Ooijen, Peter M.A. van

Purnama, Ketut E.

Sardjono, Tri A.

Veldhuizen, Albert G.

Verkerke, Gijbertus J.

Wilkinson, Michael H.F.

English

The results of ultrasound imaging of the human spine of a volunteer and the derivation of vertebral axial rotation (AR) and vertebral tilt (VT) are presented. The ARs and VTs were derived semi-automatically. In an ultrasound volume, the vertebral parts were enhanced using a length attribute filter, and localized using Robust Automatic Threshold Selection (RATS). Afterwards, a pair of two prominent regions of each vertebra was selected by ultrasound landmark points. Then, the centers of mass of these regions were used to calculate the ARs and VTs. The ARs and VTs were also determined manually based on the set of landmark points. The difference between the two methods determined the accuracy of the semi-automatic method. The overall results can be categorized into a confident, moderate, and less confident region, respectively, T3–T9, L3–L4, and other vertebrae. For the manual landmark determination, the confident region gave an error range of the ARs determination of 0°–2.1°/0.4°–1.2° (intraobserver/interobserver), and in the VTs determination it had the range of 0.3°–3.2°/0.2°–1.9° (intraobserver/interobserver). The difference between the results of semi-automatic method and the manual results was in the range –1.6° to 1.3°. The results confirm that ultrasound imaging is feasible to scan the human spine, and a semi-automatic method to derive ARs and VTs is also feasible

