 angio2 . The combination of sizeand shape attributes is biased to the size based attributefiltering. This has to do with use of the L2 metric wherethe features with a large range dominate others.The clustering of scalar attributes (i.e. d = 1) usinga suitable number of clusters for almost all attributesand most data sets gives very good results as comparedto manual threshold selection, irrespective of whether itis a shape or size based attribute. A further incrementin the number of attributes to more than 6 reveals littleor no changes in performance for both size and shapebased attributes. This could be due to the distance usedin the clustering process as the similarity measure. Nor-malisation or relevance learning could be used to com-bine features in a better way. A further problem is thatwe still must set k, and that the user must select thecorrect cluster. Selecting the correct cluster could beassisted by providing a quick rendering of each filteredresult in the GUI. This could also allow selecting mul-tiple clusters efficiently.In future work, we will study the behaviour of vec-tor attribute filters for higher d ≥ 10, and exploreother clustering methods like fuzzy c-means, meanshift or vector quantisation, including ways to estimatethe number of clusters from the attribute distribution.Other dissimilarity measures than the L2 distance willalso be investigated. The curse of dimensionality ulti-mately leads us to explore dimensional reduction tech-niques, since its clear that not all the attributes con-tribute equally to the separation of the data.References[1] E. J. Breen and R. Jones. Attribute openings, thinningsand granulometries. Comp. Vis. Image Understand.,64(3):377–389, 1996.[2] F. Kiwanuka and M. Wilkinson. Radial moment invari-ants for attribute filtering in 3D. In U. Kthe, A. Montan-vert, and P. Soille, editors, Proc. Workshop on Applica-tions of Discrete Geometry and Mathematical Morphol-ogy (WADGMM), pages 37–41, 2010.[3] F. N. Kiwanuka, G. K. Ouzounis, and M. H. Wilkinson.Surface-area-based attribute filtering in 3d. In Proceed-ings of the 9th ISMM ’09, pages 70–81, Berlin, Heidel-berg, 2009. Springer-Verlag.[4] J. B. Macqueen. Some methods of classification andanalysis of multivariate observations. In Proceedings ofthe Fifth Berkeley Symposium on Mathematical Statis-tics and Probability, pages 281–297, 1967.[5] B. Naegel, N. Passat, N. Boch, and M. Kocher. Seg-mentation using vector-attribute filters: Methodologyand application to dermatological imaging. In Proc.Int. Symp. Math. Morphology (ISMM) 2007, pages 239–250, 2007.[6] G. K. Ouzounis, S. Giannakopoulos, C. E. Simopoulos,and M. H. F. Wilkinson. Robust extraction of urinarystones from CT data using attribute filters. In ImageProcessing (ICIP), 2009 16th IEEE International Con-ference on, pages 2629 –2632, 2009.[7] P. Salembier, A. Oliveras, and L. Garrido. Anti-extensive connected operators for image and sequenceprocessing. IEEE Trans. Image Proc., 7:555–570, 1998.[8] P. Salembier and J. Serra. Flat zones filtering, connectedoperators, and filters by reconstruction. IEEE Trans.Image Proc., 4:1153–1160, 1995.[9] P. Salembier and M. H. F. Wilkinson. Connected op-erators: A review of region-based morphological imageprocessing techniques. IEEE Signal Processing Maga-zine, 26(6), 2009.[10] E. R. Urbach, N. J. Boersma, and M. H. F. Wilkin-son. Vector-attribute filters. In Mathematical Morphol-ogy: 40 Years On, Proc. Int. Symp. Math. Morphology(ISMM) 2005, pages 95–104, Paris, 18-20 April 2005.[11] M. A. Westenberg, J. B. T. M. Roerdink, and M. H. F.Wilkinson. 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Cluster-Based Vector-Attribute Filtering for CT and MRI Enhancement

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Cluster-Based Vector-Attribute Filtering for CT and MRI Enhancement

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