Hydrocephalus, characterized by increased fluid in the cerebral ventricles, is traditionally evaluated by a visual assessment of serial CT scans. The complex shape of the ventricular system makes accurate visual comparison of CT scans difficult. The current research developed a quantitative method to measure the change in cerebral ventricular volume over time. Key elements of the developed framework are: adaptive image registration based on mutual information and wavelet multiresolution analysis; adaptive segmentation with novel feature extraction based on the Dual-Tree Complex Wavelet Transform; volume calculation. The framework, when tested on physical phantoms, had an error of 2.3%. When validated on clinical cases, results showed that cases deemed to be normal/stable had a calculated volume change less than 5%. Those with progressive/treated hydrocephalus had a calculated change greater than 20%. These findings indicate that the framework is reasonable and has potential for development as a tool in the evaluation of hydrocephalus.
Hydrocephalus results from excessive accumulation of cerebrospinal fluid, leading to enlargement of the cerebral ventricles. The condition is commonly evaluated by visual comparison of serial CT scans of the head. However, the complex shape of the ventricular system and the differences in the angulation of slices combined with slight differences in positioning of the head from one CT study to the next can make direct visual comparisons of serial imaging studies difficult and of limited accuracy. This makes the quantitative assessment of the volume change desirable.
Earlier methods for quantitatively assessing ventricular volume have included the diagonal ventricular dimension [
This paper describes a novel framework to measure the change in the volume of the ventricles using CT scans taken at two separate times. The method involves registering the two CT image sequences to be compared, automatically segmenting the ventricles in all the image slices, and calculating a volume change from the results. The framework was validated and verified on both physical phantom models and clinical data.
Image registration is used to align the second set of CT images with the first, thus making the volume calculations consistent, reducing the error caused by the partial volume effect and improving the accuracy of the calculated change in volume. The differences in angulation of the slices combined with the slight differences in positioning of the head from one CT to the next is referred to in this paper as the displacement of the human head. A number of image registration techniques have been described previously, including landmark techniques [
Image segmentation is the process of separating out mutually exclusive homogeneous regions of interest and in this research is used to isolate the ventricles in preparation for the volume calculation. In this paper, the focus is on a variation of the watershed automated segmentation method. The watershed method suffers from an oversegmentation problem, and a number of methods proposed in the literature to overcome the problem have had varying success. Soille [
Once the images are registered and the ventricles are segmented, the framework calculates the change in volume. To validate the method developed in this study, physical phantoms of the brain and cerebral ventricles were constructed, using agar and water to simulate brain tissue and cerebrospinal fluid, respectively. The volume of the phantom ventricles was measured directly and was then calculated using the method described in this paper. Clinical data with known outcomes were also used to validate the results.
In Section
Algorithm framework.
The method described in this research uses an image registration technique to align the image slices of the CT scan taken at a time,
Given a clinical case with two different CT scans of the head taken at times
If
The registration method used in this research is a wavelet-based technique that maximizes the mutual information in the two image sets. The mutual information,
The mutual information registration algorithm assumes that the images are geometrically aligned by the rigid transformation
Because displacement of the human head between scans can be out-of-plane as well as in-plane, the framework in this research includes 3-dimensional registration using the complete set of image slices and trilinear interpolation. In order to reduce the local maxima effect, partial volume interpolation is used to provide a more accurate estimate of the joint histogram [
To improve the performance and robustness of the mutual information measure used in the registration algorithm, it is combined with gradient information as outlined by Pluim et al. [
The gradient vector is computed for each sample point
The six parameters in the registration function,
An adaptive segmentation based on the watershed algorithm and a novel texture measurement is used in this research. The method consists of two stages: the preliminary watershed segmentation stage and the texture classification stage. In the first stage, DT-CWT coefficients are used to extract the texture gradient for the watershed algorithm. In the second stage, DT-CWT coefficients are used as the texture measure to classify the textures.
The first stage of the segmentation algorithm is outlined in Figure
Segmentation algorithm: Stage I.
