# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 6 Discussion
### Platform Comparison  
The AS/IJ platform is of the same class as two other platforms - TWS and Ilastik. However, there are notable differences in the image processing functionality. Ilastik implements the following filters: ST eigenvalues, Hessian eigenvalues, LoG, Amplitude of Gradient, Difference of Gaussians (DoG) and Gaussian.  
Ilastik also supports extendability by plugins, however the filter set at present is still limited. Compared to Ilastik, AS/IJ is distributed with more filters implementing scale space theory--notably Bi-Laplacian, Gaussian Jet, Curvatures in 2D (line) and 3D (surface) and ALoG. The first order gradient filter is complete in the sense that it produces both coordinate components as well as the amplitude/phase decomposition, while in Ilastik only the amplitude is computed.  
TWS features a more varied filter collection, which have been selected mostly for historical reasons. These are notably the Gaussian, Hessian, Membrane projections, Ranking filers (mean, max, min, median), variance, entropy, Anisotropic Diffusion, Lipschitz, Gabor, LoG, Sobel, DoG, Kuwahara, Gaussian Derivatives (up to order 5), ST. The TWS platform limits the choice of filters only to hard-coded ones. The Sobel filter implements a gradient operation. Both the Kuwahara and Anisotropic Diffusion filters are edge-preserving smoothing filters, which are more suitable for image preprocessing. The Lipschitz implements the Lipshitz cover, which is a morphological operation, suitable for eliminating slowly-varying image background. As such this filter could be more useful for enhancing natural images or bright microscopic images, where the illumination field is uneven. The local statistic filters (i.e variance and entropy) output information about the local feature variation. As a result the range of applications of TWS is also more limited than Ilastik and AS/IJ. Moreover, the platform is geared mostly towards segmentation.  
The DoG functionality, present in both TWS and Ilastik is completely superfluous, as it was introduced as an approximation of LoG in the times when computational power and memory for personal computing were very limited. This is the reason why it is not present in AS/IJ. From this perspective, it can be used mostly for historical reasons for comparing results with legacy code.  
AS/IJ is therefore, the platform which implements more completely the functionality prescribed by differential geometry. Multiscale morphological image filers (e.g., ranking, opening, closing) were not included in the present release of the platform, although such filters are present in ImageJ, for the following reason. The filters are not separable and, therefore, computational time scales as \(\mathcal{O}(N^{k})\) for a \(k\)-dimensional image. For large images the performance penalty could become appreciable.  
A major difference with regard to the TWS is the interactive feature inspection functionality. In our experience this can be useful in two scenarios. On the first place, when refining ROIs, one can reshape them to cover filter responses that are more discriminative for the problem at hand. This is facilitated by the functionality to overlay the ROI annotations on the filter response (Figure 8). On the second place, based on an initial expert choice of filters and the outcome of the initial segmentation, one can decide either to exclude some or include other filters and retrain the system. The metadata concept is another major difference with IWS and Ilastik, which at present do not support metadata.