ORIGINAL PAPER

Automatic Detection of the Existence of SubarachnoidHemorrhage from Clinical CT Images

Yonghong Li & Jianhuang Wu & Hongwei Li &Degang Li & Xiaohua Du & Zhijun Chen & Fucang Jia &

Qingmao Hu

Received: 24 June 2010 /Accepted: 26 August 2010 /Published online: 9 September 2010# Springer Science+Business Media, LLC 2010

Abstract Subarachnoid hemorrhage (SAH) is a medicalemergency which can lead to death or severe disability.Misinterpretation of computed tomography (CT) in patientswith SAH is a common problem. How to improve theaccuracy of diagnosis is a great challenge to both theclinical physicians and medical researchers. In this paperwe proposed a method for the automatic detection of SAH

on clinical non-contrast head CT scans. The noveltyincludes approximation of the subarachnoid space in headCT using an atlas based registration, and exploration ofsupport vector machine to the detection of SAH. The studyincluded 60 patients with SAH and 69 normal controls fromclinical hospitals. Thirty patients with SAH and 30 normalcontrols were used for training, while the rest were used fortesting to achieve a testing sensitivity of 100% andspecificity of 89.7%. The proposed algorithm might be apotential tool to screen the existence of SAH.

Keywords Subarachnoid hemorrhage . Support vectormachine . Non-contrast CT. Atlas based registration

Introduction

Stroke is the secondmajor killer worldwide [1]. Subarachnoidhemorrhage (SAH) is the bleeding into the subarachnoidspace (SAS)—the area between the arachnoid membraneand the pia mater surrounding the brain. Though SAHaccounts for only about 5% of all strokes, it occurs at a fairlyyoung age [2]. The loss of productive life years is similar tothat for cerebral infarction. Over the past several decades, theincidence of other types of strokes has decreased but notthat of SAH. Because early treatment of SAH isassociated with improved outcomes, timely diagnosis iscritical [3]. Computed tomography (CT) is still the firstchoice for diagnosis of SAH in clinical practice. Butnumerous studies document that misdiagnosis of SAHoccurs approximately 25% of the time (12–50%), even inthe era of ready access to cranial CT scanning [4]. Asshown in [5], misinterpretation of CT in patients with SAHis a common problem. Various hemorrhage detectionsystems have been reported [6–10], but none of them are

Y. Li : J. Wu :X. Du : Z. Chen : F. Jia :Q. HuShenzhen Institutes of Advanced Technology,Chinese Academy of Sciences,Shenzhen, China

Y. Li : J. Wu :X. Du : Z. Chen : F. Jia :Q. HuThe Chinese University of Hong Kong,New Territories, Hong Kong

Y. LiInstitute of Computing Technology,Chinese Academy of Sciences,Beijing, China

Y. LiGraduate University of Chinese Academy of Sciences,Beijing, China

H. LiNingxia Medical University,Yinchuan, China

D. LiThe Third Affiliated Hospital of Inner Mongolia Medical College,Baotou, China

Q. Hu (*)Research Center for Human-Computer Interaction,Shenzhen Institutes of Advanced Technology,University Town of Shenzhen,1068 Xueyuan Boulevard,Shenzhen 518055, Chinae-mail: qm.hu@siat.ac.cn

J Med Syst (2012) 36:1259–1270DOI 10.1007/s10916-010-9587-8

suitable for the detection of SAH. These methods havetwo common assumptions: the hemorrhagic region inbrain CT being hyperdense, and asymmetric. However,the two assumptions do not always hold when SAHhappens. In fact, SAH generally scatters around SAS, andtypical CT changes of SAH can be very subtle due to theleakage of hemorrhage into cerebrospinal fluid (CSF)space as the time of first bleeding elapsed.

Although some systems [6, 7] claimed to be able todetect hemorrhage including SAH, they could detect only asmall part of SAH with asymmetry and hyper-intensity. Tobe able to handle the exclusive features of SAH (partlyhyperdense in SAS, and most with symmetry to the brain),we propose to detect the existence of SAH through twosteps: approximation of the SAS via registration of an atlaswith the patient data, and detecting abnormality in theapproximated SAS through support vector machine (SVM)based pattern classification.

