Int J CARS (2007) 2:19–30DOI 10.1007/s11548-007-0073-9

ORIGINAL ARTICLE

Segmentation of the temporalis muscle from MR data

H. P. Ng · Q. M. Hu · S. H. Ong · K. W. C. Foong ·P. S. Goh · J. Liu · W. L. Nowinski

Received: 20 October 2006 / Accepted: 23 January 2007 / Published online: 17 March 2007© CARS 2007

Abstract Objective A method for segmenting thetemporalis from magnetic resonance (MR) images wasdeveloped and tested. The temporalis muscle is one ofthe muscles of mastication which plays a major role inthe mastication system.Materials and methods The temporalis region of inter-est (ROI) and the head ROI are defined in referenceimages, from which the spatial relationship between thetwo ROIs is derived. This relationship is used to definethe temporalis ROI in a study image. Range-constrainedthresholding is then employed to remove the fat, bonemarrow and muscle tendon in the ROI. Adaptive mor-phological operations are then applied to first removethe brain tissue, followed by the removal of the other

H. P. Ng (B) · K. W. C. FoongNUS Graduate School for Integrative Sciencesand Engineering, Singapore, Singaporee-mail: g0404206@nus.edu.sg

H. P. Ng · Q. M. Hu · J. Liu · W. L. NowinskiBiomedical Imaging Lab, Agency for Science Technologyand Research, Singapore, Singapore

S. H. OngDepartment of Electrical and Computer Engineering,National University of Singapore, Singapore, Singapore

S. H. OngDivision of Bioengineering, National University of Singapore,Singapore, Singapore

K. W. C. FoongDepartment of Preventive Dentistry, National Universityof Singapore, Singapore, Singapore

P. S. GohDepartment of Diagnostic Radiology, National Universityof Singapore, Singapore, Singapore

soft tissues surrounding the temporalis. Ten adult headMR data sets were processed to test this method.Results Using five data sets each for training and test-ing, the method was applied to the segmentation of thetemporalis in 25 MR images (five from each test set).An average overlap index (κ) of 90.2% was obtained.Applying a leave-one-out evaluation method, an aver-age κ of 90.5% was obtained from 50 test images.Conclusion A method for segmenting the temporalisfrom MR images was developed and tested on in vivodata sets. The results show that there is consistencybetween manual and automatic segmentations.

Keywords Temporalis · Masticatory muscle · MRI ·Segmentation

Introduction

The temporalis muscle belongs to the group of masti-catory muscles that also includes the masseter, lateraland medial pterygoids. The masticatory muscles have adirect influence on one’s ability to chew and smile inthe desired manner. Facial surgery to correct anatomicjaw relationship spatial discrepancies can affect the ana-tomic position and chewing function of the masticatorymuscles. Pre-surgical facial models have been designedand pre-surgical simulations have been carried out toaid surgeons in their surgical planning [1–3].

The primary inadequacy of current pre-surgical mod-els is that they lack information about spatial position,shape and size of the masticatory muscles, which wouldbe useful for surgical planning. Our long-term goal isto create accurate models of the masticatory musclesthrough segmentation of these muscles. We are currently

20 Int J CARS (2007) 2:19–30

working on automated techniques for the segmentationprocess, which, compared to manual contour tracing,would result in greater consistency, savings in clinicians’time, and quantification of important parameters suchas volume and surface area.

In our earlier work [4], we present a method for thesegmentation of the masseter, lateral and medial pteryg-oids. Our focus here is on the temporalis. This muscle hasa very wide origin from the entire temporal fossa and thefascia covering the muscle. Its fibers insert into the cor-onoid process of the mandible. When the entire musclecontracts, the overall action pulls up on the coronoidprocess, which results in the mandible being elevatedand the mouth closed. We propose a new method forthe temporalis as the method described in [4] relies onthe GVF [11] snake, which does not give good segmen-tations of the temporalis (see Comparison with resultsobtained using GVF snake).

