110	D.P. Costello and P.A. Kenny

Fig. 5.13 (a) T1w GE image (b) global segmentation using the GMM and (c) classification of visceral fat

after global segmentation using the GMM. Region growing allows for classification based on spatial location. This is illustrated in Fig. 5.10 where both the liver and visceral fat are differentiated from fat in the image. Further work is required to fully automate classification.

5.6 Conclusions

This chapter reviewed the challenges associated with the quantification of fat in MR images. Accurate segmentation and analysis of fat using MRI is not a trivial matter. Ideally, MR images acquired for fat analysis should contain three distinct

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tissue classes: fat, soft tissue and air. However, as a result of the many artifacts inherent to MRI this ideal image model is rarely attained. Therefore, consideration must be given to both the PVE and intensity inhomogeneities when segmenting fat in MR images. It is crucial that fat segmentation is approached with a thorough understanding of these artifacts and the limitations they present. A number of techniques used to segment fat in the presence of inhomogeneities were outlined in this chapter.

Image acquisition is the most important step in the quantification and analysis of fat using MRI. Before scanning patients, the imaging sequence should be optimized to achieve a balance between image contrast and acquisition time. Selection of an appropriate imaging sequence, such as the WS–bSSFP can significantly reduce the complexity of the segmentation algorithm required.

Fat classification currently requires manual intervention and will remain a significant challenge in the future. Future work in this field will investigate the prospect of fully automating the classification process and the use of soft segmentation algorithms involving fuzzy sets to overcome the PVE.

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