Materials and methods

Materials

One hundred and twenty-nine head CT studies, including60 cases with SAH and 69 normal controls, wereretrospectively collected from five hospitals in China inthe years from 2006 to 2009. Among the 129 subjects, 75were female and 54 were male; the age (53±12) rangedfrom 17 to 78. All the studies were acquired with one of thethree types of CT scanner from GE, SIEMENS andTOSHIBA (Table 1). All images were axial obtainedparallel to the orbito-meatal line, at 120 kV and 80–413 mA. All the normal controls (69) were scanned with10 mm slice thickness throughout the brain, which is thestandard protocol used in most of Chinese hospitals foremergency studies. Among the 60 patients with SAH, 32were scanned with 10 mm slice spacing superior to theposterior fossa and 5 mm slice spacing for the rest of thehead, 28 were scanned with 9 mm slice spacing superior tothe posterior fossa and 4.5 mm slice spacing for the rest ofthe head.

We randomly chose 30 SAH patients and 30 normalcontrols as training dataset. The remaining studies wereused as testing dataset for validation. All the images weretransferred from the CT archive of workstations in hospitalsto our personal computer. The original data were all inDICOM (Digital Imaging and Communication in Medicine)format.

Method overview

The pipeline of the proposed method is illustrated in Fig. 1.It consists of five major components: image preprocessing,brain segmentation, atlas based registration, feature extrac-tion, and SVM based classification.

Image preprocessing

This is to handle gantry tilt, merge scanning series, andnoise removal. The gantry tilt technique is often used inclinical head CT scanning for reducing radiation tosensitive organs. The tilted gantry causes the qualitydegradation of constructed CT image volume. The tiltedgantry angle α is retrieved from the DICOM file header. Ifit is not zero, we use the shear transform to do thecompensation, since the tilted volume is actually a dataafter shear transform. Assume the coordinate of a voxel inthe original image is (x, y, z), and the transformedcoordinate is (x’, y’, z’). Then we have the followingequation, where T represents the transpose of a matrix.

½x;; y;; z;�T ¼ M � ½x; y; z�T ð1Þ

M is defined as follows (Eq. 2).

M ¼1 0 00 1 �D0 0 1

������

������ð2Þ

The displacement D is computed in Eq. 3.

D ¼ tanðaÞ � Voxz � ðn� 1Þ=Voxx ð3ÞWhere Voxz is the slice spacing, n is the number of axial

slices of the scan and Voxx is the voxel spacing in the XY

Hospitals Tian Tan Bei Da Shekou Ning Xia Bao Gang

Category SAH SAH SAH SAH/Normal Normal

No of studies 28 8 9 15/29 40

Scanner type Sensation 16 Aquilion LightSpeed16 LightSpeed Ultra

Manufacture SIMENS TOSHIBA GE GE GE

KVP 120 kV 120 kV 120 kV 120 kV 120 kV

Tube current 200–413 mA 200 mA 200 mA 210 mA 80–130 mA

Slice thickness 9/4.5 mm 10/5 mm 10/5 mm 10/5 mm 10 mm

Table 1 The imaging conditionsfor all the studies from fivehospitals in China

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plane. More details of gantry tilt compensation can befound in [11].

In routine SAH CT imaging, physicians often performtwo series scanning with thinner slice spacing inferior tobasal cistern regions. We have to merge the two sub-volumes into a single one three-dimensional (3D) head CTvolume with a uniform slice spacing to facilitate imagereconstruction and registration. From the header informationof original CT images, we can compute the distance of slicesfor the two separate series. Then we can derive new sliceimages using a linear interpolation based on the slice distance[11]. For example, for a CT scan with 5 mm slice spacinginferior to the posterior fossa and 10 mm slice spacingfor the rest of the head, we may get a new slice betweenthe two slices with slice spacing of 10 mm through linearinterpolation. In this way, a volume with slice spacing of5 mm is constructed.

Clinical images are often degenerated by various noisesdue to limitations of imaging hardware and reconstructionalgorithm. A number of image enhancement and denoisingtechniques have been investigated [12, 13]. To balancebetween complexity and performance, we employed a

median filter with 3×3 neighborhoods to simultaneouslyreduce noise and preserve edges.

