Quantitative morphology of cytoskeletal organization: new classifier architectures and applications

Recently, these authors developed a heterogeneous, one-level image classifier (CH) based on morphological descriptors from direct domain analysis (spatial differentiation), fractal analysis and "spectrum enhancement" (a kind of non-linear filtering). Classifier CH was applied to epi-fluorescence microscope images of cytoskeletal microtubules and was trained to recognize structural alterations of the cytoskeleton in various circumstances. The application dealt with images of rat hepatocytes (rh). The scope of this paper is twofold: a) to investigate different classifier architectures, which include the multiobjective optimization of some image analysis parameters by means of suitable algorithms; b) to apply said classifiers to new sets of images obtained from mouse fibroblasts (mf) and HepG2 (hg) cells. Image sets from control and treated cell cultures are analyzed. Classifier CH is applied to mf microtubules. A new classifier entirely relying on "spectrum enhancement" (although on different descriptors) is developed and applied to rh and hg images. From the latter classifier, by bringing in descriptors from direct domain and fractal analysis, a hierachical classifier is derived and applied to rh images. Results are compared. Classifier performance is expressed in terms of sensitivity, specificity and information contents of the first three principal components.