measure of bone age precision based on an interpolation of the bone age from sequential radiographs, and assuming linear growth. The precision of the system was 0.17 years (standard deviation), and this was very good compared with an average operator precision of 0.50 years, demonstrating the improvement in reproducibility that can be achieved using an automated system.
The BoneXpert system has been released as a commercial product with pay-per-use licens- ing (http://www.boneXpert.com, Jan 2009).
2.1.4 Summary
Three approaches to the computerised assessment of bone age have been discussed: computer- assisted manual methods, user-assisted computerised methods, and fully automated meth- ods. This review has demonstrated, through examples, that it is possible to achieve rea- sonable bone age accuracy with each of these approaches.
Some of the fully automated methods are limited in that they only use of a small set of bones in the analysis. If only a limited set of bones is chosen, such as the third digit method, then there is more risk that variability in bone maturity across the hand could result in an unreliable bone age estimate. The range of bone-specific skeletal ages within the hand and wrist can be large, even within normal children [Roch88, p29]. Furthermore, some pathologies can exaggerate bone age differences between the bones of the hand and wrist [Roch88, p29]. This presents a challenge for fully automated assessment: include as many bones as possible to span the period of skeletal development and to protect against variability in bone maturity, yet use this variability to indicate the confidence in the bone age estimate.
Most of the effort given to automated bone age assessment has been focussed on the radius, ulna, and short-bones – the so-called RUS bones. This review has shown that research on the automated assessment of carpal bones has been limited to younger children, less than nine years old. The role of the carpal bones in older children has not been investigated. This is a research opportunity that is discussed further in Section 2.3.
2.2
Model of a system for the computerised assessment of bone
age
The aim of this brief section is to describe one possible model of a fully automatic sys- tem for bone age assessment, or at least a system with minimal expert-user intervention. Included is discussion on approaches that other researchers have taken to address require- ments of this model.
2.2.1 Overview of the model
The task of automating the assessment of bone age from hand-wrist radiographs is mostly one of computer vision. A computer is presented with an input image that is processed to generate a description of the objects within the image (the bones) which is then used to derive a measure of the bone age of the child. One model for such a system is shown in Figure 2.1. The model depicts individual stages of the assessment process, but in a practical implementation the stages are often interdependent. For example, some methods require the extraction of a limited set of features before image segmentation can be performed.
Image formation
and acquisition Preprocessing
Region of interest extraction Segmentation Feature extraction and classification Decision system Bone age estimate
Figure 2.1 Model of the proposed bone age assessment system.
This is only one possible model. An example of another model is the use of ‘pixel-processing’ neural networks that attempt to circumvent the need for segmentation. Such techniques have been used by Rucci et al. and Bocchi et al. for bone age assessment (see Section 2.1.3). The various stages of this model will now be briefly described.
2.2.1.1 Image formation and acquisition
The hand-wrist radiograph is formed from x-rays leaving an x-ray source, passing through the hand, and some of the transmitted x-rays causing ionisation in an x-ray sensitive re- ceptor. The x-rays usually pass through the hand in a straight line. Of those that do not, some undergo scattering, and the rest are completely absorbed by bone or soft tissue. The amount of x-ray scattering and absorption depend on the energy of the x-rays and the thickness, density, and atomic number of the material through which they pass. It is the differences in these four parameters that cause differences in the relative x-ray flux at the x-ray receptor. For example, if the x-ray energy is high there will only be a small differ- ence between the absorption of the x-rays in bone and soft tissue. This results in a loss of contrast between the bone and soft tissue on the radiograph.
2.2 Model of a system for the computerised assessment of bone age 47
The type of x-ray image receptor can also influence the contrast. For example, a recep- tor consisting of a combination of an x-ray sensitive, light-emitting screen with a light- sensitive film has a non-linear dependence on the flux of x-rays reaching the screen. The non-linearity in this screen-film combination is caused by the optical response of the film. If the x-ray flux is small (underexposure) or large (overexposure), there is loss in image contrast. The screen-film combination will only give optimal contrast performance over a limited range of x-ray flux. If the x-ray exposure is poorly controlled, the image contrast will be degraded.