Brain segmentation

To align the 3D head CT volume constructed in theprevious step with a brain atlas, the brain needs to beautomatically segmented from the head CT volume asmanual delineation of the 3D brain is tedious and timeconsuming. We implement a fully automatic method whichcombines fuzzy c-means (FCM) clustering andmorphologicaloperations (Fig. 2). The following steps are used in the brainsegmentation. Firstly a two-dimensional (2D) referenceimage is chosen to well represent the intensity characteristicsof the original 3D head volume. Then FCM based clusteringis used to determine the threshold of gray matter (GM)and white matter (WM). Thus a region-of-interest (ROI,the head mask including GM and WM) is determinedbased on maximum connected component and thresholds.Within the ROI the low and high thresholds aredetermined based on the spatial and intensity relationshipbetween the brain and non-brain tissues. Then the head

Input DICOM Series

Gantry tilt compensation Noise removalSeries merging

Maximum connected component

Fuzzy C-means clustering

Image preprocessing

Binary brain mask Propagate brain maskChoose reference

image

B spline transformMutual information

metric

ICBM Atlas

Brain segmentation

Atlas based registration

Features normalization

SVM based classification

Feature extraction

SAS model

LBFGS optimization

ROI delineation

Fig. 1 Pipeline of the proposed method for SAH detection

J Med Syst (2012) 36:1259–1270 1261

volume is binarized using the two thresholds. Connectedcomponents and distance criteria are combined to get thebrain candidates, which are further refined throughpropagation of brain masks to find the final brain mask.More details can be found in [14].

Atlas based registration

The shape and size of different brains may vary greatly. Tolocate the SAS of each subject, a possible method is anatlas based registration scheme.

Atlas construction

To the best of our knowledge, there is no SAS atlasavailable. As the SAS is in CSF space, the brain atlas withCSF region segmented can be explored. We choose theICBM452 T1 atlas [15] from the international consortium

for brain mapping (ICBM). The ICBM probabilistic atlas isalso used for the delineation of CSF space. This probabilisticatlas has been classified as GM, WM and CSF. These twoatlases share the same coordinate system (Fig. 3). The volumesize of these two atlases is 149×188×148, and the voxel sizeis 1 mm in X, Y, and Z directions.

For better distinguishing SAH in different CSF regionsof brain CT sans, we manually define seven subregions inthe ICBM probabilistic CSF atlas based on brain anatomicalstructures related to the SAS. These subregions are: left/rightsylvian cisterns, basal cistern, ambient cistern, and superior/anterior/posterior longitudinal fissures (Figs. 4, 5 and 6). Theseven subregions have been delineated manually by anexperienced physician. For distinguishing each subregion inthe subsequent image registration, we construct an 8-bit labelimage with the same dimension as the ICBM 452 T1 atlas.In the label image, a unique number in the range of 0 and255 is assigned for every voxel corresponding to each of the

Fig. 3 The three orthogonalviews of ICBM 452 T1 atlas(top) and ICBM probability CSFatlas (bottom) are shown incomparison mode. The twoatlases share the same coordi-nate system. They have samenumber of voxels and voxelsizes in X, Y and Z directions

Fig. 2 The brain is segmented from original head CT images (a) by combining FCM clustering and morphological operations. The head mask (b)is determined based on maximum connected component and thresholding. The final segmented brain is shown in (c)

1262 J Med Syst (2012) 36:1259–1270

seven subregions. For instance, the subregion of left sylviancistern is assigned 100, while the subregion of basal cisternis assigned 50. Each subregion can have 1 or more 2Dregions in 1 or more axial slices. Each of these 2D regions isa candidate SAH region and is called a ROI for SAH, or ROIin short.

Image registration

We explore a non-rigid B-spline registration based on thegeneral ITK image registration framework [16]. B-splinesare computationally attractive since the change of a control

point only affects the transformation within a localneighborhood of the point. Moreover, the multi-resolutionscheme can be incorporated for faster computation.

