# Evaluating the Effect of Intensity Standardisation
## 4 Discussion
As both z-score and WhiteStripe showed a similar behaviour and both standardisation strategies led to negative values, we hypothesise that negative values may have affected FSL-FLIRT's performance. Further research in this regard is needed to determine the specific step affected by this standardisation scheme. z-score is one of the simplest intensity standardisation methods considered in this study. Despite being based on statistics which are sensitive to outliers, this strategy reduced inter-subject variations in both datasets, as depicted in Figure 3. If applied after registration, z-score could be included within FSL-SIENA. Clustering-based intensity standardisation methods--fuzzy c-means and Gaussian mixture model--segment regions of interest in the brain and use the mean intensity of the selected region to standardise intensities. Leveraging on these types of techniques seemed compatible with FSL-SIENA as they reduced inter-scan intensity variability. However, low tissue contrast as well as the presence of brain abnormalities and extra-cerebral regions can weaken their performance. Therefore, we recommend visual inspection of input scans and histogram of intensities to determine whether clustering is feasible and the optimal number of clusters to reach optimal performance. The histogram matching method proposed by [PERSON] et al. [26] assumes equal tissue proportions between the input scans [17], i.e., histograms of both the reference and input scan are equivalent. However, such an assumption does not necessarily hold in longitudinal studies due to the appearance of new brain lesions and brain tissue loss between visits. Moreover, linear interpolation between landmarks results in potential information and contrast loss. Thus, we do not recommend its use in longitudinal studies using FSL-SIENA. WhiteStripe standardises intensities based on the right-most mode (in T1-w scans) and, thus, ensures histograms of intensities will coincide at least on the detected peak. Furthermore, this methods does not require segmentation, thus reducing computation time and dependency on the accurate identification of the region of interest. Despite its theoretical advantages and improved performance in other scenarios [17], our experimental results suggest WhiteStripe is not compatible with FSL-SIENA as it can compromise its performance. Note that this outcome does not imply WhiteStripe is not a recommended intensity standardisation strategy, but that its use within FSL-SIENA compromises FSL-SIENA's performance. Additionally, we suggest using the original implementation of WhiteStripe in R as third-party implementations may not perform the same processing steps. Kernel density estimation also processes the histogram of intensities to find the right-most mode (in T1-w scans). Unlike WhiteStripe, it normalises intensities by the maximum peak, i.e., its application results in positive values only. This might explain why the method did not affect FSL-FLIRT's performance. At an experimental level, we found that the incorporation of this intensity standardisation method to FSL-SIENA did not seem to compromise its scan-rescan performance. Furthermore, its application resulted in higher effect sizes, i.e., an increase differentiation between Alzheimer's disease and normal control patients. However, the performance of kernel density estimation depends on the smoothing parameter \(h\): small values of \(h\) may lead to spiky estimates while large values may misrepresent the multimodal nature of the data. Our work has limitations. First, we assumed that higher atrophy differences between Alzheimer's disease and normal control subjects implied reduced errors in atrophy quantification compared to when omitted. Even though we found out pathological groups had higher atrophy rates than normal control groups with or without standardisation, in line with the literature [7], the values themselves varied. Whether one or the other is more accurate or reflective of the actual pathology is unclear and out of the scope of this work.