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Testing of the noise model using the hand phantom

4.3 Radiographic noise

4.3.4 Testing of the noise model using the hand phantom

In the previous section the noise model was shown to be valid for the unprocessed radio- graphs of a stepwedge at doses typical of those used for hand-wrist radiographs. The hand phantom was used to test this model for radiographs processed using clinical protocols. The hand phantom was intentionally designed with uniform attenuation areas that could

4.3 Radiographic noise 87

be used to measure the variance of the noise without structural features superimposed upon the image data. Regions of interest were used within these uniform areas to measure the variation of image noise with dose to the CR plate. To reduce variability, the same size and position of regions of interest were used, and the CR plate was positioned within a template to reduce changes in the position of the regions. There were seven useful regions within the hand phantom image, although one (region 4) produced erroneous results be- cause it was of very large radiographic contrast, and its small size meant that the region of interest data was affected by edge enhancement routines used as part of the clinical image processing protocol. The results are presented in Figure 4.11 for the standard deviation on a log scale. At low doses the system applies low-dose compensation and this significantly changes the noise characteristics of the images, as observed for the 9 and 18.5 µGy curves. The logarithm of the noise is approximately linear for the clinically relevant doses of 39 and 48.5 µGy. Interesting to note is the sharp drop in noise for the the smallest pixel intensities. This region of interest is in the image background and these pixel intensities are normally not included in the calculations for the tone scaling of the image [Bogu95]. The drop could be due to a true reduction in noise in the background, or a compression of the image pixel intensity scale for the background region; that is, the pixel intensity for the background is higher than predicted from the standard deviation of the noise, so the data point is shifted by the mean pixel intensity.

Given the tone scaling and other image enhancements performed by the Kodak CR system, the results presented in Figure 4.11 for the clinically relevant doses of 39 and 48.5 µGy are a little surprising. Although these results validate the noise model of Eqn. (4.6) for radiographs processed using clinical protocols, the uniformity of the regions of interest used in the analysis would not normally be found in clinical radiographs. However, the results suggest that if regions of interest can be found that provide a reliable estimate of the image statistics in clinical radiographs, then it should be possible to measure the noise characteristics and fit a linear model to the logarithm of the standard deviation of the noise. The results for higher entrance doses show a departure from linearity that can be ex- plained by the non-logarithmic dose response of the CR system at these doses. This non- logarithmic response could result in a decreased standard deviation of the noise. Another explanation is the noise reduction due to the effect of the tone scaling for higher raw pixel intensities. Either way, such high doses are normally not used clinically, and hand doses as high as 99 µGy can still be reasonably well approximated with an exponential function (linear fit in Figure 4.11) if the dark areas of the image are avoided. These dark areas of the image are outside of the main region of interest of the hand and are not of importance once the hand outline has been detected for further processing.

Mean pixel intensity

1000 2000 3000 4000

Standard deviation of noise (log scale)

20 40 60 80 10 100 Dose 9.0 Dose 18.5 Dose 39.1 Dose 48.5 Dose 99.1 Dose 159 Dose 199 Dose 399

Figure 4.11 Kodak CR system radiographic noise for clinical processing of hand phantom images.

The curves for the clinically relevant doses of 39 and 49 µGy support the proposed noise model being exponential with pixel intensity. The non-linear nature of the curves for doses over 49 µGy can be explained by the departure of the CR system from a logarithmic dose response for high doses, as also observed in Figure 4.10. The encircled data points are outliers corresponding to erroneous results for region of interest 4 of the images (refer to Figure 4.4). All doses are in units of µGy.

important for image denoising because it not only shows that the noise is non-stationary, it also indicates that there is significant variation in the amount of noise for the different pixel intensities in the radiographs. This can be seen from the results for the hand-phantom presented in Figure 4.11 where the variation in image noise (standard deviation) across the image is as high as a factor of six. If a denoising method uses an average value for the image noise, then some parts of the image will undergo excessive denoising, while other parts of the image will undergo insufficient denoising. This is especially important for the bones in the radiographs because these have the highest noise levels and require the most denoising. This is explored further in Chapter 6.

