## 4 Discussion  
Textural features are directly or indirectly related with MATV, quantization level, and segmentation method. It is, therefore, very important to understand the relationships of textural features with other parameters before they can be used as tumor staging and prognostic biomarkers. One part of the study was to investigate the relationship of quantization to the lesion volumes by filling the spheres with the same homogeneous activity. The investigation reveals that, since bigger volumes incorporate a wider range of intensities, it is sensitive to the volumes. In such cases, bigger homogeneous spheres can be interpreted as heterogeneous compared to the smaller ones if a higher level of quantization level is selected. On the other hand, quantization dependency on volume can be reduced by selecting a lower level of quantization which forces the intensities to appear more homogeneous. However, selection of a lower quantization level can also eliminate the actual heterogeneity information of lesion. Considering the characteristics and relationships of all the textural features for the homogeneous spheres over a range of volumes, either quantization level 32 or 64 appeared to be the most suitable. Previous studies also suggested similar quantization levels [17; 23]. Although all six textural features extracted from GLCM are more or less dependent on volumes, entropy and energy are the most sensitive ones. VOIs of similar volumes delineated in the background reveal that spill in effect represented by PVE on the extracted textural features is far smaller compared to the effect of volume itself. It has been reported that, for volumes greater than 45 cm\({}^{3}\), the dependency of entropy on MATV significantly reduces for quantization level 256 [24]. However, a quantization level 64 for volume greater than 10 cm\({}^{3}\) was suggested by a different study [28]. Investigation of quantization vs. volume relationship carried out in this study reveals the cause of suggestion of different cutoff volumes by different research groups. Investigation on heterogeneous spheres carried out in this study also reveals that energy and entropy are not suitable to be used  
Figure 11: Six textural features vs. volumes for all six homogeneous spheres for VOI\({}_{\text{true}}\) and for two heterogeneous spheres for VOI\({}_{\text{true}}\), VOI\({}_{\text{4 QT}}\), and VOI\({}_{\text{A}}\). (**a**) Homogeneity, (**b**) contrast, (**c**) energy, (**d**) contrast, (**e**) dissimilarity and (**f**) entropy. Contrast and quantization level were kept fixed at 4:1 and 64 respectively. as prognostic biomarker of heterogeneity. This is because the response may occur due to change in both volume and heterogeneity, whereas energy and entropy can only track changes in volumes. Similarly, correlation measure is also less suitable as it is highly sensitive to intensity. This study also included the effects of segmentation accuracy on textural features. For that purpose, two threshold-based (40% fixed and adaptive) lesion delineation methods were implemented. It was found that fixed threshold can only be optimal for a certain contrast for all noise levels and remains sensitive not only to contrast and volume but also to noise. With an adaptive approach, the dependency on contrast and noise is reduced and becomes less sensitive to contrast and noise. However, for the case of clinical setting where the acquisition duration is generally 15 min or 900 s, the adaptive threshold performance is not optimal particularly when the volume is small and contrast is low in comparison to the bigger spheres. However, the overall performance of the adaptive threshold is better than the fixed threshold-based segmentation method. The two threshold-based segmented volumes (VOI4 OT and VOIA) are considerably different from each other. For this reason, although sometimes the delineated volumes are similar, textural features extracted using these VOIs are different. On the other hand, there is a good overlap between VOIA and VOItrue.