The watershed algorithm is an automatic segmentation method based on visualizing a 2D image in 3-dimensions (two spatial dimensions, (
Serious oversegmentation problems result when the required gradient information is based solely on pixel intensities [
In this paper, the texture gradient is derived from the Dual-Tree Complex Wavelet Transform (DT-CWT) coefficients [
The texture gradient is obtained in several steps. First of all, directional median filtering [
In practice, the size of the median filter is related to the extent of the filter bank impulse response at that level and was chosen as
After directional median filtering, the new subbands
After obtaining the texture gradient of the image, a modulated gradient is obtained. The modulated gradient is based on texture activity as described in [
Now, the texture gradient and the modulated gradient are combined to obtain a final “Modified" gradient,
As a final step in this stage, the H-minima transform [
All the methods in the previous section are gradient modifications and provide only a partial solution to the watershed over-segmentation problem in real medical images. A novel texture classification method is used to merge regions of similar textures, thus further reducing the oversegmentation and improving algorithm performance.
Traditional texture classification is based on a rectangular-shaped window of a fixed sized [
The texture feature is extracted from a region using a method that is based on the DT-CWT coefficients, relying on their shift invariance and selective sensitivity. The DT-CWT decomposes an image into seven subband images at each scale level. Only one of the subband images, filtered by the lowpass filter, is the approximation information of the image. The remaining six subbands contain detail information, which includes texture information. For example, for scale level 4, one approximation subband image and 24 detail subbands can be obtained. Since the DT-CWT allows perfect reconstruction, a black image is substituted for the approximation subband image. When the image was reconstructed using the inverse DT-CWT, the result, the texture map, contained most of the texture information, and no approximation information.
After the construction of the texture map, the original image and the texture map, along with the label map output from Stage I, are passed to the KS test. Two similarity matrices are obtained:
The two regions which have the maximum value in
Segmentation algorithm: Stage II.
Example of modified gradient for segmentation: Stage I.
Original image
Texture gradient
Modulated gradient
Modified gradient
In summary, an image is oversegmented at the first stage and then a texture classification stage is applied to optimize the outcome of the segmentation until a termination criterion is achieved. Figure
Since the watershed segmentation result segments the entire image, and only the ventricles in the image are of interest, some user interactions are included in the framework. This interaction allows the user to identify which regions should be included in the ventricular system. After the regions have been selected, the framework generates an outcome image which only includes the ventricles.
The ultimate goal is to calculate the change in the volume of the ventricles. A combination of several algorithms was required to reach this goal. Registration of the two image sets is the first step in this process. Then the ventricles are segmented from the brain tissue. After segmentation, the complete set of slices is used to perform the ventricular volume calculation. The area of the ventricles in each slice is given by
Once the total volume of the ventricles is calculated, the change in volume between registered scans is calculated using (
Since it is not possible to measure the true volume of the cerebral ventricles directly in a living person (i.e., without resorting to another image-based morphometric technique), the precision and reliability of the volume calculation framework were tested using a physical phantom with known ventricular volume. A number of physical phantom models have been described in the literature, including plexiglass rods submerged in water cylinders [
A set of 5 physical phantoms was constructed [
The collection of clinical images was approved by the Research Ethics Board of the IWK Health Centre, and the requirement for informed consent was waived. All clinical CT studies were collected in anonymized DICOM format. The CT studies were from patients whose outcome (normal, stable hydrocephalus, developing hydrocephalus, treated hydrocephalus) was known and were selected by a radiologist (MHS) to reflect a range of outcomes. Of the 13 cases provided, nine cases labeled p
The volume calculation results for the set of five physical phantoms are summarized in Table
Physical phantoms: volume calculation results.
Phantom | Mean error | ||||
no. | ( | ( | (%) | ||
1 | 88 | 89.2 | 1.3 | 1.7 | 0.8 |
2 | 101 | 103.4 | 1.1 | 2.4 | 1.1 |
3 | 102 | 104.4 | 0.8 | 2.3 | 0.8 |
4 | 112 | 115.1 | 0.8 | 2.7 | 0.7 |
5 | 132 | 135.1 | 0.4 | 2.3 | 0.3 |
Overall | 2.3 | 0.8 |
The improvement in alignment achieved by the registration algorithm is illustrated in Figure
Sample registration result.
Reference image,
Float image,
Registered image,
Comparison of segmentation results.
Standard watershed result
Adaptive segmentation result
Physical phantom.
CT Slice image, physical phantom model
Ventricular system ice model
Volume calculation results for clinical cases.