Numerous image processing techniques have beendeveloped for the segmentation of various anatomicstructures, but to our knowledge, there is none for thetemporalis. In our work, we make use of threshold-ing and morphology operators. Thresholding is amongthe commonly used techniques for the segmentationof medical images. It is an intuitive and often effec-tive means for obtaining the segmentation of images inwhich different structures have contrasting intensities.A recent approach, supervised range-constrained thres-holding [5], improves on conventional thresholding byutilizing knowledge of the relative sizes of the regions ofinterest (ROI) to confine the analysis in the histogramto the ROI. Though it is not proven that this techniquewill always yield a better threshold than conventionalthresholding, our experience shows that it is able to pro-vide thresholds that result in more consistent and robustbinarization. A more recent approach to thresholdingusing prior knowledge of object proportion and inten-sity contrast has been proposed for the extraction ofsmall objects [6]. Other examples of thresholding beinginstrumental in medical imaging include a local thresh-old algorithm that determines marrow intensity value inthe neighborhood of each voxel based on nearest-neigh-bor statistics [7] and a multilevel thresholding techniquethat generates a contour map used for the identifica-tion of coherent regions [8]. Besides thresholding, othercommonly used techniques in medical image processinginclude the active model, which matches a deformablemodel to an image [9,10]. In general, they are energyminimizing splines whose energy depends on their shapeand location within the image.

The lack of a method for the segmentation of thetemporalis could be due to the fact that it has a com-plex structure, and that it has similar intensity values

with surrounding tissues without any clear boundariesat times. Our method provides a solution to the prob-lems posed by the above characteristics. A spatial rela-tionship relating the temporalis ROI to the head ROI isfirst obtained from reference images where the tempo-ralis has been manually segmented. The temporalis ROIis automatically detected in a study image. Range-con-strained thresholding is used to remove the fat, whitematter, cerebrospinal fluid (CSF) and muscle tendon inthe ROI. This is followed by the use of adaptive mor-phological operations to first remove the brain tissue,followed by removing other soft tissues surrounding thetemporalis, before finally deriving the final segmenta-tion. Our proposed method produces superior resultswhen applied to the temporalis compared to the GVFsnake [11].

The Section Materials and methods describes the dataused and proposed method, and the following sectionspresent the Results, Discussion and the Conclusion.

Materials and methods

Data acquisition

Ten data sets were acquired using a 1.5 T Siemens MRscanner (symphony maestro class with quantum gradi-ents) and a T1 FLASH imaging sequence (1 mm thick-ness, 512 × 512 matrix, 240 mm FOV, TR = 9.93,TE = 4.86). The subjects are adult volunteers whoseidentities are masked. Five data sets were used to trainthe system to automatically detect the temporalis ROIwhen given a test image. The other five data sets wereused for validation purposes.

Overview of method

The proposed method (Fig. 1) is designed to automati-cally detect the head and temporalis ROIs (Fig. 2a) in astudy 2D MR image of the head by first acquiring knowl-edge of the spatial relationship between the head andtemporalis ROIs from training images. We then employrange-constrained thresholding [5] to remove the fat,white matter, CSF and muscle tendon in the temporalisROI, after which, adaptive morphology operators areapplied to extract the temporalis.

Selection of reference slice from each MR data set

With our current imaging protocol for scanning thehuman muscles of mastication, each MR data set con-tains images for the entire masseter and pterygoids.However, a small segment of the temporalis that is

Int J CARS (2007) 2:19–30 21

Relate head ROI to temporalis ROI

Temporalis ROI detection

Head ROI detection

Input reference image

Remove soft tissue connected to temporalis

Remove brain tissue

Apply range-constrained thresholding to temporalis ROI

Automatic temporalis ROI detection

Head ROI detection

Input study image

Fig. 1 Overview of methodology, Stage 1 Knowledge acquisitionon spatial relationship between head ROI and temporalis ROIStage 2 Segmentation of temporalis in study image

cephalad to the pinna is not imaged, but this does nothave any significant effect on the development of theproposed method. Unlike our previous work on themasseter and pterygoids [4], where the reference slicefor each targeted muscle is the image with the largestarea of the targeted muscle, we select the referenceslice for the temporalis at the level just cephalad of thepinna. Currently this selection is done manually, but itcan be automated via tracking the contour of the headboundary.

Spatial relationship between temporalis and ROIsfrom reference images

From the reference MR images (one from each of thefive training data sets), we determine the spatial rela-tionship between the head and temporalis ROIs. Thetemporalis and head ROIs are the bounding boxes ofthe temporalis and head regions, respectively, in a 2DMR image. This spatial relationship serves as the prior

Fig. 2 a Spatial relationship, b Spatial measurements betweenhead and temporalis ROI in reference image

knowledge for training the system to identify the tem-poralis ROI in the study image.

