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FOV, which reduces non-uniformities in the image. Smaller self-contained sections also remove the need for post processing such as image stitching and histogram matching. Transverse images of the abdomen make it possible to get an estimate of visceral fat volume from a small number of sample slices, which reduces scan time significantly [20].

Slice thickness and voxel size both influence SNR and PVE. Larger voxels increased SNR but increase the incidence of partial volume voxels (PVE is discussed in Sect. 5.3.1). Therefore, it is important to find a balance between SNR and PVE to optimize the segmentation process. Increasing the gap between slices can reduce acquisition time without compromising image quality. A slice gap of 100% will half acquisition time. Fat volume in the gap can be estimated using interpolation.

5.3 Image Artifacts and Their Impact on Segmentation

5.3.1 Partial Volume Effect

PVE occurs when a single voxel contains a mixture of two or more tissue types, (e.g. at the interface of fat and soft tissue or fat and air), resulting in blurring at boundaries. Figure 5.3 illustrates the effect of the PVE on subcutaneous fat. MR images have limited resolution which increases the probability of the PVE occurring [21]. Voxels affected by the PVE have an intermediate gray level intensity, which is determined by the proportion of each tissue type contained within that voxel [22]. This effect is observed at interfaces between gray and white matter in the brain and fat and soft tissue throughout the body. The PVE can cause fuzziness (or uncertainty) at the boundaries of two tissues classes [8]. This inhibits the use of edge detection methods due to ambiguity at the interface of tissue classes leading to a lack of clear edges [8, 23].

Edge-based segmentation methods aim to find borders between regions by locating edges (or boundaries) within images. MR images have a relatively low resolution and SNR when compared to other modalities such as CT. PVE and low

Fig. 5.3 (a) T1w GE image containing fat, soft tissue, and background (b) is a profile through plot of the region outlined in red in image (a)

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SNR can result in uncertainty at the boundary of objects in MR images which limits the use of edge detection algorithms [8]. Figure 5.3b illustrates the problem encountered by edge detectors when faced with the PVE. Where does fat stop and background start? The use of edge detection algorithms can lead to incomplete segmentation of boundaries in MR images. PVE is a three-dimensional phenomenon and can affect large volumes of tissue in a local area. This can affect the performance of global segmentation techniques, discussed in Sect. 5.4.

5.3.2 Intensity Inhomogeneities

Intensity inhomogeneities can have a significant impact on segmentation and quantitative analysis of MR images. They are caused by non-uniformity in the RF field (B1), irregularities in the main magnetic field (B0), susceptibility effects of normal tissue and receiver coil sensitivity profile [24]. This artifact can cause the appearance of skewed or bimodal fat peaks in the image histogram [25]. As a result, clinical MR images require some processing before segmentation and analysis can take place. Inhomogeneities in MR images can be modeled as a multiplicative bias field [23]. The bias field is characterized as a gradual change in intensity within segmentation classes across the entire image which cannot be attributed to random noise [23]. It can degrade the performance of the intensity–based segmentation algorithm, as the model assumes spatial invariance between tissues of the same class across the entire image. An expression for the biased image is given in (5.1).

fbiased = foriginal (x, y) β (x, y) + n(x, y),

(5.2)

where fbiased is the degraded image, forignal is the image without degradation or noise and n(x, y) is random noise. A number of approaches are investigated in the literature for the correction of the bias field (26–30). The impact of intensity inhomogeneities on thresholding is illustrated in Fig. 5.4.

Siyal et al. [26] and Rajapakse et al. [23] reviewed a number of approaches to reduce the appearance of the intensity inhomogeneities. These approaches can be split into two categories, retrospective and prospective modeling. Prospective modeling uses prior knowledge of the bias field, which can be obtained by imaging a homogeneous phantom. Homogeneous phantoms only provide a good estimate of the bias field for objects of similar size to the phantom. When imaging patients, the dimensions of the scanned volume can vary from patient to patient and also between sections of the same patient (e.g. the legs and the torso). The volume of the area being imaged changes the loading on the receiver coils’ in the MRI scanner, which in turn alters the coils sensitivity profile [27]. To account for this Murakami et al. [28] performed a calibration scan directly on the patient to estimate the bias field. Prospective modeling of the bias field can be impractical for studies containing large numbers of patients, as imaging time increases due to the need for additional phantom/patient scans [27].

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Fig. 5.4 (a) Ideal unbiased image (b) Product of the ideal image and bias field, (c) and (d) are the corresponding thresholded images

Retrospective methods are more practical to implement, as they are based on image processing and do not require any additional scan time. A number of methods to retrospectively correct the bias field were used in the literature [27, 29–32]. In an MR image, the bias field is considered to have a low spatial frequency, while anatomical structures are likely to consist of higher spatial frequencies. Therefore, it is possible to model the bias field based on the low frequency components of the original image [33]. This model can then be used with (5.2) to correct the inhomogeneity. Guillemaud et al. [34] proposed a method that changes the multiplicative bias field into an additive one, by applying homomorphic filtering to the logarithmic transform of the image data. An extension to this correction method which combines homomorphic filtering and normalized convolution was also proposed by Guillemaud et al. [32]. Both of these methods can affect high frequency components in the image. However, the second approach uses normalized convolution to compensate for this.