ARTS repository - University of Groningen

   University of GroningenUltrasound imaging for human spinePurnama, Ketut E.; Wilkinson, Michael H.F.; Veldhuizen, Albert G.; Ooijen, Peter M.A. van;Sardjono, Tri A.; Verkerke, Gijbertus J.Published in:International Journal of Computer Assisted Radiology and SurgeryIMPORTANT 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:2007Link to publication in University of Groningen/UMCG research databaseCitation for published version (APA):Purnama, K. E., Wilkinson, M. H. F., Veldhuizen, A. G., Ooijen, P. M. A. V., Sardjono, T. A., & Verkerke, G.J. (2007). Ultrasound imaging for human spine: Imaging and analysis. International Journal of ComputerAssisted Radiology and Surgery, 2(Suppl. 1), S114- S116.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: 21-06-2022AcknowledgmentBiospace Med (Paris, France) for the partial funding of this study.References1. Benameur et al (2001) 3D biplanar reconstruction of scolioticvertebrae using statistical models. In: Proceedings of theIEEE computer society conference on computer vision andpattern recognition, vol 2, pp II577–II5822. Laporte S et al (2003) A biplanar reconstruction methodbased on 2D and 3D contours: application to the distal femur.Comput Methods Biomech Biomed Eng 6(1):1–63. Le Bras A et al (2004) 3D reconstruction of the proximalfemur with low-dose digital stereoradiography. Comput Ai-ded Surg 51–574. Pomero V et al (2004) Fast accurate stereoradiographic 3D-reconstruction of the spine using a combined geometric andstatistic model. Clin Biomech 19(3):240–247Ultrasound imaging for human spine: imaging and analysisKetut E. Purnamaa,e, Michael. H. F. Wilkinsonb,Albert G. Veldhuizenc, Peter. M. A. van Ooijend, Jaap Lubbersa,Tri A.Sardjonoa, Gijbertus J. VerkerkeaaDepartment of Biomedical Engineering, University MedicalCenter Groningen (UMCG), University of Groningen,The NetherlandsbInstitute for Mathematics and Computing Science,University of Groningen, The NetherlandscDepartment of Orthopaedic Surgery, University Medical CenterGroningen (UMCG), University of Groningen, The NetherlandsdDepartment of Radiology, University Medical Center Groningen(UMCG), University of Groningen, The NetherlandseDepartment of Electrical Engineering, ITS, Surabaya, IndonesiaAbstract The results of ultrasound imaging of the human spine ofa volunteer and the derivation of vertebral axial rotation (AR)and vertebral tilt (VT) are presented. The ARs and VTs werederived semi-automatically. In an ultrasound volume, the verte-bral parts were enhanced using a length attribute filter, andlocalized using Robust Automatic Threshold Selection (RATS).Afterwards, a pair of two prominent regions of each vertebra wasselected by ultrasound landmark points. Then, the centers of massof these regions were used to calculate the ARs and VTs. The ARsand VTs were also determined manually based on the set oflandmark points. The difference between the two methods deter-mined the accuracy of the semi-automatic method. The overallresults can be categorized into a confident, moderate, and lessconfident region, respectively, T3–T9, L3–L4, and other vertebrae.For the manual landmark determination, the confident regiongave an error range of the ARs determination of 0°–2.1°/0.4°–1.2°(intraobserver/interobserver), and in the VTs determination it hadthe range of 0.3°–3.2°/0.2°–1.9° (intraobserver/interobserver). Thedifference between the results of semi-automatic method and themanual results was in the range –1.6° to 1.3°. The results confirmthat ultrasound imaging is feasible to scan the human spine, and asemi-automatic method to derive ARs and VTs is also feasible.Keywords Vertebral axial rotation Æ Vertebral tilt ÆLength attribute filter Æ Robust automatic threshold selection1. IntroductionA drawback of using X-ray imaging to follow scoliosis progres-sion is the radiation exposure. Therefore the number of annualimages is limited. On the other hand, scoliosis progression shouldbe examined frequently, because significant changes can happenwithin 1 month. Another drawback of radiograph imaging is thatit gives 2D images while scoliosis is a 3D phenomenon. Theassessment of the 3D information such as axial rotation andvertebral tilt is important to monitor scoliosis progression inlateral and frontal direction and to decide the right treatment. Asa result, the use of a freehand 3D ultrasound system was proposed[1].The work described in this paper aimed to investigate the possi-bilities of ultrasound to image the human spine, to derive the axialrotation (AR) and vertebral tilt (VT) of each vertebra, and tointroduce a semi-automatic method for this purpose.2. Methods2.1 Image acquisition and volume reconstructionA volunteer was positioned on a bed in prone position. Imageacquisition was performed with the freehand 3D ultrasound sys-tem (FH3DUS) of the Institute of High Frequency Engineering,Ruhr-University Bochum, Germany. It was composed of a 2Dultrasound machine (Siemens Sonoline Omnia), an optical track-ing system (Polaris from NDI) with active markers and a com-puter system. The positional accuracy of the 3D ultrasoundsystem was reported to be 0.66 mm [2]. A 5 MHz curved arrayprobe with an imaging depth of 90 mm was used. The ultrasounddata was acquired via the SVHS-interface of the 2D ultrasoundmachine and a frame grabber card (IDS Falcon) with 25 framesper second (PAL-standard). The resulted frames were used tocreate an ultrasound volume in a volume reconstruction proce-dure. MRI images of the volunteer’s spine in supine position weretaken with a 1.5 T MR scanner (Siemens Avanto).2.2 Recognizing the vertebral parts and calculating thevertebral axial rotation and vertebral tiltThe ultrasound volume was studied by two observers creating apair of landmark