In each reference image, the head ROI is determinedthrough the projections of the image in the x (horizon-tal) and y (vertical) directions. The temporalis ROI isdefined to be the bounding box of the manually seg-mented temporalis in the reference image.

The spatial relationship between the head and tem-poralis ROIs (Fig. 2a) is specified in terms of the dis-tance between the boundaries of the head ROI and theorigin of the muscle ROI. For a reference image of thetemporalis, the distances b1, w1, j1, k1, v1, h1 (Fig. 2b) aremeasured and the relative distances calculated as fol-lows:

h1, r = h1

b1, v1, r = v1

w1, k1, r = k1

b1, j1, r = j1

w1(1)

To obtain a good estimate of the spatial relationship, weuse the mean values of h1, r, v1, r, k1, r, j1, r obtained fromthe five reference images.

Detection of temporalis ROI in study images

Given an image from the test data set, the system firstautomatically determines the head ROI based on thevertical and horizontal projections (described in the

22 Int J CARS (2007) 2:19–30

Fig. 3 Cumulative histogram of temporalis ROI

previous section). Given that the width and length ofthe head ROI in the study image are w2 and b2, respec-tively, we derive equations for the spatial parametersj2, k2, v2, h2

j2 = w2jirs, k2 = b2kirs, v2 = w2virs, h2 = b2hirs

(2)

The graphical representation can be found in Fig. 7aof our earlier paper [4], where the parameters here,j2, k2, v2, h2 are represented by e1, f1, d1, c1, respectively.Parameter s is a scaling factor introduced to allow for theslight variations in location, shape and size of the mus-cles between different subjects. In our work, the valueof s is set to 1.3. We discuss the effects of varying s inSensitivity to scaling factor s and rotation of MR image.

Range-constrained thresholding on temporalis ROI

Having determined the temporalis ROI (Fig. 4a), weapply the default fuzzy C-means (FCM) clustering [12]in Matlab 7.0 for an initial segmentation of the tempo-ralis ROI. While we use FCM with four clusters in ourwork, experiments show that FCM with three clustersyields similar final results. We denote the fraction of thepixels belonging to the clusters with the lowest and high-est mean intensities as x1 and x2, respectively (indicatedin the cumulative histogram of Fig. 3). In the temporalisROI, fat and white matter have relatively high inten-sity values compared to the temporalis and other softtissue. Therefore, they belong to the cluster with thehighest mean intensity value. On the other hand, CSFand muscle tendon have relatively low intensity values,and hence it is reasonable to assume that they belongto the cluster with the lowest intensity. We make use ofrange-constrained thresholding [5] to remove fat, whitematter, CSF and the tendon from the temporalis ROI,

since it has been demonstrated in [5] that it is able toprovide a threshold with more consistent and robust bi-narization than conventional thresholding methods.

Two parameters associated with range-constrainedthresholding Hb

l and Hbh , constrain the fraction range

within the ROI. When determining the upper thresholdfor the removal of fat and white matter, we set Hb

l andHb

h , to be 1−x2−µ and 1−x2+µ, respectively, where µ

is the tolerance to FCM’s clustering error. When deter-mining the lower threshold for the removal of CSF andtendon, we set Hb

l and Hbh ,to be x1 − µ and x1 + µ,

respectively. Having determined the frequency ranges,we then made use of Otsu’s method [13] to determinethe respective thresholds within the specified fractions.In our study, similar results are obtained for µ varyingbetween 0.05 and 0.10. Examples of these results, as wellas a discussion on FCM susceptibility to local minima,are described in Sensitivity of range-constrained thres-holding to fraction range in ROI and comparison withFCM and Otsu methods.

Adaptive morphology to remove brain tissuein temporalis ROI

A substantial amount of brain tissue remains in the tem-poralis ROI after thresholding (Fig. 4b). Although thistissue constitutes the largest proportion of the ROI, it isnot feasible to use connected component labeling [14] tolocate the largest connected component and remove itbecause there is a possibility that small parts of the tem-poralis may be connected to it. The use of a fixed struc-turing element to morphologically separate the brainand muscle tissue is also not advisable as this may causeexcessive erosion to the muscle structure. Hence, wepropose the use of adaptive morphology operations toseparate the brain tissue from the other soft tissues.