To expand the optimal-contrast range, traditional screen-film combinations can be replaced by computed radiography or direct radiography. Computed radiography uses photostim- ulable phosphor plates that store the ionisation caused by the x-rays as a latent image, that is then readout from the plate using a laser beam to stimulate the phosphor. Alternatively, a direct radiography panel can be used to immediately convert the x-ray ionisation to an electrical signal for storage as digital image [Doi06]. Unlike screen-film combinations, both of these systems can control the relationship between the incident x-ray flux and the result- ing output pixel values. This allows changes to the image contrast and dynamic range, and improves tolerance to over and under-exposure of the x-ray receptor. If required, spatial filters can also be applied to enhance edges.
With computed radiography and direct radiography systems there are no further steps in the image acquisition stage for computerised bone age assessment - the image is inherently in a digital form that is stored ready for transfer to a processing system. With screen-film systems, the film has to be digitised. Dedicated x-ray film digitisers are available for this task, but digitising video cameras have also been mounted over x-ray light boxes to capture the image data.
The digital form of the radiographic image is usually a rectangular array of pixels with depths of 8, 12 or 16 bits. The overall contrast in the image is determined by the energy of the x-rays, scattered and absorbed radiation in the tissues of the hand, energy-dependent absorption of x-rays at the image receptor, and the relationship between the x-ray flux reaching the receptor and the output pixel values. The overall image resolution is deter- mined by the size of the x-ray source in the x-ray tube, the distance from the x-ray tube to the image receptor, the distance between the hand and the image receptor, and the design of the image receptor itself. Often only the image receptor resolution is considered because the other factors are controlled by a standard radiographic exposure (Section 1.2.1). The noise in the digital image is determined by a combination of structural noise sources from the image receptor, variations in the x-ray fluxes due to stochastic processes of x-ray generation and interaction (quantum mottle), and quantization noise from the digitisation
process. Although not strictly a source of noise, the radiographic projection of the inter- nal composition of the bones - the bony trabeculae - can produce image textures that are difficult to analyse.
2.2.1.2 Preprocessing
The preprocessing requirements usually depend on the subsequent methods used in the bone age assessment system. For example, if a segmentation algorithm is intolerant of noise, then significant spatial filtering may be required as part of the preprocessing. Typ- ically, the preprocessing prepares the image for subsequent analysis. This may include removing or excluding image labels such as the ’L’ typically used to indicate the left hand, as well as eliminating white borders caused by collimation of the x-ray beam, and reorien- tation of the image.
The preprocessing system should also be responsible for the initial screening of the hand- wrist radiograph. This should include rejecting poor quality radiographs that would lead to erroneous results. Such radiographs would then be flagged for either user intervention or full, manual reporting by a radiologist or specialist physician.
2.2.1.3 Region of interest extraction
The purpose of the region of interest (ROI) extraction stage is to break down the bone age assessment into different processes. The GP, TW, and Fels methods separately consider the phalangeal and metacarpal epiphyses, the carpal bones, and the radius and ulna epiphyses. The model adopted here is that all three regions should also be considered separately in an automated system.
The output of the ROI extraction stage is a numerical representation of regions surrounding the areas of interest. These regions usually completely enclose the bones of interest, but may focus on areas such as the metaphyseal-epiphyseal region.
2.2.1.4 Image segmentation
Image segmentation can be complex because there is no standard definition or theory for it, nor single standard method of performing it [Prat01, p551]. The segmentation process of this model refers to the separation on bone structures from other structures of the hand and wrist by way of some attribute of the bone. The attribute is commonly based upon differences in intensity in the digital radiographic image, but it could also be based on another attribute such as a region of common bone texture. The segmentation is usually described by a contour defining the edge of a bone, or region of bone. This contour may be open or closed, depending on the bone and the features that are extracted from it.