points on the two prominent parts of eachvertebra (US-LP). Each observer performed the measurementsthree times to obtain three sets of landmark points. The AR andVT of each vertebra were determined based on the pair of land-mark points. In MRI images, three sets of landmark points (MRI-LP) were also created on the corresponding parts, and the ARsand VTs were calculated.2.3 Calculating the vertebral axial rotation and vertebral tiltsemi-automaticallyA procedure to semi-automatically determine the ARs and VTswas developed. This procedure consisted of the following steps.The first step was the vertebral features enhancement. This stepaimed to exclude elongated parts. It used a length attribute filter, atype of attribute filter in mathematical morphology [3, 4]. In itsimplementation, this filter employs a data structure called max-tree. Parts of the volume (3D regions) were grouped based on theirspatial location and their intensity. Each node of the max-treestored a 3D region and the attribute of this region. The length inlongitudinal direction of each region was used as an attribute. Inthe filtering process, all nodes of the max-tree were inspected. Theattribute of each node was compared with a length thresholdrange. The nodes which have longitudinal length outside the rangewill be removed. Several range values were evaluated to determinean optimum value.The next step was the vertebral localization step. This step usedthe Robust Automatic Threshold Selection (RATS) [5–7] methodto extract high intensity regions of the filtered volume, which wereexpected to contain the vertebral features. This method is a simpleand fast method to extract objects from the background of noisygrey-level (intensity) images. Instead of using a global thresholdvalue, this method determines a local threshold value to extractlocal object. The result of the application of RATS was a numberof high intensity regions including the vertebral regions.A pair of two prominent regions of each vertebra was selected bythe US-LP. The centers of mass of the selected regions were usedto calculate the AR and VT of each vertebra. The difference withthe ARs and VTs derived from the US-LP determined the accu-racy of the semi-automatic method.S114 Int J CARS (2007) 2 (Suppl 1):S82–S1331232.4 Displaying the curvatureA curve that connected the middle point of the centers of mass ofthe left and right region of each vertebra represented the 3Dcurvature of the spine. The projection of this curve to a coronalplane (anterior-posterior view) gave the information about thelateral deviation, and the projection to a sagittal plane (lateralview) gave the information of the degree of the kyphosis andlordosis of the volunter’s spine.3. ResultsAn ultrasound volume which contained the images of all thoracicand lumbar vertebrae was obtained from the image acquisitionand volume reconstruction procedures. The transverse processesin the thoracic region and superior articular processes in thelumbar region appeared. The laminas in several vertebrae and thehead of the ribs in the thoracic regions also appeared. Figure 1shows the axial, sagittal, and coronal cross section images takenfrom the resulted volume.In the ultrasound volume, the landmark points were created onthe transverse processes (T1–T10), laminas (T11–T12), andsuperior articular processes (L1–L5). The region of thoracic ver-tebra T3 until T9 gave an intraobserver (AE) and interobserver(EE) error range of the ARs determination of 0°–2.1° (AE) and0.4°–1.2° (EE). In the VTs determination the range was 0.3°–3.2°(AE) and 0.2°–1.9° (EE). The difference between the results ofsemi-automatic method and the manual results was in the range–1.6° to 1.3°. The region of the lumbar vertebra L3 and L4 pre-sented bigger errors, and the other thoracic and lumbar vertebraeshowed the biggest errors. In the MRI images, the results derivedfrom T3 until T9 were: 0° –7.8° (AE of ARs), 0.4°–4.6° (EE ofARs), 0°–4.1° (AE of VTs), and 0.2°–2.8° (EE of VTs).The results of the application of different length threshold valuesto the original image (Fig. 2a) are shown in Fig. 2b until Fig. 2d.In Fig. 2b, the skin and muscle which cover the inner structureswere removed. In Fig. 2c, the muscles (high intensity clouds in thelower part in Fig. 2b) were also removed. The pleura (the chain ofhigh intensity regions in the upper part in Fig. 2b, c) were ex-cluded as shown in Fig. 2d.Figure 2e shows the result of the application of RATS to thevolume of Fig. 2d. The number of 3D regions was reduced sig-nificantly while most of the regions of the vertebral features re-main.The regions selected by the ultrasound landmark points are shownin Fig. 2f. A curve which connects the middle point of the centersof mass of the two regions of each vertebra represents the 3Dcurvature of the spine. Its projection to a coronal plane andsagittal plane are shown in Fig. 2g, h, respectively.4. ConclusionThe results confirm that ultrasound imaging is feasible to scan thehuman spine. Based on the resulted errors, the region of thoracicvertebra T3 until T9, L3 and L4, and the other thoracic andlumbar vertebrae can be categorized, respectively, as a confident,moderate, and less confident region regarding the recognition ofthe vertebral features. The differences are mainly caused by thepresence or absence of muscles and other soft tissues. The confi-dent region showed the smallest intraobserver and interobservererror in determining the axial rotation and vertebral tilt. MRIimages that were aimed to be used for validation had comparableFig. 1 The axial a, sagittal b, and coronal c cross section imagesof the ultrasound volume resulted from the image acquisition andvolume reconstruction procedures. The transverse process (TP)and superior articular processes (SAP) of the vertebra, rib (R),and pleura (P) are visibleFig. 