Automatic identification of intracranial hemorrhage innon-contrast CT with large slice thickness for trauma cases

Pragnya Maduskara and Mausumi Acharyyaa

a Siemens Information Systems, 84 Keonics, Electronic City, Bangalore, India

ABSTRACT

In this paper we propose a technique for automatic detection of intracranial hemorrhage (ICH) and acuteintracranial hemorrhage (AIH) in brain Computed Tomography (CT) for trauma cases where no contrast can beapplied and the CT has large slice thickness. ICH or AIH comprise of internal bleeding (intra-axial) or external(extra-axial) to the brain substance. Large bleeds like in intra-axial region are easy to diagnose whereas it canbe challenging if small bleed occurs in extra-axial region particularly in the absence of contrast. Bleed regionneeds to be distinguished from bleed-look-alike brain regions which are abnormally bright falx and fresh flowingblood. We propose an algorithm for detection of brain bleed in various anatomical locations. A preprocessingstep is performed to segment intracranial contents and enhancement of region of interests(ROIs). A number ofbleed and bleed-look-alike candidates are identified from a set of 11 available cases. For each candidate texturebased features are extracted from non-separable quincunx wavelet transform along with some other descriptivefeatures. The candidates are randomly divided into a training and test set consisting of both bleed and bleed-look-alike. A supervised classifier is designed based on the training sample features. A performance accuracy of96% is attained for the independent test candidates.

Keywords: Intracranial hemorrhage, feature extraction, quincunx wavelet transform

1. INTRODUCTION

Intracranial hemorrhage (IH) occurs when a diseased blood vessel within the brain bursts, allowing blood toleak inside the brain. The sudden increase in pressure within the brain can cause damage to the brain cellssurrounding the blood. Rapid increase in blood amount may cause sudden buildup in pressure which in turn canlead to unconsciousness or death. IH might extend into ventricles in association with deep, large hematomas.Intracranial hemorrhage usually occurs in selected parts of the brain, including the basal ganglia, cerebellum,brain stem, or cortex. Intracranial hemorrhage accounts for 10 to 15 percent of all cases of stroke and is associatedwith the highest mortality rate, with only 38 percent of affected patients surviving the first year.1,2

IH can be categorized into intraaxial and extraaxial hemorrhage based on bleeding inside or outside thebrain substance. Intraaxial hemorrhage can be further classified into cerebral hemorrhage, and intraventricularhemorrhage (IVH) based on the exact anatomical location of bleeding. Extraaxial hemorrhage is classifiedaccording to the anatomical layer of meninges where bleeding occurs, namely extradural hemorrhage (EDH),subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH).

In moderate to severe head trauma cases, non-contrast-enhanced CT scanning is the most useful and preferredchoice for initial imaging technique. Reasons behind this choice is CT scans being rapid and its ability to detectacute hemorrhage, cerebral swelling, evidence of elevated intracranial pressure and pneumocephalus. Freshintracerebral blood typically appears hyperdense on CT due to the high protein concentration and its high massdensity. However, occasionally acute intracerebral hematoma can appear isodense or even hypodense on CT.CT scanning is able to demonstrate the size and location of the Intracranial Hemorrhage accurately. Also it cansuggest potential causes such as tumor, vascular malformation, or aneurysm.1

Since past few years, computer-aided diagnosis (CAD) has become one of the major research areas in medicalimaging and diagnostic radiology. The basic concept of CAD is to provide a computer output as a second opinionto assist radiologist’s image readings. The goal of CAD is to improve the quality and productivity of radiologist’s

Further author information: (Send correspondence to Mausumi Acharyya) E-mail: acharyya.mausumi@siemens.com

Medical Imaging 2009: Computer-Aided Diagnosis, edited by Nico Karssemeijer, Maryellen L. GigerProc. of SPIE Vol. 7260, 726011 · © 2009 SPIE · CCC code: 1605-7422/09/$18 · doi: 10.1117/12.812276

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tasks by improving the accuracy and consistency of radiological diagnoses and also by reducing the image readingtime. The general approach for CAD is to find the location of a lesion and also to determine an estimate of theprobability of a disease; these correspond to CAD for detection of a lesion and CAD for differential diagnosis.CAD can be applied to all imaging modalities, including projection radiography, CT, MRI, ultrasound and nuclearmedicine imaging, used for all body parts such as the skull, thorax, heart, abdomen and extremities, and all kindsof examinations including skeletal imaging, soft tissue imaging, functional imaging and angiography. However,the majority of CAD schemes developed in the past include the detection of breast lesions on mammogram, thedetection of lung nodules in chest radiographs and thoracic CT, and the detection of polyps in CT colonography.