In the temporalis ROI, we first check if the tissue onthe left side of the ROI (which actually lies in the righthemisphere of the brain) is connected to the soft tissueon the right side of the ROI. If they are, we apply mor-phological opening to the ROI with a circular structuringelement of radius 1. If the brain tissue is still connectedto the soft tissue on the right side, we apply morpholog-ical opening with the radius of the structuring elementincreased, by 1. The entire process is iterated until thebrain tissue is separated from the remaining soft tis-sue. This approach fits our work because in the tempo-ralis ROI, the CSF is removed after range-constrainedthresholding and there will be a demarcation betweenbrain tissue and the remaining soft tissue. There maybe some weak connections between them, and these are

Int J CARS (2007) 2:19–30 23

Fig. 4 Temporalis ROIa before, b afterrange-constrainedthresholding, c segmentedbrain tissue, d temporalis ROIafter subtraction of braintissue, e ROI with smallconnected componentsremoved

removed after applying the above adaptive morpholog-ical procedure.

The segmented brain tissue (Fig. 4c) is subtractedfrom the temporalis ROI, leaving the other soft tissue inthe ROI (Fig. 4d). Small connected components are thenremoved from the ROI and only the largest connectedcomponent is left (Fig. 4e).

Removal of unwanted soft tissue around temporalisin ROI

After applying range-constrained thresholding and mor-phological operations to the temporalis ROI, the pres-ence of unwanted soft tissue persists. From empiricalstudies with the study images, it is observed that thetop half of the temporalis ROI usually contains moreunwanted components compared to the bottom half ofthe temporalis ROI. In the upper half, the eye and othersoft tissue surrounding the temporalis are not removedduring thresholding. In the lower half, there is only aslight presence of unwanted soft tissue (if any) surround-ing the temporalis after range-constrained thresholdingand adaptive morphological removal of the brain tis-sue. We provide an illustration in Fig. 5a and b, whichdisplay the top half and bottom halves, respectively,of the temporalis ROI. Comparing the two, it is clearthat the former requires more processing to remove theunwanted soft tissue compared to the latter.

Hence, we first divide the temporalis ROI (j2 × k2)into two equal partitions (top and bottom), with the toppartition comprising the first (j2/2) rows of the tempo-ralis ROI and the bottom partition comprising the next(j2/2) rows. We then make use of a similar adaptive mor-phology technique, described in Adaptive morphologyto remove brain tissue in temporalis ROI, to process thetwo partitions. For the top partition, it is assumed that nopixel belonging to the temporalis lies on the boundariesof the temporalis ROI. Hence, in our method, a check isfirst carried out to see if any pixel belonging to the larg-

Fig. 5 a Top partition, b Bottom partition of temporalis ROI inFig. 4e

est connected component lies on the boundaries of thetemporalis ROI. If so, morphological opening is appliedto the top partition to remove small unwanted compo-nents. The structuring element used in the first iterationis a disk with radius 1. If any boundary pixel is still partof the largest connected component, we increase thedisk radius by 1 and re-apply morphological opening tothe original top partition. The iterations stop when thelargest connected component does not have any pixelslying on the boundary of the temporalis ROI. The sameprocess is employed on the bottom partition.

Evaluation

The manual segmentations of the temporalis, whichserve as the ground truth in the evaluation of our pro-posed method, are provided by an expert radiologist(PSG, with more than 15 years of clinical experience).We make use of the κ index [15] to evaluate the consis-tency between our computerized segmentations and themanual segmentations

κ = 2 ×(

N(M ∩ S)

N(M) + N(S)

)× 100% (3)

where M and S denote the regions obtained by manualsegmentations and the proposed method, respectively,

24 Int J CARS (2007) 2:19–30

M ∩ S the intersection between M and S, and N(·) thenumber of pixels in a region. The smallest value of κ

is 0% (no overlap) and the largest value is 100% (exactoverlap).

Two other evaluation measures used are the false pos-itive rate (FPR) and the false negative rate (FNR). FPRmeasures the probability of the method incorrectly giv-ing a positive result and is defined as

FPR =(

N(Fp)

N(M)

)× 100% (4)

where N(Fp

)denotes the number of pixels which the

method incorrectly determines as positive. The proba-bility of a negative result and is given by

FNR =(

N(Fn)

N(M)

)× 100% (5)

where N(Fn) denotes the number of pixels which themethod incorrectly determines as negative.