The registration is accomplished in two steps. First, thesegmented brain CT volume is set as reference image, a B-spline based deformation field is computed iteratively fromthe atlas to individual brain. The deformation is modeled oncubic B-splines because of their computational efficiency,smoothness and good local control. A hierarchical registrationscheme is used, starting from an affine transform, and endingwith a deformable transform. This scheme integrates theglobal adaptive capability and local deformable capability ofB-spline transforms. In the second step, the deformation fieldresulting from the first stage is applied to the labeled atlaswhich warps ROIs onto the individual subject using nearestneighbor interpolation.

To be tolerant to errors of atlas registration due to largeslice spacing along Z direction and variation in the size ofindividual SAS, we enlarge each ROI by morphologicaldilation to the binary ROI mask. The structure element isoperationally set as 5×5×3. Six enlarged ROIs afterbrain image registration are shown in Fig. 7. We alsoillustrate the image registration in Fig. 8 (brain CT andatlas before registration) and Fig. 9 (brain CT and atlasafter registration).

Feature extraction

To detect whether SAH is present for a suspect patient, itis necessary to examine all ROIs of the head CT. Foreach ROI, its features are extracted to be used forclassifying the ROI as “SAH being present” or “SAHbeing absent.” As the shape and size of ROIs varygreatly, the features we used should be independent ofthem. The following features including the mean gray

Fig. 6 A ROI of the superior longitudinal fissure in the subarachnoidspace in a typical slice of ICBM 452 T1 atlas

Fig. 4 ROIs of the basal cistern (yellow) and ambient cistern (orange)in the subarachnoid space in a typical slice of ICBM 452 T1 atlas

Fig. 5 ROIs of the left/right sylvian cistern (purple/red), anteriorlongitudinal fissure (green) and posterior longitudinal fissure (blue) inthe subarachnoid space in a typical slice of ICBM 452 T1 atlas

J Med Syst (2012) 36:1259–1270 1263

value (M), variance (V), entropy (EP) and energy (EG) areemployed.

M ¼ 1

N � L

XNi¼1

xi ð4Þ

V ¼ 1

N

XNi¼1

xi � xð Þ2 ð5Þ

EP ¼ � 1

K

XLj¼0

pj log pj ð6Þ

EG ¼ 1

L2XNi¼1

x2i ð7Þ

where N is the number of voxels in the ROI, L themaximum number of gray scales which is 255 for an 8-bitimage, xi(i=1,...,N) the gray value of the ith voxel, pj theprobability of gray scale j in the ROI, K the value ofmaximum entropy related to the images which is 8 for 8-bitgray image according to the theory of maximum entropy[17]. It can be shown that all the features defined by Eqs. 4to 7 are normalized to the range of [0, 1], which facilitatesthe subsequent SVM based classification.

SVM based classifier

SVM basics

SVM is a supervised method used for pattern classification.The idea is to find the optimal hyperplane that bestseparates vectors from both classes by mapping the vectorsinto a high-dimensional space [18, 19]. There are bothlinear and nonlinear SVMs.

Linear SVM Label the training data xi as yi, we get {xi,yi},i=1,⋯,l,yi ∊ {−1,1},xi ∊ Rd. Suppose the hyperplane

Fig. 7 Six enlarged ROIs after brain image registration of the: left sylvian cistern (a), right sylvian cistern (b), basal cistern (c), superiorlongitudinal fissure (d), anterior longitudinal fissure (e), and posterior longitudinal fissure (f)

1264 J Med Syst (2012) 36:1259–1270

which separates the positive from the negative samples has thefollowing form wTx+b=0, where w is normal to thehyperplane. For the linearly separable case, SVM computesthe optimal separating hyperplane with the maximum

margin by solving the following optimization problem:minimize ‖w‖2, subject to yi wTxi þ bð Þ � 1 � 0 8i. If thetwo classes are nonlinearly separable, slack variables ξi areintroduced. SVM finds the optimal hyperplane by mini-

Fig. 8 The three orthogonalviews of brain CT and ICBM452 T1 atlas are shown as acheckerboard before registra-tion. There are large misalign-ment and deformations betweenthem both in the boundary andbrain ventricle systems

Fig. 9 The three orthogonalviews of brain CT and ICBM452 T1 atlas are shown as acheckerboard after registration.The large misalignment anddeformations are almostinvisible

J Med Syst (2012) 36:1259–1270 1265

mizing the following equation subjected to yi wTxi þ bð Þ�ð1� xiÞ � 0 8i:

JðwÞ ¼ 1

2wk k2 þ C

Xl

i¼1

xi ð8Þ

where the constant C > 0 is a parameter to be chosen bythe user which is a trade-off between the maximization ofthe margin and minimization of the classification error.