Case | R | Clinical | |||||
% | ( | ( | (%) | ( | (%) | comments | |
p1 | 70.9 | 4.4 | 4.7 | healthy | |||
p2 | 20.2 | 71.7 | 169.8 | hy | |||
p3 | 64.2 | 23.4 | 24.3 | healthy | |||
p4 | 47.2 | 4.4 | 5.6 | healthy | |||
p5 | 62.5 | 6.7 | 7.5 | healthy | |||
p6 | 63.3 | 29.8 | 30.1 | hy:stable | |||
p7 | 55.7 | 24.1 | 14.9 | hy:treated | |||
p8 | 63.8 | 10.6 | 12.6 | healthy | |||
p9-1 | 53.8 | 50.1 | 83.4 | hy | |||
p9-2 | 68.0 | 83.4 | 76.9 | hy:stable | |||
p9-3 | 49.8 | 76.9 | 11.1 | hy:treated | |||
p10 | 67.0 | 8.5 | 98.0 | hy | |||
p11-1 | 62.0 | 54.2 | 149.4 | hy | |||
p11-2 | 55.5 | 149.4 | 155.6 | hy:stable | |||
p11-3 | 98.7 | 155.6 | 178.5 | hy:stable | |||
p12-1 | 61.2 | 7.6 | 21.3 | hy | |||
p12-2 | 58.2 | 21.3 | 37.6 | hy | |||
p13-1 | 109.7 | 42.0 | 9.8 | hy:treated | |||
p13-2 | 69.9 | 9.8 | 12.5 | hy:stable | |||
p13-3 | 66.4 | 9.8 | 39.9 | hy | |||
p13-4 | 19.1 | 39.9 | 22.1 | hy:treated | |||
p13-5 | 41.3 | 22.1 | 2.7 | hy:treated | |||
p13-6 | 41.0 | 2.7 | 3.2 | hy:stable |
The segmentation portion of the framework was validated by calculating the similarity index,
Similarity index calculated between adaptive and manual segmentation.
Case name | Similarity index % |
---|---|
ps1 | 76.8 |
ps2 | 77.1 |
ps3 | 72.0 |
ps4 | 72.4 |
ps5 | 72.4 |
ps6 | 74.9 |
ps7 | 80.2 |
ps8 | 74.6 |
ps9 | 72.5 |
ps10 | 89.1 |
ps11 | 72.4 |
ps12 | 80.6 |
ps13 | 83.9 |
Mean | 76.8 |
5.3 |
Since the objective of the research is to measure the change in volume of the ventricular system with time, the difference in volume between two scans was calculated using (
The
Graphical results for clinical cases: change in volume,
Using this predictor value, the diagnostic performance of the framework was compared to the clinical comments supplied by the radiologist (MHS) and the results are summarized in Table
Diagnostic performance analysis.
Predicted positive | Predicted negative | Total | |
---|---|---|---|
Positive examples | 8 (TP) | 0 (FN) | 8 |
Negative examples | 0 (FP) | 5 (TN) | 5 |
Total | 8 | 5 | 13 |
true positive: the number of cases which are diagnosed as hydrocephalus and the algorithm output also suggests a hydrocephalus diagnosis.
true negative: the number of the cases which are diagnosed as nonhydrocephalus and the algorithm also suggests a nonhydrocephalus diagnosis.
false positive: the cases are non-hydrocephalus but the algorithm suggests a hydrocephalus diagnosis.
false negative: the algorithm predicts a non-hydrocephalus diagnosis but the true diagnosis is hydrocephalus.
For ease of comparison, the clinical comments associated with each case are also listed in Table
For all the positive and negative examples, the framework prediction and the clinical comments match.
In this paper, a framework was implemented to measure the volume of the ventricular system to aid in the diagnosis of hydrocephalus. This framework consists of four important algorithms: a modified registration algorithm using a combination of the wavelet multiresolution pyramid and mutual information, an adaptive watershed segmentation with a novel feature extraction method based on the DT-CWT coefficients, and a volume calculation algorithm. In order to quantify the assessment of the success of the algorithms, an improvement ratio was calculated for the registration algorithm and a similarity index for the segmentation algorithm. Finally, physical phantom models with known volumes and clinical cases with known diagnoses were used to verify the volume calculation algorithm.
The average of
For the volume calculation method on the physical phantom models, all the error rates were below
Future work will include a more rigorous determination of the predictor value as well as collecting and testing a larger set of clinical data to examine the algorithm's performance on a wider range of clinically significant volume changes, particularly small clinically relevant changes.
The authors would like to thank the Natural Sciences and Engineering Council of Canada (NSERC) and the Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada, for the financial support.