Results

The proposed method was applied to five MR study datasets. In each data set, the user is required to select the ref-erence slice for the temporalis. We then performed 2Dsegmentation of the temporalis on the reference slice, aswell as on the two slices superior and two slices inferiorto the reference slice. Hence, a total of 25 segmentationresults (five from each of the five study data sets) wereobtained.

As the number of data sets used in our work is rela-tively small, we also made use of the leave-one-out eval-uation strategy [16] to evaluate the performance. Withthis method, all the data sets were involved in training aswell as testing, giving a total of 50 segmentation results.

Accuracy

A set of results obtained after each stage of opera-tion in our method is shown in Fig. 6. The results havebeen obtained using s = 1.3. Numerical validations areperformed on the 25 segmentation results, using fivetraining sets and five test sets by checking for their con-sistencies with the ground truths (Table 1). The mean κ ,FPR and FNR are 90.2, 8.7 and 10.9%, respectively.

Using the leave-one-out evaluation strategy, the meanκ , FPR and FNR obtained from 50 results are 90.5, 9.1and 9.8%, respectively. The results (Table 2) are similarto those obtained earlier.

Table 1 Numerical validation results for segmentation of tempo-ralis

Image Index κ (%) FPR (%) FNR (%)

1 91.7 8.1 8.52 88.5 9.8 13.23 85.2 11.6 18.04 92.0 6.3 9.75 89.7 8.5 12.16 90.6 7.7 11.17 88.6 10.4 12.48 92.2 5.8 9.89 89.8 9.8 10.610 91.6 8.8 8.011 92.5 7.1 7.912 89.5 11.8 9.213 92.0 4.5 11.514 91.1 6.8 11.015 88.9 10.1 12.116 87.8 11.8 12.617 92.1 7.5 8.318 92.8 5.5 8.919 90.2 8.8 10.820 91.5 6.8 10.221 89.5 9.1 11.922 91.0 10.8 7.223 88.1 9.8 14.024 87.8 12.1 12.325 90.8 7.5 10.9Mean 90.2 8.7 10.9

Sensitivity to scaling factor s and rotation of MR image

We introduce a scaling factor s in Detection of tempo-ralis ROI in study images to enlarge the temporalis ROIin the study image. A larger ROI may be needed toensure that the muscle is fully enclosed. As illustratedin Fig. 7, when s = 1, the ROI fails to enclose the entiretemporalis. From experiments, we obtain a minimumvalue of s = 1.3 to ensure the ROI encompasses thetemporalis. In addition, we have also experimented withs = 1.5, 1.75, 2.0 and 2.5, with the results in Fig. 7. Thesegmentation results, with mean κ around 90%, are com-parable to the result obtained using s = 1.3. The advan-tage of using a larger value of s is that it allows the ROI tofully enclose the temporalis even when the images havebeen rotated. This is analogous to a situation where thesubject’s head is rotated for the scan. However, when s isincreased to a large value, the proposed method may failto produce the expected segmentation results. Figure 8displays the corresponding images when s = 3.5 and thetemporalis is not segmented correctly.

We tested our method for its robustness in scenarioswhere the image has been rotated by 15◦ clockwise andanti-clockwise, or 15◦ up and down from the uprightposition. The value of s used is 2.5. The segmentationresults are comparable to those obtained when the

Int J CARS (2007) 2:19–30 25

Fig. 6 Results after eachstage of segmentation

images are not rotated and the average κ is around90%. We note that for s = 2.5, the ROI will containmore spurious components compared to s = 1.3. How-ever, our method is sufficiently robust to overcome thisproblem and produce good segmentations of the tem-poralis. Where there is a wide rotation of the head(>15◦), a possible solution will be to locate the midsag-ittal plane (MSP) in the image [17] and rotate the imagetill the MSP is in an upright position before applyingour method. Besides the MSP, locating other anatomicalfeatures, such as the eyes or medulla could also help in

the localisation and make our proposed method morerotationally invariant.

