Non-negative matrix analysis for effective feature extraction in X-ray spectromicroscopy

X-Ray absorption spectromicroscopy provides rich information on the chemical organization of materials down to the nanoscale. However, interpretation of this information in studies of "natural" materials such as biological or environmental science specimens can be complicated by the complex mixtures of spectroscopically complicated materials present. We describe here the shortcomings that sometimes arise in previously-employed approaches such as cluster analysis, and we present a new approach based on non-negative matrix approximation (NNMA) analysis with both sparseness and cluster-similarity regularizations. In a preliminary study of the largescale biochemical organization of human spermatozoa, NNMA analysis delivers results that nicely show the major features of spermatozoa with no physically erroneous negative weightings or thicknesses in the calculated image

Cluster analysis of soft X-ray spectromicroscopy data

We describe the use of principle component analysis (PCA) to serve as a prefilter for cluster analysis or pattern récognition analysis of soft x-ray spectromicroscopy data. Cluster analysis provides a method to group régions with common spectral features even if no prior knowledge of their spectra is available, such as in biology or environmental science

Dynamic shape instantiation for intra-operative guidance.

Primary liver cancer and oligometastatic liver disease are one of the major causes of mortality worldwide and its treatment ranges from surgery to more minimally invasive ablative procedures. With the increasing availability of minimally invasive hepatic approaches, a real-time method of determining the 3D structure of the liver and its location during the respiratory cycle is clinically important. However, during treatment, it is difficult to acquire images spanning the entire 3D volume rapidly. In this paper, a dynamic 3D shape instantiation scheme is developed for providing subject-specific optimal scan planning. Using only limited planar information, it is possible to instantiate the entire 3D geometry of the organ of interest. The efficacy of the proposed method is demonstrated with both detailed numerical simulation and a liver phantom with known ground-truth data. Preliminary clinical application of the technique is evaluated on a patient group with metastatic liver tumours