Nonlinear SVM To formulate non-linear classifiers,researchers suggested a way by applying kernel mappingtechnique to maximum-margin hyperplanes [20]. Theresulting algorithm is similar to that of linear SVM, exceptthat every dot product is replaced by a non-linear kernelfunction. The most commonly used kernel functions areGaussian radial basis functions (RBFs) shown in Eq. 9.

K xi; xj� � ¼ exp �g xi � xj

�� ��2� �ð9Þ

SVM based classification

In our SAH detection system, the seven subregions aredivided into two groups: three with longitudinal fissure asG2 and four without as G1, since the longitudinal fissure isclose to the falx cerebri which shows hyperintensity innormal CT image. These two groups are trained andclassified independently. The training samples for G1 groupand G2 group are specified by two experienced physicians.

For each ROI in a given slice, four features arecomputed. For every ROI, a label y is assigned +1 for“SAH being present” if there is SAH in the ROI, otherwiseit is assigned −1 for “SAH being absent”.

The kernel function in an SVM plays a central role ofimplicitly mapping the input vector into a high-dimensionalfeature space. We explore Gaussian kernel function (Eq. 9),because of its good general performance and small numberof parameters. With this kernel function, only two param-eters need to be determined in the training of SVM model:C and γ, where γ>0 is a constant that defines the kernel widthand C is the penalty parameter of the error termwith a larger Ccorresponding to assigning a higher penalty to errors.

We use LIBSVM, an open source SVM package [21] tooptimize these parameters. A “grid-search” strategy on Cand γ is used to find the best pairs. To prevent over-fitting

during SVM training, we employ a k-fold cross-validationtechnique. Generally, a smaller portion of the data shouldbe used as validation set (k>2) because the validation set isused merely to set a single global property of the classifierrather than the large number of classifier parameters learnedusing the training set [22].

The SVM model is trained offline, and is applied onlineto classify each ROI in the testing CT images as “SAHbeing present” or “SAH being absent.” On a patient level, apatient is classified as with SAH if there is at least one ROIbeing classified as “SAH being present”.

Results

Performance in brain segmentation

To quantitatively evaluate the performance of brain seg-mentation, a radiologist manually segmented 30 clinicalcases (15 SAH and 15 normal controls) which were randomlyselected from our dataset to be used as gold standards. Themeasures we employed are Jaccard similarity index, false

Table 3 Summary of the result in the training cases according to thestatus of SAH in each of the predefined seven subregions

ROI TP FN TN FP Total

Left sylvian cisterns 45 12 340 21 418

Right sylvian cisterns 42 16 327 28 413

Basal cistern 31 12 196 7 246

Ambient cistern 47 16 301 9 373

Superior longitudinal fissures 40 12 459 39 550

Anterior longitudinal fissures 35 13 379 13 440

Posterior longitudinal fissures 47 38 291 32 408

Total 287 119 2,293 149 2,848

TP for true positive (an SAH ROI is classified as SAH present), FNfor false negative (an SAH ROI is classified as SAH absent), TN fortrue negative (a normal ROI is classified as SAH absent), FP for falsepositive (a normal ROI is classified as SAH present)

Table 4 Summary of the result in the testing cases according to thestatus of SAH in each of the predefined seven subregions

ROI TP FN TN FP Total

Left sylvian cisterns 26 9 405 8 448

Right sylvian cisterns 50 18 382 32 482

Basal cistern 37 6 215 12 270

Ambient cistern 68 19 322 6 415

Superior longitudinal fissures 25 14 448 31 518

Anterior longitudinal fissures 40 23 446 12 521

Posterior longitudinal fissures 79 55 296 40 470

Total 325 144 2,514 141 3,124

Table 2 Number of ROIs for the training set and testing set

Group Number in training set Number in testing set

G1 1,450 1,615

G2 1,398 1,509

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positive rate (FPR), and false negative rate (FNR). TheJaccard similarity index, also known as the Tanimotocoefficient, is defined as the intersection of the two setsdivided by the size of their union (Eq. 10), where A and B arethe two sets corresponding to the manual segmentation andautomatic segmentation results, respectively.

