BOUND-BOUND AND BOUND-FREE CONTRIBUTIONS TO THE ABSORPTION SPECTRUM OF (H2O)-30

We report absolute photodestruction cross sections for (H2O)[MATH] in the range 0.75[MATH]4.66 eV and recover a spectrum quite similar to that of [MATH] but redshifted by about 0.5 eV. The spectrum can be deconvolved into two distinct components : the low energy region below the peak in the absorption which arises from a transition to a bound excited state, and the high energy tail which arises from absorption into the electron continuum. The escape probability of the electron is unity upon excitation with more than about 0.5 eV of excess energy above the vertical electron detachment energy of the cluster

Texture analysis applied to second harmonic generation image data for ovarian cancer classification

Remodeling of the extracellular matrix has been implicated in ovarian cancer. To quantitate the remodeling, we implement a form of texture analysis to delineate the collagen fibrillar morphology observed in second harmonic generation microscopy images of human normal and high grade malignant ovarian tissues. In the learning stage, a dictionary of “textons”—frequently occurring texture features that are identified by measuring the image response to a filter bank of various shapes, sizes, and orientations—is created. By calculating a representative model based on the texton distribution for each tissue type using a training set of respective second harmonic generation images, we then perform classification between images of normal and high grade malignant ovarian tissues. By optimizing the number of textons and nearest neighbors, we achieved classification accuracy up to 97% based on the area under receiver operating characteristic curves (true positives versus false positives). The local analysis algorithm is a more general method to probe rapidly changing fibrillar morphologies than global analyses such as FFT. It is also more versatile than other texture approaches as the filter bank can be highly tailored to specific applications (e.g., different disease states) by creating customized libraries based on common image features