There are very few CAD systems available in the field of automated detection of IH. One CAD systemnamed Stroke Suite supports the evacuation of hemorrhage by thrombolytic treatment. It aims at progressionand quantification of blood clot removal. The clot is automatically segmented from CT time series, its volumemeasured, and displayed in 3D along with a catheter.3 Another CAD system has been developed for diagnosisand automatic detection of increased density of acute intracranial hemorrhage in CT images.4–6 It extracts highdensity intracranial contents and evaluate these candidates as true positive using morphological and positionalfeatures evaluated by fuzzy classifier. 95 percent sensitivity per patient have been reported on detection ofsmall acute intracranial hemorrhage on brain CT.7 Few techniques exist for quantitative analysis i.e. volumedetection and measurement of affected region due to IH.8,9 None of the existing techniques works well forall types of hemorrhage specially very small and isodense/hypodense intracranial hemorrhage. This leavesscope for development of more sophisticated and efficient algorithms for automatic identification of intracranialhemorrhage.

We propose an automated technique which can provide accurate identification of regions of intracranialhemorrhage and can clearly discriminate between bleed and bleed-look-alike structures in head trauma non-Contrast CT Cases where accurate diagnosis is crucial wherein having minimal false positive rate so as to provehigh sensitivity of the algorithm over existing ones.

2. METHODS

The algorithm has been divided into a sequence of steps which are explained below.

2.1 Segmentation of Intracranial Contents

This step involves segmentation of intracranial contents like brain and cerebral spinal fluid (CSF) containingspaces. Firstly brain is segmented into - air/CSF, grey/white matter, bone regions using a global thresholding.The air/CSF is removed by thresholding; the value of which is chosen from the histogram of the air/CSF region.The bone region is removed by selecting a threshold from the histogram of the pixels at the boundaries ofgrey/white matter and bone clusters. We are left with only grey/white matter. Small components like: scalptissues, structures in the orbits and sinuses are removed by connected component labeling and morphologicaloperations. Fig. 2 shows the original CT slice and slice after segmentation of intracranial contents.

2.2 Preprocessing

Preprocessing of intracranial contents in brain CT involves a series of steps - cupping artifacts adjustment,gamma mapping, sigmoid mapping based contrast enhancement and scale-space smoothing which enhances theregion of interest to facilitate more accurate categorization of bleed candidates.

The following steps are devised on the slices

• Adjustment for cupping artifacts - CT cupping artifacts increase intensity in regions immediately adjacentto the skull and is inversely proportional to distance from the soft tissue-bone interface. This is shownas bright regions at the skull boundary. To remove this artifact a series of five morphological erosionsare performed on the binarized image of the intracranial contents successively using a circular structuringelement on each slice. Differences of the successively eroded images are computed and averaged for allnon-zero voxels. Any intensity value above this average value is eliminated from the volume to get rid ofthe artifact.7 The process is illustrated in Fig. 1.

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• Gamma Correction - Applied this standard enhancement technique both to compensate for nonlinearitiesin the image and to increase the apparent dynamic range. The output is related to the input by raising itto the factor (1/gamma) properly scaled so that the end points are not adjusted. A value of gamma < 1.0is chosen for the image so that it is darkened, with the biggest effect happening for the dark (low input)pixel values.

• Contrast Enhancement - Since CT is low contrast, contrast is increased by applying a sigmoid-shapedmapping function to the input pixels. To increase the contrast, the pixels above some mid-value arelightened while those below are darkened.

• Scale-space smoothing - Anisotropic Diffusion10 is used for smoothing such that Diffusion coefficient ischosen to vary spatially so as to encourage intra-region smoothing in preference to inter-region one.