Sensitivity to choice of reference slice

As mentioned in Selection of reference slice from eachMR data set, we selected the reference slice for the tem-poralis at the level just cephalad of the pinna. Usingthis reference slice, we obtain a segmentation accuracyof around 90%. In addition, we tested the robustnessof the proposed method to different selected reference

26 Int J CARS (2007) 2:19–30

Table 2 Validation results using leave-one-out evaluation strategy

Test set Image κ (%) FPR (%) FNR (%) Test set Image κ (%) FPR (%) FNR (%)

1 1 85.2 11.6 18.0 6 26 87.8 11.8 12.62 88.5 9.8 13.2 27 92.1 7.5 8.33 91.7 8.1 8.5 28 92.8 5.5 8.94 92.0 6.3 9.7 29 91.5 6.8 10.25 89.7 8.5 12.1 30 90.2 8.8 10.8

2 6 88.6 10.4 12.4 7 31 87.8 12.1 12.37 90.6 7.7 11.1 32 89.5 9.1 11.98 92.2 5.8 9.8 33 91.0 10.8 7.29 91.6 8.8 8.0 34 90.8 7.5 10.910 89.8 9.8 10.6 35 88.1 9.8 14.0

3 11 89.5 11.8 9.2 8 36 89.8 9.7 10.712 92.0 4.5 11.5 37 91.5 10.1 6.913 92.5 7.1 7.9 38 93.1 6.8 7.114 91.1 6.8 11.0 39 91.8 8.1 8.315 88.9 10.1 12.1 40 90.5 8.8 10.2

4 16 88.7 10.5 12.1 9 41 89.8 11.5 8.917 90.4 9.2 10.1 42 91.6 10.8 6.118 92.3 7.1 8.3 43 93.4 8.1 5.119 91.2 8.8 9.0 44 92.5 6.5 8.520 86.9 13.5 12.7 45 89.3 12.3 9.1

5 21 90.7 9.4 9.2 10 46 90.9 9.5 8.722 91.3 7.2 10.2 47 91.7 8.3 8.323 93.0 8.7 5.4 48 92.6 7.7 7.124 90.5 11.8 7.2 49 90.8 12.1 6.325 87.9 12.3 11.9 50 89.1 11.8 10.1

Mean κ = 90.5% Mean FPR = 9.1% Mean FNR = 9.8%

slices. We selected as reference slice the image slicesthat are five slices superior and five slices inferior to theproposed reference. The values of κ , FPR and FNR are87.0, 17.2, 7.6%, respectively, when the reference slice isfive slices superior to the proposed reference, and 87.6,20.0, 2.7%, respectively, when the reference slice is fiveslices inferior to the proposed reference. The results aresimilar to those obtained when the proposed referenceis used. Selecting the reference slice to be at the leveljust cephalad of the pinna offers the possibility of auto-mating the process via tracking the contour of the headboundary.

Sensitivity of range-constrained thresholdingto fraction range in ROI and comparison withFCM and Otsu methods

As mentioned in Range-constrained thresholding ontemporalis ROI, there are two parameters associatedwith range-constrained thresholding, namely Hb

l andHb

h , that constrain the fraction range within the ROI.Hb

l and Hbh can be varied by adjusting µ, which we

define as the tolerance to FCM’s clustering error. Weexperimented with values of µ ranging from 0.05 to 0.10and obtained similar results. An illustration of the results

produced by range-constrained thresholding usingµ = 0.05 and µ= 0.10 is shown in Fig. 9. We have also com-pared our method against FCM clustering and Otsu’smethod, with the results in Fig. 9. It is seen that Otsu’smethod misclassified some brain tissue and parts ofthe temporalis as background. FCM clustering producessatisfactory results but it also has the problem of mis-classifying some boundary brain tissue. The susceptiabil-ity of FCM to local minima depends on the complexityof the feature space as well as the initial condition. Ifthe system contains local minima and the initial state islocated near a local minimum, then FCM will convergeto the local minimum. On the other hand, similar to var-iance-based thresholding methods, FCM tends to resultin a type of systematic error which selects the thresh-old towards the component with the larger probabilityor larger variance [18]. Nevertheless, FCM could pro-vide a good initial solution, which can be improved bymethods incorporating some prior knowledge such asrange-constrained thresholding.