JðA;BÞ ¼ A \ Bj j A [ Bj j= ð10ÞThe FPR and FNR are respectively the number of false

brain voxels and the number of false non-brain voxels, alldivided by the number of brain voxels.

Visually the segmented brains of all of the 129 head CTscans were good. For the 30 manually segmented cases, ourautomatic brain segmentation yielded an average Jaccardindex of 0.915±0.032, average FPR of 5.4%, and averageFNR of 11.0%.

Performance in SVM based SAH detection

For the 60 training subjects and 69 testing subjects, thenumber of ROIs included in group G1 and group G2 forthe training set and testing set were given in Table 2. Inthe training stage of SVM, we chose the search grid range

of [2−5, 212] and [2−4, 26] for C and γ respectively. Duringour experiment in the k-fold cross-validation, we tried 2-fold,3-fold, 5-fold and 10-fold to find that the best accuracy wasachieved at 3-fold cross-validation. The possible reason mightbe the small number of degrees of freedom in the classifiercompared to the number of training samples. Under thiscondition, the predicted generalization error was relativelyinsensitive to the choice of k. The best (C, γ) pair was (8, 8)for G1 group with the cross-validation rate 92.4%, andthe best (C, γ) pair was (256, 16) for G2 group with thecross-validation rate 88.4% on the ROI level.

The performance of our method was evaluated bysensitivity and specificity on both ROI and patient levels.The ROI level provides the detail information of thedistribution of SAH on different brain cisterns. On theother hand, the performance on patient level is of moreclinical relevance than the performance on ROI level indetecting SAH. The reason is that the clinical decisionsdepend on the presence or absence of SAH rather thequantity of ROIs.

Sensitivity is the ratio between the number of thecorrectly detected SAH cases/ROIs and the number ofSAH cases/ROIs, while specificity is the ratio between thenumber of correctly detected normal cases/ROIs and thenumber of normal cases/ROIs.

For the 60 training cases, there were 406 ROIs (G1 221and G2 185) with SAH (for every predefined subregionsuch as left sylvian fissure, it usually has at least threeslices). On ROI level, the overall sensitivity and specificitywere 70.7% (287/406) and 93.9% (2,293/2,442), respectively(Table 3).

Table 5 The sensitivity and specificity of the proposed method forthe testing dataset

Sensitivity Specificity

ROI level 69.3% 94.7%

Patient level 100% 89.7%

Fig. 10 Screen capture ofgraphical user interface of theproposed system. The enlargedaxial slice is shown with the redenclosed curve to represent theROI with SAH

J Med Syst (2012) 36:1259–1270 1267

For the 69 testing cases, there were 469 ROIs (G1 233and G2 236) with SAH. On ROI level, the sensitivity andspecificity were 69.3% (325/469) and 94.7% (2,514/2,655),respectively (Tables 4 and 5).

On the patient level, the sensitivity and specificitywere 96.7% (29/30) and 90% (27/30) respectively for thetraining cases. The system achieved sensitivity of 100%(30/30) and specificity of 89.7% (35/39) for the testingcases.

Figure 10 showed the graphic user interface (GUI) of oursystem implemented with C++ under windows XP. Someresults of the detected SAH with ROI enclosed in red colorwere shown in Fig. 11. A typical SAH slice withhemorrhages symmetrically visible in both left and rightsylvian fissures was shown in Fig. 11(a). It was correctlydetected with our method. Sometimes, hemorrhage onlyoccupies part of the ROI as shown in Fig. 12. In thissituation, our method might fail to detect the existence of

Fig. 11 The SAH distributionon CT is generally symmetrywhich can not be correctlydetected by existing ICH detec-tion method based on symmetry.Three slices of patients withtypical SAH are shown. Foreach column from (a) to (c), theleft picture shows the originalhead CT, while the right oneshows the detection result of ourmethod. The ROI is enclosed inred when its classification resultis positive

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SAH. However, as other ROIs of the same patient withSAH could be classified correctly, this ROI level misclas-sification will just decrease the sensitivity at the ROI levelbut not at the patient level.

