5 Fat Segmentation in Magnetic Resonance Images

105

Fig. 5.10 (a) A T1w GE image and the seed points used for region growing and (b) is the resultant segmentation following region growing, visceral fat is labeled with red, other fat is labeled blue and the liver purple. Each labeled group is quantified separately

to the lack of clearly defined edges. Despite this, the data presented by Brennan et al. were well segmented. Brennan’s method did not classify and label body fat. Further steps are required to develop classification algorithm.

5.4.5 Adaptive Thresholding

Due to intensity inhomogeneities in MR images a single threshold may not be sufficient for the entire image. Segmentation using adaptive thresholding can

106	D.P. Costello and P.A. Kenny

Fig. 5.11 (a) Whole body T1-weighted GE image affected by intensity inhomogeneities; (b) Result of global segmentation using the GMM on (a); (c) Sub-images used for adaptive thresholding; and (d) is the result of adaptive thresholding

compensate for intensity inhomogeneities [39]. Adaptive segmentation can be achieved by dividing an image into a number sub-images as shown in Fig. 5.11c [50]. Each sub-image is then segmented using one of the segmentation algorithms discussed in Sect. 5.4.2.

Two factors must be considered when selecting the size of the sub-images:

(1)They must be small enough so the impact of the intensity inhomogeneity is minimal across each of their areas

(2)They must contain enough voxels to maintain a workable SNR

Figure 5.11a is an example of an image that is affected by intensity inhomogeneities and (b) is the result of global segmentation using the GMM algorithm. Using adaptive segmentation a significant improvement can be seen in Fig. 5.11d. If the sub-images cannot be made small enough to reduce the impact of the intensity inhomogeneities, a technique which uses overlapping mosaics may be used [35], this is discussed in Sect. 5.4.6.

Local adaptive thresholding (using a sliding window) can be an effective segmentation algorithm in the presence of inhomogeneities [51]. This technique

5 Fat Segmentation in Magnetic Resonance Images

107

thresholds individual voxels using the mean or median value of their surrounding n × n neighborhood. MR images acquired for fat analysis can contain large monotone regions consisting of a single tissue type. In order to achieve meaningful segmentation the size of the neighborhood must be large enough to contain more than one tissue class at any point tin the image. This should be considered when selecting the neighborhood size.

5.4.6 Segmentation Using Overlapping Mosaics

Yang et al. [35] developed a method to segment fat in MR images using overlapping mosaics. The segmentation technique consists of 3 steps:

(1)Mosaic bias field estimation

(2)Adipose tissue segmentation

(3)Consistency propagation

Following smoothening (low pass filtering) to remove noise the expression for the biased image in (5.2) becomes:

fbiased

(x, y) = foriginal(x, y)β (x, y),

(5.16)

where fbiased(x, y) is the image after filtering. Assuming the bias field varies gradually across the entire image, log (β (x, y)) can be approximated by a piecewise

linear function. Yang divides fbiased(x, y) into a array of overlapping mosaics or subimages (Tij). Within each of the sub-images Log (β (x, y)) is assumed to be first

order linear, therefore, (x, y) Tij:

log f	(x, y) = log( f	original	(x, y)) + aij	x	−	x(0)	+ bij y	−	y(0)	+ cij, (5.17)
biased						ij			ij

where (x(ij0), y(ij0)) is the upper left voxel in each sub image. Optimal values for a and b are estimated by maximizing the function:

2

P =

∑

∑ δ

(log( f

(x, y)))

−

aij

x

−

x(0)

−

bij y

−

y(0)

−

ξ ,

biased

ij

ij

ξ(x,y) Tij

(5.18) where δ (x) = 1 when x = 0, and ξ is the gray-scale intensity index of the image histogram. Cij is not calculated because it affects voxels in the sub–image uniformly, causing a change in position of gray–levels in the image histogram but not the shape. Once the image is corrected, the skewed and bimodal peaks discussed in Sect. 5.3.2 appear more distinctive.