2 The non-vertebral parts of the original images (a) areremoved using a length attribute filter: the elongated parts such asskin and muscle (b), the muscles in the lower part (c), and pleura(d). The result of the vertebral feature localization using RobustAutomatic Threshold Selection is shown (e)Int J CARS (2007) 2 (Suppl 1):S82–S133 S115123intraobserver and interobserver error. Hence, the validation cannot be performed. A semi-automatic method appears to be fea-sible in deriving the axial rotation and vertebral tilt of each ver-tebra.AcknowledgmentsThe authors acknowledge the support of the Institute of HighFrequency Engineering, Ruhr-University Bochum, Germany. Thefirst author would like to express his gratitude to Jan Visscher,Wim Tukker, and Peter Kappert of the Department of Radiology,University Medical Center Groningen, The Netherlands for theirhelp in providing medical images.References1. Purnama KE, Wilkinson MHF, Veldhuizen AG, OoijenPMAv, Sardjono TA, Brendel B et al (2006) Imaging thewhole spine using a free-hand 3D ultrasound system. In: The52th annual meeting of the orthopaedic research societyCD-ROM2. Brendel B, Winter S, Ermert H.A simple and accurate cali-bration method for 3D freehand ultrasound (unpublished)3. Breen EJ, Jones R (1996) Attribute openings, thinnings, andgranulometries. Comput Vis Image Underst 64(3):377–3894. Salembier P, Serra J (1995) Flat zones filtering, connectedoperators, and filter by reconstruction. IEEE Trans ImageProcess 4(8):1154–11605. Kittler J, Illingworth J (1985) Threshold selection based on asimple image statistic. Comput Vis Graph Image Process30:125–1476. Wilkinson MHF (1998) Optimizing edge detectors for robustautomatic threshold selection: coping with edge curvature andnoise. Graph Models Image Process 60:385–4017. Wilkinson MHF (2006) Gaussian-weighted moving-windowrobust automatic threshold selection. CAIP LNCS 2756:369–376Analysis of texture features for registration of DRRand EPI imagesAndreja Jarca,b, Peter Rogelja, Stanislav KovačičaaUniversity of Ljubljana, Faculty of electrical engineering, SloveniabSipronika d.o.o, LjubljanaAbstract The aim of our research is to analyse the importance oftexture information for registration of a DRR (Digital Recon-structed Radiograph) and EPI (Electronic Portal Image) medicalimages. In our research, texture features are extracted by Lawstexture coefficients and used for computing registration criterionfunctions. The proposed feature based approach is compared tothe commonly used approach, where a registration criterionfunction is computed directly from intensity features, i.e. greyvalues. For this purpose we observed accuracy of registration, thedistinctiveness of local extrema and the distinctiveness of a globalextremum of the criterion functions. These parameters are essen-tial to achieve a correct image alignment. Our results show thatfor the given image modalities we can expect more robust andmore correct registration when texture based criterion functioninstead of intensity based one is used.Keywords Image registration Æ Texture analysis ÆCriterion function1. IntroductionThe aim of our research is to analyse the importance of textureinformation for registration of a DRR (Digital ReconstructedRadiograph) and EPI (Electronic Portal Image) medical images.These two image modalities are present in radiotherapy treatment.By correcty matching the two modalities, it would be possible toverify the positioning of the patient during radiation therapy andautomatically adjust the positioning if necessary.There are mainly two approaches to solve the problem of regis-tering multi-modal images: (1) by extracting simple features fromimages (such as salient points or contours) and register thesefeatures (i.e. feature matching); or (2) by using the original grayvalue information from images (i.e. intensity matching), andmatch the images by optimizing some intensity-based similaritymeasure [1, 2]. Feature based registration algorithms involvegeometrical information from both images, which is extractedduring the pre-segmentation. Precise segmentation of anatomicalgeometrical features is a tedious and subjective task and, fur-thermore, difficult to automate. On the other hand, intensitybased registration can easily be automated. However, intensityvalues for DRR/EPI image modalities due to 2D representation of3D data, do not comply with some global intensity relationship,expected by intensity-based registration approaches. Conse-quently, intensity based registration is not reliable.In the paper we propose an alternative registration approach,based on texture features. This approach couples advantages ofintensity-based registration with advantages of feature-basedregistration. It does not require explicit pre-segmentation andrelies on structural information obtained from original images.In order to test the proposed registration approach, a comparisonof the criterion functions for both intensity and texture features isdiscussed. The experiments have been carried out on five sets of2D DRR and EPI images of pelvis (Figs. 1, 2).2. Materials and methodsTexture feature based registration requires, first, extraction oftexture features from both of the images. In our approach weextracted Laws texture coefficients. Laws [3] developed a set of 2DFig. 1 DRR image of pelvis. The reference image of resolution:490 · 375 pixelsFig. 2 EPI image of pelvis. The floating image of resolution:490 · 375 pixelsS116 Int J CARS (2007) 2 (Suppl 1):S82–S133123

Ultrasound imaging for human spine:Imaging and analysis

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Ultrasound imaging for human spine:Imaging and analysis

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