Light invariant photometric stereo.

An imaging setup that enables unsynchronized photometric stereo (PS) for Lambertian objects based on modulated light sources is presented. Knowing the specific frequency of the modulated light source allows to filter out any other light in the scene. This creates an image that depends only on the particular light source while ignoring the ambient light. Moreover, if the scene is illuminated by multiple modulated sources with different frequencies, repeating this process for every frequency will produce a sequence of images with the corresponding illumination. This sequence is then used as an input to the PS algorithm for 3D reconstruction. The proposed approach, named Light Invariant Photometric Stereo (LIPS), was verified on both synthetic and real-world data. LIPS eliminates the need for synchronization between the sources and the camera and significantly outperformed the classical PS method in an illuminated environment.

A probabilistic approach to spectral graph matching.

Spectral Matching (SM) is a computationally efficient approach to approximate the solution of pairwise matching problems that are np-hard. In this paper, we present a probabilistic interpretation of spectral matching schemes and derive a novel Probabilistic Matching (PM) scheme that is shown to outperform previous approaches. We show that spectral matching can be interpreted as a Maximum Likelihood (ML) estimate of the assignment probabilities and that the Graduated Assignment (GA) algorithm can be cast as a Maximum a Posteriori (MAP) estimator. Based on this analysis, we derive a ranking scheme for spectral matchings based on their reliability, and propose a novel iterative probabilistic matching algorithm that relaxes some of the implicit assumptions used in prior works. We experimentally show our approaches to outperform previous schemes when applied to exhaustive synthetic tests as well as the analysis of real image sequences.

Electric field stimulation integrated into perfusion bioreactor for cardiac tissue engineering.

We describe herein the features of a novel cultivation system, combining electrical stimulation with medium perfusion for producing thick, functional cardiac patches. A custom-made electrical stimulator was integrated via inserting two carbon rod electrodes into a perfusion bioreactor, housing multiple neonatal Sprague-Dawley rat cardiac cell constructs between two 96% open-pore-area fixing nets. The stimulator produced adjustable stimulation waveform (i.e., duty cycle, number of stimulating channels, maximum stimulation amplitude, etc.), specially designed for cardiac cell stimulation. The cell constructs were subjected to a homogenous fluid flow regime and electrical stimulation under conditions optimal for cell excitation. The stimulation threshold in the bioreactor was set by first determining its value in a Petri dish under a microscope, and then matching the current density in the two cultivation systems by constructing electric field models. The models were built by Comsol Multiphysics software using the exact three-dimensional geometry of the two cultivation systems. These models illustrate, for the first time, the local electric conditions required for cardiomyocyte field excitation and they confirmed the uniformity of the electrical field around the cell constructs. Bioreactor cultivation for only 4 days under perfusion and continuous electrical stimulus (74.4 mA/cm², 2 ms, bipolar, 1 Hz) promoted cell elongation and striation in the cell constructs and enhanced the expression level of Connexin-43, the gap junction protein responsible for cell-cell coupling. These results thus confirm the validity of the electrical field model in predicting the optimal electrical stimulation in a rather complex cultivation system, a perfusion bioreactor.

Improving shape retrieval by spectral matching and meta similarity.

We propose two computational approaches for improving the retrieval of planar shapes. First, we suggest a geometrically motivated quadratic similarity measure, that is optimized by way of spectral relaxation of a quadratic assignment. By utilizing state-of-the-art shape descriptors and a pairwise serialization constraint, we derive a formulation that is resilient to boundary noise, articulations and nonrigid deformations. This allows both shape matching and retrieval. We also introduce a shape meta-similarity measure that agglomerates pairwise shape similarities and improves the retrieval accuracy. When applied to the MPEG-7 shape dataset in conjunction with the proposed geometric matching scheme, we obtained a retrieval rate of 92.5%.

Studies on acute human infections using FTIR microspectroscopy and cluster analysis.

A novel methodology for the diagnosis of acute infections using FTIR microspectroscopy (FTIR-MSP) data on blood components and cluster analysis is presented. Blood samples were collected from 11 patients suffering from various infections and 16 age-matched healthy human controls. Blood components such as white blood cells, red blood cells, and plasma were isolated using standard procedures and FTIR-MSP of these components was utilized. A cluster analysis of the FTIR spectra was performed. The spectra obtained from the three blood components of patients were different from those of controls. The FTIR spectra of white blood cells from patients suffering infections were significantly different from the controls. Cluster analyses of averaged FTIR-MSP spectra of white blood cells provided 100% classification between patients and healthy controls.

Diagnostic potential of Fourier-transform infrared microspectroscopy and advanced computational methods in colon cancer patients.

Colon cancer is the third leading class of cancer causing increased mortality in developed countries. A polyp is one type of lesion observed in a majority of colon cancer patients. Here, we report a microscopic Fourier transform infrared (FTIR) study of normal, adenomatous polyp and malignant cells from biopsies of 24 patients. The goal of our study was to differentiate an adenomatous polyp from a malignant cell using FTIR microspectroscopy and artificial neural network (ANN) analysis. FTIR spectra and biological markers such as phosphate, RNA/DNA derived from spectra, were useful in identifying normal cells from abnormal ones that consisted of adenomatous polyp and malignant cells. However, the biological markers failed to differentiate between adenomatous polyp and malignant cases. By employing a combination of wavelet features and an ANN based classifier, we were able to classify the different cells as normal, adenomatous polyp and cancerous in a given tissue sample. The percentage of success of classification was 89%, 81%, and 83% for normal, adenomatous polyp, and malignant cells, respectively. A comparison of the method proposed with the pathological method is also discussed.