When intensity inhomogeneities are corrected, image segmentation is carried out using a multi-level thresholding technique on each sub-image. Segmentation

108	D.P. Costello and P.A. Kenny

Fig. 5.12 The intermediate processing results of the proposed algorithm applied to a synthetic phantom. (a) The original image with an SNR of 20 dB; (b) the optimum manually segmented result by using intensity thresholding; (c) the bias corrected image by using overlapping mosaics; (d) the result of initial segmentation; (e) the final fat distribution after applying intermosaic consistency propagation. This material is reproduced with kind permission from Springer Science+Business Media B.V [35]

is based on an automated thresholding technique that minimises the total variance within the three classes, fat soft tissue and background, giving 2 threshold values ξ1 and ξ2 [16].

A measure of confidence, λij, of the segmentation result is calculated, as not all sub images will contain all three tissue classes.

meanHij(ξ )

ξ ≥ξ2
λij = meanHij(ξ )	,	(5.19)

ξ <ξ1

Hij(ξ ) is the log transform of the image histogram. λij is likely to be large when all three tissue classes are present in the sub-image. However, when only one or two tissue classes are present λij will be much smaller indicating misclassification. The mosaic tile with the highest value of λij is used as a seed for consistency propagation. The regions of overlap between the seed tile and its nearest neighbors are compared. If any conflicting segmentation results are present, then the value for ξ2 in neighboring tile is changed to that of the seed. This process is propagated to all tiles within the image until segmentation result like those shown in Fig. 5.12 are achieved. Peng et al. [16], compared this technique to the gold standard, manual segmentation, and found that the mean percentage between the two was 1.5%.

5 Fat Segmentation in Magnetic Resonance Images	109
5.5 Classification of Fat

To appreciate the complexities associated with quantifying fat in medical images, it is important to know what exactly needs to be measured. The most common conflict in the literature is in the terminology used, (i.e. fat or adipose tissue) [52]. The difference between fat and adipose tissue is important when quantifying fat in MR images. Bone marrow in a typical T1w imaging sequence has the same graylevel as body fat. However, bone marrow adipose tissue is not classified as fat because it is connected to haematopoietic activity2 and not to obesity [53, 54]. Classification is further complicated by the subdivision of fat into three main categories: total body fat [15], visceral fat and subcutaneous fat [6]. Whole body fat includes the measurement of all adipose tissue except bone marrow and adipose tissue contained in the head, hands and feet [52]. A summary of the proposed classification of adipose tissue within the body is given by Shen et al. [52] and is presented in Table 5.1. Examination of body fat distribution involves the analysis of two or more of the fat categories outlined in Table 5.1.

Global segmentation algorithms such as thresholding require extra steps to classify fat. This can be achieved manually by drawing a region of interest around areas such as the viscera. Figure 5.13 shows the result of manual classification

Table 5.1 Proposed classification of total body adipose tissue as given by Shen et al. [52]

Adipose tissue compartment	Definition
Total adipose tissue	Sum of adipose tissue, usually excluding bone marrow and
	adipose tissue in the head, hands, and feet
Subcutaneous adipose tissue	The layer found between the dermis and the aponeuroses
	and fasciae of the muscles. Includes mammary adipose
	tissue
Superficial subcutaneous	The layer found between the skin and a fascial plane in the
adipose tissue	lower trunk and gluteal-thigh area
Deep subcutaneous adipose	The layer found between the muscle fascia and a fascial
tissue	plane in the lower trunk and gluteal-thigh areas
Internal adipose tissue	Total adipose tissue minus subcutaneous adipose tissue
Visceral adipose tissue	Adipose tissue within the chest, abdomen, and pelvis
Non-visceral internal adipose	Internal adipose tissue minus visceral adipose tissue
tissue
Intramuscular adipose tissue	Adipose tissue within a muscle (between fascicles)
Perimuscular adipose tissue	Adipose tissue inside the muscle fascia (deep fascia),
	excluding intramuscular adipose tissue
Intermuscular adipose tissue	Adipose tissue between muscles
Paraosseal adipose tissue	Adipose tissue in the interface between muscle and bone
	(e.g., paravertebral)
Other non-visceral adipose	Orbital adipose tissue; aberrant adipose tissue associated
tissue	with pathological conditions (e.g., lipoma)

2Hematopoietic activity: pertaining to the formation of blood or blood cells.