Discussion

Detection of SAH

At the acute stage, SAH is typically seen as a “white” areaof high signal intensity compared with surrounding CSF.The presence or absence of SAH depends on the imagefeatures of CSF in the area of SAS (our seven predefinedsubregions). As a kind of hemorrhagic stroke, SAH showssimilar features (high intensity) in CT to intracerebralhemorrhage (ICH). However, the method for detection of

ICH is not well suitable for SAH because of different blooddistribution and the circulation of CSF. The CSF circulationredistributes focal SAH into other parts of the SAS andventricular systems. The resulting dilution reduces conspi-cuity on CT. These present great challenges to thedevelopment of automatic detecting method based on lowlevel image processing.

To the best of our knowledge, there are no other reportson the automatic detection of SAH from clinical head CTimages. The main idea of the proposed method is to locatethe SAH distribution with knowledge incorporated by anSAS atlas. It is convenient for clinicians to make ajudgment given the SAH ROIs being highlighted.

Our method has a relatively low sensitivity on ROI level.The main reason is the decreasing hyperdense distributionfor SAH patients in sub-acute stage and the ROIs in theslice far from the slice corresponding to the source of

Fig. 13 The scattered small amount of hemorrhage was misclassi-fied as normal (a), normal longitudinal fissure was misclassified asSAH present due to the very variable intensity distribution along the

longitudinal fissure (b), and the normal sinus was misclassified asSAH present due to the registration error which misinterpreted theROI in red as a ROI of the ambient cistern (c)

Fig. 12 The SAH may occupyonly small part of predefinedROI. As shown in right part ofabove figure, only part of thebasal cistern have “white”hyperintensity signal. Theproposed method failed underthis condition

J Med Syst (2012) 36:1259–1270 1269

bleeding. Fortunately, our method could detect at least twoSAHROIs for 29 out of 30 patients with SAH. The specificityat the patient level is lower than that at the ROI level which isdue to that the ratio of misclassified normal ROIs being lowerthan the ratio of misclassified normal subjects.

The only SAH case which was misclassified as normalwas due to scattered small hemorrhage (Fig. 13a). Themajor reasons for a normal ROI being misclassified were 1)variable intensity distribution along the longitudinal fissuredue to sinuses (Fig. 13b), and 2) errors of registrationbetween the CSF atlas and the patient data (Fig. 13c).

Limitations of our study and further improvement

The image noise has been simply processed using a medianfilter which might be enhanced according to noise natures.

Another problem is related to the time efficiency of atlasbased registration. The average time cost is about 3 min for theregistration. The recent progress on graphics processing unitsin image registration may help to speed up the registration.

Further improvement may include identification ofpossible sites of aneurysm for SAH caused by the ruptureof aneurysms. Possible clues presented on the CT can bethe size and shape of a localized clot and the distributionpattern of hemorrhages.

Conclusion

In this paper, a method to detect the existence of SAH onclinical non-contrast head CT was proposed combining anatlas based registration and SVM based classifier to achievea sensitivity of 100% and specificity of 89.7% at the patientlevel for 69 testing cases. It may be a potential tool toscreen the suspected SAH clinically. Future work includesenhancing the accuracy, testing on more clinical data, puttinginto practice the study in various application areas such asin emergency departments and stroke units, and improvingregistration accuracy with constraints on segmented lateralcerebral ventricle.

Acknowledgment The authors would greatly thank Dr. ChengWang (Peking University Shenzhen Hospital) and Dr. Zhidong Yuan(Peking University Shenzhen Hospital) for providing the clinicaldataset. This work was supported by National Natural ScienceFoundation of China under Grant Nos. of 60803108 and 30700165.

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