Comparison with results obtained using GVF snake

We have compared the results obtained using ourmethod against those obtained using the GVF snake,which is one of the popular techniques used in medical

Int J CARS (2007) 2:19–30 27

Fig. 7 Segmentation resultswhen parameter s is varied

image segmentation. We initialize the GVF snake withpoints close to the boundaries of the temporalis, andthe results are shown in Fig. 10. The value of κ , FPRand FNR are 80.8, 26.7, 12.1%, respectively, for the firstresult in Fig. 10, and 84.7, 14.7, 15.7%, respectively, forthe second result in Fig. 10. Comparing these resultsagainst those in Figs. 6 and 7, we note that our methodproduces superior results. The GVF snake does not seg-ment the temporalis accurately but, instead, convergesto the muscle tendon within the temporalis (Fig. 10). Incontrast, the proposed method is able to retain the thinspace occupied by the tendon (Figs. 6, 7). Another indi-cation that our method produces superior results is thatit manages to delineate the temporalis from its surround-ing tissue in narrow and concave regions, unlike the use

of the GVF snake, which propagates inwards and awayfrom the concave boundaries despite the initializationsbeing made near them.

Discussion

The temporalis has a complex structure as observed inthe MR images. This, together with the fact that it issurrounded by soft tissue with similar intensity values,has made its segmentation from MR images challenging.The proposed method, which involves thresholding andmorphological operations, is able to produce segmenta-tions with average κ of 90%.

28 Int J CARS (2007) 2:19–30

Fig. 8 Segmentation resultsobtained when s = 3.5

Fig. 9 Resulting temporalisROI using differentthresholding techniques

Int J CARS (2007) 2:19–30 29

Fig. 10 Segmentation resultsobtained using GVF snake

Based on empirical studies, we divided the temporalisROI into two equal partitions before further process-ing, as mentioned in Removal of unwanted soft tissuearound temporalis in ROI. The presence of the eye andunwanted soft tissue surrounding the temporalis, evenafter range-constrained thresholding and extraction ofthe brain tissue, means that the top half of the ROIrequires more processing than the bottom half. We makeuse of morphological opening to separate the temporal-is from the unwanted components. With reference toFig. 5a and b, based on our method, the top partitionundergoes morphological processing whereas the bot-tom partition does not, and hence the original structureof the temporalis in the bottom partition is preserved.If we had processed the temporalis ROI as a whole,we would find that the bottom half of the temporal-is ROI undergoes the same morphological opening asthe upper half, and the resulting structure will not be agood representation of the temporalis. To further illus-trate our method, Fig. 11a displays the bottom partitionfrom another temporalis ROI. Unlike that in Fig. 5b, itrequires morphological processing to break the connec-tions between the temporalis and its surrounding soft

Fig. 11 Bottom partition of temporalis ROI a before processingb after processing

tissue. Fig. 11b displays the result after our method hassuccessfully removed the unwanted soft tissue.

Our segmentation results indicate that our methodis capable of segmenting the temporalis from 2D MRimages. The advantages with our method are that it iseffective when the ROI is enlarged to encompass moreunwanted components and when the images are rotated.

30 Int J CARS (2007) 2:19–30

These signs are encouraging and we plan to validate ourmethod against more data sets from different imagingsystems. We also plan to invite more clinicians to provideground truths.

Our segmentation results indicate that our proposedmethod is capable of segmenting the temporalis from2D MR images. However, more efforts will have to beput to extend this preliminary work to 3D segmentationof the temporalis in the future.

Conclusion

We have proposed a method to segment the temporal-is, one of four groups of muscles of mastication, fromMR images. This, to our best knowledge, is currentlyunavailable. The task is a challenging one due to sur-rounding tissue with similar intensities. Despite this, ourproposed method, which automatically detects the tem-poralis ROI in a study image, is able to perform seg-mentation of the muscle with average overlap index (κ)of more than 90%. We have made use of a numberof image processing techniques, including range-con-strained thresholding and adaptive morphological oper-ations, to achieve our objective. Our method is designedto be as automatic and adaptive as possible, with the onlyhuman intervention involved being the selection of thereference slice, and hence the results will be subjectedto intra- and inter-observer variations to a lesser extent.

Though there is an increased emphasis on model-based techniques, such as level sets [19,20], whichinvolves the use of deformable models and can beapplied to both 2D and 3D images, the complex struc-ture of the temporalis inhibits us from directly designinga 3D segmentation method for it. An extension from 2Dto 3D involving the information acquired from the 2Dsegmentation is currently being investigated.