It = div(c(x, y, t)∇I) = c(x, y, t)ΔI + ∇)c.∇I (1)

where, It - brightness value at time/space t; c - conduction coefficients; div - divergence; ∇ - Gradientand Δ - Laplacian operator w.r.t space variables (x, y, z). To encourage smoothing within a region inpreference to smoothing across boundaries the conduction coefficient is set at 1 in the interior and 0 at theboundaries. Blurring then take place separately in each region with no interaction between regions whileregion boundaries remain sharp. The final preprocessed image is shown in Fig. 2.

Figure 1. Top Left : A typical brain slice Top Right: corresponding intensity profile along a row shown by dotted lines;Bottom Left: Brain slice after removal of cupping artifactBottom Right: corresponding intensity profile along the samerow.

2.3 Candidate Generation

Due to lack of adequate number of trauma CT cases, we could not perform a case wise analysis, instead weidentified several bleed and bleed-look-alike candidates for further processing. The ground-truth is generatedby a radiologist which is stored in our database as a representative point. We have considered all the bleed

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Figure 2. A typical brain slice (Left:) after intracranial segmentation (Middle:) and after the final preprocess step (Right:).

candidates in the database. Similarly, we have manually selected bleed-look-alike candidates which are mainlybright falx, calcification and fresh flowing blood regions. A 3D semi-automatic segmentation is applied to segmentout the bleed as well as bleed-look-alike containing the candidate.

This 3D semi-automatic segmentation technique performs a number of steps on the CT volume startingfrom the slice which contains representative point. These steps mainly consists of finding out appropriate bleedintensity range and then refining the segmented region using cluster overlap and distance criteria across slices.

Required steps are explained below:

• Bleed intensity range calculation - We take the intensity value of a representative (seed) point (rx, ry, rz)as Sval. A range R of intensity values is defined taking intensity value of seed (Sval) as center. Intensityis varied between this range to calculate the number of voxels belonging to each range. Finally bleedintensity range is considered to be the range in which we get maximum number of voxels. Fig. 3 shows arepresentative slice containing seed point (representative point) and its gray map for the bleed correspondingto the seed point. This gray map shows labels from 1 to R which in turn is the number of voxels for eachintensity range. From this gray map we will select the range or label corresponding to maximum numberof voxels.

• Low and high threshold calculation for bleed - Label corresponding to maximum area or maximum numberof voxels (Lmax−area) in representative slice gives the range of intensity values for bleed. Let the labelselected for representative slice be lr. Low and high threshold for bleed intensity are computed as follows:

lowthresh = Sr − (lr + σ)

highthresh = Sr + (lr + σ)

where, Sr is seed value for representative slice and σ is some small intensity tolerance.

• 3-D Binarized Map of bleed - Using the above found thresholds we binarize the original CT volume V (x, y, z)to get the 3-D binary map binaryMap(x, y, z) (Fig. 4).

binaryMap(x, y, z) ={

1 if lowthresh ≤ V (x, y, z) ≤ highthresh

0 otherwise

• Segmentation on representative Slice - It is evident from Fig. 4 that apart from the actual bleed there areother components present as well. To get rid of these regions we perform connected component analysis onthe representative slice in the 3D binary map (binaryMap) and keep only the component containing seedpoint and making other components zero. This gives the 2D segmented bleed corresponding to the seedpoint in binaryMap (Fig. 5).

• 3D Finer Segmentation based on Cluster Overlapping and Distance Map criteria - Using binaryMap andthe representative slice S as reference, voxels corresponding to bleed can be segmented out from all the

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Figure 3. Representative Slice Left: corresponding gray map Right:. Gray map corresponds to the Bleed shown bycross-wire.

Figure 4. Representative Slice Left: corresponding binary map Right:. Binary map corresponds to the Bleed shown bycross-wire.

slices to get 3D bleed segmentation. This slice-wise analysis is done in both the directions starting fromslice S till Zstart and starting from slice S till Zend where Zstart and Zend are start and end slices of CTvolume. In each slice we will select the connected components overlapping with the bleed cluster on sliceS. We have also calculated a threshold empirically for optimum centroid distance between any cluster andreference cluster on slice S. Clusters matching the distance threshold criteria are finally segmented as bleedregion. Typical slice containing bleed and its final bleed segmentation is shown in Fig. 6.

