Régis Huez

Independent component analysis of Raman spectra: Application on paraffin-embedded skin biopsies

Abstract Raman spectra provide wealthy but complex information about the chemical constituents of biological samples. Digital processing techniques are usually needed to extract the spectra of chemical constituents and their associated concentration profiles. However, spectral signatures may admit transformations from those recorded on pure constituents and these techniques require a priori knowledge of spectra to be estimated. We propose in this study to analyse paraffin-embedded skin biopsies of malignant and benign tumors dedicated to oncology researches by Raman spectroscopy and advanced signal processing methods. We show that the commonly used principal component analysis (PCA) does not give physically interpretable estimators of spectra and associated concentration profiles. Based on a linear model and taking into account the statistical properties of spectra, independent component analysis (ICA) is used to better estimate the spectra of chemical constituents. The estimators of associated concentration profiles are no longer orthogonal and have only positive values, contrary to PCA. ICA allows to model the paraffin by three Raman spectra and provides good estimators of underlying spectra of the human skin, which is of great interest in oncology since the retrieval of spectral features of different types of skin tumors is sufficient for their discrimination.

Digital Dewaxing of Raman Signals: Discrimination between Nevi and Melanoma Spectra Obtained from Paraffin-Embedded Skin Biopsies

Malignant melanoma (MM) is the most severe tumor affecting the skin and accounts for three quarters of all skin cancer deaths. Raman spectroscopy is a promising nondestructive tool that has been increasingly used for characterization of the molecular features of cancerous tissues. Different multivariate statistical analysis techniques are used in order to extract relevant information that can be considered as functional spectroscopic descriptors of a particular pathology. Paraffin embedding (waxing) is a highly efficient process used to conserve biopsies in tumor banks for several years. However, the use of non-dewaxed formalin-fixed paraffin-embedded tissues for Raman spectroscopic investigations remains very restricted, limiting the development of the technique as a routine analytical tool for biomedical purposes. This is due to the highly intense signal of paraffin, which masks important vibrations of the biological tissues. In addition to being time consuming and chemical intensive, chemical dewaxing methods are not efficient and they leave traces of the paraffin in tissues, which affects the Raman signal. In the present study, we use independent component analysis (ICA) on Raman spectral images collected on melanoma and nevus samples. The sources obtained from these images are then used to eliminate, using non-negativity constrained least squares (NCLS), the paraffin contribution from each individual spectrum of the spectral images of nevi and melanomas. Corrected spectra of both types of lesion are then compared and classified into dendrograms using hierarchical cluster analysis (HCA).

A smart flat-coil eddy-current sensor for metal-tag recognition

This paper describes a smart eddy-current sensor for locating and identifying metal tags used to recognize buried pipes. We first describe the tags used and the technology of our locator, which is based on the induction-balance principle. We also describe an essential measurement-distance system controlling the sampling of data. An originality of the detector is the use of two flat coils carved onto an epoxy-resin support to generate the best possible electromagnetic field. The tags are made up of metal pieces of varying sizes separated by spaces of varying sizes. Intelligent pattern-recognition methods and their combination by the theory of evidence, which provide good and reliable recognition, are briefly presented. Finally, the systems performance for various depths of burial is analysed and ways to improve these results are presented.

Pre-processing and Source Separation methods for Raman spectra analysis of biomedical samples

Raman spectroscopy is a useful tool to investigate the molecular composition of biological samples. Source separation methods can be used to efficiently separate dense informations recorded by Raman spectra. Distorting effects such as fluorescence background, peak misalignment or peak width heterogeneity break the linear and instantaneous generative model needed by the source separation methods. Preprocessing steps are required to compensate these deforming effects. We show in this paper how efficiency of source separation methods is deeply dependent on preprocessing steps. Resulting improvements are illustrated through the study of the numerical dewaxing of Raman signal of a human skin biopsy. The applied source separation methods are a classical ICA algorithm named JADE and two positive source separation methods called NMF and MLPSS.

Smart Electromagnetic Sensor for Buried Conductive Targets Identification

In this paper, the authors introduce the evolution of an eddy current sensor based on the induction balance principle. Its objective is to localize and identify the various types of buried pipelines like gas and water without excavation. Starting from an analogical version of the sensor, they use modeling to increase its sensitivity. The modeling is realized using a distributed point source method, which gives us rather interesting results. Based on these, the authors describe the hardware and the different electronic parts which composes the detector. They present the second generation of the sensor and the different changes added to improve its performances. Two coding systems are associated to the sensor. It gives an important number of targets (tags) necessary to the application. Each kind of pipe type is associated with a characteristic tag integrating conductive elements. The identification of the tag allows the recognition of the corresponding pipe. The response of the buried tag can be disrupted by the presence of metallic objects in the neighborhood. To eliminate their effects, they use blind-source-separation algorithms. This represents the preprocessing step followed by signal processing software. There are many algorithms used to recognize buried tags. These are based on different principles like neural networks, fuzzy logic, or structural recognition. The multiplicity of the number of algorithms is necessary to surpass the difficult identification and drives us to use an original method of the combination of results, trying to increase the reliability of the final decision. Finally, the authors focus on the sensor evaluation and considered prospects

Denoising Using Blind Source Separation for Pyroelectric Sensors

This paper deals with a process of denoising based on a Blind Source Separation (BSS) method. This technique is inserted in an experimental device of nondestructive testing. Its excitation is a laser beam and its detectors are pyroelectric sensors. The latter are sensitive to the temperature. As they are also piezoelectric, they are particularly sensitive to the environmental noise. Therefore, it is necessary to denoise them. With this aim in view, a technique of blind source separation is implemented. One source corresponds to the incidental beam and the other sources are various noise. A judicious experimental device was designed in the laboratory. It fits to the requirements of the BSS technique, and it allows indeed a restoration of the incident signal.

Original improvements of a smart eddy current sensor by BSS techniques

In this paper, we explain how we have improved the performance of a smart eddy current sensor, based on the induction balance principle, by using blind source separation (BSS) methods. The initial sensor was dedicated to the recognition of metal tags buried in the ground. It has been described in a previous article (Belloir F, Huez R and Billat A 2000 Meas. Sci. Technol. 11 367-74 [link]). The problem with this kind of sensor is that it is also sensitive to the presence of conductive perturbations located close to the target to be recognized. Thus, the sensor response can be disturbed. The aim of this paper is to show that starting from some hardware transformations made jointly with the use of BSS algorithms one can restore the tag response perfectly. We first describe the hardware modifications that we had to bring to that sensor in order to use the BSS methods. Then we check theoretically and experimentally if the application conditions required by the BSS are fulfilled. We prove that with these improvements, we have realized a new device which permits one to circumvent all kinds of perturbations. We end by presenting various impressive results obtained using the modified sensor.

ON THE MODELING OF PARAFFIN THROUGH RAMAN SPECTROSCOPY

Abstract Raman spectroscopy is employed to record spectra which highlight vibrational information of biological structures. These spectra provide useful information about molecular composition of a tissue. However, the tissues are usually embedded into paraffin for a preservation purpose. The Independent Component Analysis (ICA) technique can be used to numerically dewax Raman spectra and to extract the specific information of the tissue. We show in this paper that the paraffin must be modeled by a three-source model in order to succeed. This linear model, confirmed also by analyzing paraffin blocks exclusively, should be preferred instead of commonly used one-source model.