Acknowledgments The first author will like to thank Agencyfor Science, Technology and Research (A*Star), Singapore forfunding his Ph.D studies. This project is funded byNUS R-222-000-011-112 from the Faculty of Dentistry, National Universityof Singapore. The authors thank Mr Christopher Au, PrincipalRadiographer at National University Hospital, Singapore for hisassistance in data acquisition.

References

1. Meehan M, Teschner M, Girod S (2003) Three-dimensionalsimulation and prediction of craniofacial surgery. J OrthodonCraniofac Res 6(1)103–107

2. Xia J, Samman N, Yeung RW, Wang D, Shen SG, Ip HH,Tideman H (2000) Computer-assisted three-dimensional sur-

gical planning and simulation. 3D soft tissue planning andprediction. Int J Oral Maxillofac Surg 29(4):250–258

3. Gladilin E, Zachow S, Deuflhard P, Hege HC (2004) Anatomyand physics-based facial animation for craniofacial surgerysimulations. Med Biol Eng Comput 42(2):167–170

4. Ng HP, Ong SH, Hu QM, Foong KWC, Goh PS, Nowinski WL(2006) Muscles of mastication model-based MR image seg-mentation. Int J Comput Assis Radiol Surg 1(3):137–148

5. Hu QM, Hou ZJ, Nowinski WL (2006) Supervised range-constrained thresholding. IEEE Trans Image Process15(1):228–240

6. Qiao Y, Hu QM, Qian GY, Luo SH, Nowinski WL (2007)Thresholding based on variance and intensity contrast. PattRecogn 40(2)596–608

7. Vasilic B, Wehrli FW (2005) A novel local thresholding algo-rithm for trabecular bone volume fraction mapping in thelimited spatial resolution regime of in vivo MRI. IEEE TransMed Imaging 24(12):1574–1585

8. Shiffman S, Rubin GD, Napel S (2000) Medical image segmen-tation using analysis of isolable-contour maps. IEEE TransMed Imaging 19(11):1064–1074

9. Ray N, Acton ST, Altes T, Lange EE, Brookeman JR (2003)Merging parametric active contours within homogeneousimage regions for MRI-based lung segmentation. IEEE TransMed Imaging 22(2):189–199

10. Pluempitiwiriyawej C, Moura JMF, Wu YJ, Ho C (2005)STACS: New active contour scheme for cardiac MR imagesegmentation. IEEE Trans Med Imaging 24(5):593–603

11. Xu C, Prince JL (1998) Snakes, shapes, and gradient vectorflow. IEEE Trans Image Process 7(3)359–369

12. Shen S, Sandham W, Granat M, Sterr A (2005) MRI fuzzysegmentation of brain tissue using neighborhood attractionwith neural-network optimization. IEEE Trans Inform Tech-nol Biomed 9(3):459–467

13. Otsu N (1979) A threshold selection method from gray-levelhistograms. IEEE Trans Syst Man Cybern 9(1):62–66

14. Hu QM, Qian GY, Nowinski WL (2005) Fast connected-component labelling in three-dimensional binary imagesbased on iterative recursion. Comput Visi Image Understand99(3):414–434

15. Leemput VK, Maes F, Vandermeulen D, Suetens P (1999)Automated model-based tissue classification of MR imagesof the brain. IEEE Trans on Med Imag 18(10):897–908

16. Fukunaga K, Hummels DM (1989) Leave-one-out proceduresfor nonparametric error estimates. IEEE Trans Patt AnalMach Intell 11(4):421–423

17. Hu QM, Nowinski WL (2003) A rapid algorithm for robustand automatic extraction of the midsagittal plane of thehuman cerebrum from neuroimages based on local symmetryand outlier removal. NeuroImage 20(4):2153–2165

18. Hou ZJ, Hu QM, Nowinski WL (2006) On minimum variancethresholding. Patt Recogn Lett 27(14):1732–1743

19. Suri JS, Liu K, Singh S, Laxminarayan SN, Zeng X, Reden L(2002) Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. IEEE TransInform Technol Biomed 6(1):8–28

20. Yang J, Staib LH, Duncan JS (2004) Neighbor-constrainedsegmentation with level set based 3-D deformable models.IEEE Trans Med Imaging 23(8):940–948