4.4

Conclusions

The Kodak computed radiography system uses a logarithmic transformation to convert the stimulated light output from the CR plate to image pixel intensities. Using this transforma-

4.4 Conclusions 89

tion and modelling of the radiographic noise as a single Poisson distribution, it was shown that the standard deviation of the noise is signal-dependent and varies approximately ex- ponentially with increasing pixel intensities for radiographs processed using the raw mode on the CR plate reader. When clinical protocols are used to process simulated hand radio- graphs, the algorithms for tone scaling and visual enhancement maintain pixel intensities over a wide range of doses. This result is used in Chapter 5 for identifying image artefacts. Even after tone scaling and visual enhancement, the image noise for uniform regions of the radiograph still has an approximately exponential relationship between the standard deviation of the noise and the pixel intensities. This model is used in Chapter 6.3 for noise estimation as part of image denoising.

Chapter 5

Preprocessing and region of interest

extraction

The analysis of most images is performed in a series of stages. One of the first stages in the analysis of hand-wrist images is preprocessing. This stage is required to improve the accuracy and robustness of the image processing. In this chapter the preprocessing tasks of artefact identification and hand outlining are addressed. This preprocessing improves noise estimation methods (see Section 6.3), and creates an outline of the hand to initialise methods for region of interest extraction. Significant research has already occurred on the topic of region of interest extraction and this part of a bone age assessment system is essentially a development task. Rather than duplicate or parallel this research, the author chose to select the carpal region manually for this proof-of-concept research. This approach had the additional advantage of removing the region of interest extraction as a source of variability in the automated assessment of the carpal bones.

5.1

Introduction

Preprocessing and region of interest (ROI) extraction refer to image processing tasks that prepare the hand-wrist radiograph for bone segmentation and subsequent feature extrac- tion for bone age estimation. The requirements for preprocessing depend on both the means by which the image was acquired and the subsequent image processing methods. For example, some of the computerised methods that worked with digitised screen-film radiographs had to be modified to work with computed radiography images [Piet01a]. The preprocessing takes an image considered suitable for reporting by a radiologist and changes it to a computerised representation that is suitable for segmentation and feature extraction. This means potentially changing the image characteristics like spatial resolu-

tion, contrast and noise. An important question is how much preprocessing is tolerable, that is, how much can the images change before these changes affect the computerised bone age measurements? This question has not been answered in the published litera- ture. Similarly, and perhaps more surprising, there is no consensus on the image quality requirements for the manual methods of bone age assessment that are in use. One possible explanation for the lack of consensus may be the different methods of bone age assessment. For example, a high resolution screen-film combination is recommended for use with the TW3 method [Tann01], whereas it has been claimed that if a bone age precision of only 0.5 years is considered acceptable, then a reasonable estimate can be achieved using a much lower resolution bone density scanner combined with an atlas-based method similar to that of Greulich and Pyle [Plud04]. Because of the uncertainty regarding the image quality requirements, the approach taken in this research was to preserve spatial resolution and image contrast wherever possible.

It has not been possible to handle all variations of image characteristics in this research. Although the focus was on computerised methods of bone age assessment, the primary objective was to investigate these methods to identify the role of the carpal bones in older children. Some working assumptions were adopted in order to focus on this primary ob- jective and to make best use of the computed radiography data available for use in this research:

• The hand can be oriented in any direction on the radiograph, • The radiograph is of the left hand,

• All five digits are within the radiograph and all bones are visible,

• All carpal bones are within the radiograph and there are no more than eight bones, • The radius and ulna have at least 1 cm of diaphysis visible on the radiograph, • The radiograph is of suitable quality for reporting by a radiologist. Furthermore, it

should be free of artefacts that cross the bone boundaries and free of complicating factors such as fusion of the hamate and capitate bones. These special cases will be dealt with in future research.

The demarcation between preprocessing, region of interest extraction (ROI), and the sub- sequent stage of bone segmentation is vague. This reflects the dependencies between these methods and the use of some segmentation methods in ROI extraction. This chapter ad- dresses the preprocessing tasks of artefact identification and hand soft tissue outlining. The chapter begins with a review of existing preprocessing methods that have been used for automated assessment of bone age. These methods include radiographic border detection,