2.4 Feature Extraction

Texture based features are extracted from non-separable quincunx wavelet transform.11 The nonseparable quin-cunx sampling is represented by the sampling matrix Dq. Fig. 7 illustrates quincunx sublattices of Z2 in thespatial and frequency domains.

Dq =[

1 1−1 1

](2)

Quincunx transform (QT), uses nonseparable and non-oriented filters, followed by the nonseparable samplingrepresented by the matrix Dq in (2). At each level, the input image is decomposed with the multiresolution scale

Figure 5. Representative Slice Left: and its bleed segmentation Right:. Segmentation corresponds to the Bleed shown bycross-wire.

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Figure 6. Typical slice containing bleed Top Left: and Bottom Left: and corresponding bleed segmentation Top right: andBottom Right:. Bleed shown by cross-wire.

Figure 7. Left: Quincunx lattice in the spatial and frequency domains. Right: Division of spectrum of quincunx transform.

factor√

2 , resulting in one low-resolution sub-image and one non-oriented wavelet sub-image. The idealizedpartitions of the frequency domain after the separable and quincunx decomposition are illustrated in Fig. 7.

For the texture characterization we use a 2D diamond shaped non-separable filter derived from the 1Dbiorthogonal spline basis in12 using McClellan transform.13 Biorthogonal spline filters are short with almostequal length for the low and highpass filters and give the best tradeoff between the filter lengths and theregularity of corresponding wavelet. The filter coefficients for the 2D biorthogonal diamond shaped filter is givenin Table 1, the surface plots of the lowpass and highpass filters in the spatial domain are shown in Fig. 8. Severaldescriptive features like energy, eccentricity, orientation, average absolute deviation from mean, channel varianceare derived from the above wavelet transform to characterize bleed texture.

Table 1. Filter Coefficients for the 2-D Biorthogonal Diamond-Shaped Filter Used

h0 0.65 0.125 -0.0125 0 0 -0.025 0 0 0

h̃0 37/56 -43/320 -3/224 3/2240 0 -3/112 9/2240 0 0

2.5 Classification

We have randomly divided bleed and bleed-look-alike candidates into train and test set. Features computedfor these candidates will form a feature set which is used as input for training the classifier. We have used

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Figure 8. Surface plot of low-pass (left) and high-pass (right) 2-D filter belonging to biorthogonal spline family. Thecoefficients of this filter correspond to the filter family in Table 1.

Figure 9. A typical slice containing Left: bleed and calcification (bleed-look-alike) Middle: bleed and bright falx (bleed-look-alike) Right: flowing blood (bleed-look-alike). Bleed regions with dashed contour and bleed-look-alike regions withsolid contour. All these regions are correctly identified by the classifier.

Discriminant analysis function14 for this purpose. Discriminant analysis uses training data to estimate theparameters of discriminant functions of the predictor variables. Discriminant functions determine the boundaries,in predictor space, between various classes. The resulting classifier is then said to discriminate among the classes(the categorical levels of the response), based on the predictor data. A diagonal quadratic discriminant function ischosen. Quadratic discrimination fits the multivariate normal (MVN) densities with diagonal covariance matrixestimates stratified by group.14 Performance of algorithm is reported based on classifier output on test set.

3. RESULTS AND DISCUSSION

The data set consists of 11 trauma head CT cases on GE scanner having average slice thickness of 2.5. Groundtruth consists of 85 bleed representative points which are split into 45 for training and 40 for testing. Alsowe have manually labeled 75 bleed-look-alike points which are divided into 45 in train set and 30 in test set.The classification accuracy for bleed and bleed-look-alike is 96%. Out of 40 bleed candidates 38 are identifiedcorrectly with 2 misclassification, whereas 2 bleed-look-alike are identified falsely as bleed and the rest areidentified correctly.

A typical CT image containing bleed and bleed-look-alike regions and highlighted classifier output is shownin Figure. 9. Regions marked by dashed boundary indicates the regions identified by classifier as bleeds whereasregions with solid boundaries indicate those which have been identified by classifier as bleed-look-alike. Resultsshow that classifier is able to discriminate between a bleed and bleed-look-alike (bright falx, calcification, flowing-blood). Hence, the feature set is capable of giving strong discrimination between bleed and bleed-look-alikestructures.

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