Pilar Beatriz García Allende

Spectral scatter scanning system for surgical margin detection

Tumour margin detection in real time without the use of additional molecular stain would be desirable. Surgical use of a microscopy tool would be ideal, but most systems focus on microscopic disease evaluation, whereas tools for macroscopic scanning of tissue such as enhanced endoscopy imaging are less well developed. A raster scanning scatter spectroscopy system has been evaluated for imaging the spectral signature remitted from tissue, with an online classification algorithm, which maximizes the ability to identify regions of tumour from regions of normal tissue. The system uses a wide band of wavelengths from 400 nm up to 700 nm, and recovers the scatter power, scatter amplitude, and absorption species, from the reflectance from a 100 micron spot, allowing imaging of tissue a high frame rate. The system uses dark field illumination and spectrometer detection in the emission channel together with a scanning mirror. The early prototypes of the system were tested on pancreas tumours and prostate tumour margin detection, and current work is ongoing in breast cancer margin delineation. The automated tissue classification from the data uses a k-nearest neighbours classification, to provide tissue delineation. from 1st Scientific Meeting of the Head and Neck Optical Diagnostics Society London, UK. 14 March 2009

Hyperspectral imaging sustains production-process competitiveness

Because of increased worldwide competition, efficiency improvements and establishing processes that are suitable for manufacturing high-end, high-quality products are primary commercial aims. In the agri-food industry, raw-material online characterization during the input stages of processing chains is of key importance for quality control. Classification into different quality categories and separation of unwanted spurious materials are traditionally done based on time-consuming machine-vision techniques. In industrial settings, however, welding-quality monitoring plays a major role, particularly in aeronautics and the nuclear industry, and the appearance of flaws (such as porosities or inclusions) must be prevented. Using x-rays, ultrasound, or other offline nondestructive evaluation techniques is associated with both economic and productivity costs, however. Optical spectroscopy (OS)1 has been widely used in process manufacturing, since each substance is uniquely identifiable from its spectral properties. However, traditional OS techniques generate information about the bulk properties of (or part of) a sample,2 and, in commonly used applications, many aspects of the derived sample properties result from composition heterogeneity. Imaging spectroscopy simultaneously determines the optical spectral components of a particular light-matter interaction (such as absorption) and its spatial location. It can successfully monitor sample heterogeneity. Hyperspectral imagers allow data collection in hundreds or even thousands of spectral bands. Thus, multicomponent information and high sensitivity to minor components are achieved, thus allowing enhanced quality control. Of the different types of imaging spectrometers,3 Figure 1. Global block diagram of our hyperspectral-imaging system for agri-food and industrial applications. LOS: Line of sight. a.u.: Arbitrary units. : Wavelength.

Enhanced tumor contrast during breast lumpectomy provided by independent component analysis of localized reflectance measures

A spectral analysis technique to enhance tumor contrast during breast conserving surgery is proposed. A set of 29 surgically-excised breast tissues have been imaged in local reflectance geometry. Measures of broadband reflectance are directly analyzed using Principle Component Analysis (PCA), on a per sample basis, to extract areas of maximal spectral variation. A dynamic selection threshold has been applied to obtain the final number of principal components, accounting for inter-patient variability. A blind separation technique based on Independent Component Analysis (ICA) is then applied to extract diagnostically powerful results. ICA application reveals that the behavior of one independent component highly correlates with the pathologic diagnosis and it surpasses the contrast obtained using empirical models. Moreover, blind detection characteristics (no training, no comparisons with training reference data) and no need for parameterization makes the automated diagnosis simple and time efficient, favoring its translation to the clinical practice. Correlation coefficient with model-based results up to 0.91 has been achieved.

Linear classifier and textural analysis of optical scattering images for tumor classification during breast cancer extraction

Texture analysis of light scattering in tissue is proposed to obtain diagnostic information from breast cancer specimens. Light scattering measurements are minimally invasive, and allow the estimation of tissue morphology to guide the surgeon in resection surgeries. The usability of scatter signatures acquired with a micro-sampling reflectance spectral imaging system was improved utilizing an empirical approximation to the Mie theory to estimate the scattering power on a per-pixel basis. Co-occurrence analysis is then applied to the scattering power images to extract the textural features. A statistical analysis of the features demonstrated the suitability of the autocorrelation for the classification of notmalignant (normal epithelia and stroma, benign epithelia and stroma, inflammation), malignant (DCIS, IDC, ILC) and adipose tissue, since it reveals morphological information of tissue. Non-malignant tissue shows higher autocorrelation values while adipose tissue presents a very low autocorrelation on its scatter texture, being malignant the middle ground. Consequently, a fast linear classifier based on the consideration of just one straightforward feature is enough for providing relevant diagnostic information. A leave-one-out validation of the linear classifier on 29 samples with 48 regions of interest showed classification accuracies of 98.74% on adipose tissue, 82.67% on non-malignant tissue and 72.37% on malignant tissue, in comparison with the biopsy H and E gold standard. This demonstrates that autocorrelation analysis of scatter signatures is a very computationally efficient and automated approach to provide pathological information in real-time to guide surgeon during tissue resection.

Blind breast tissue diagnosis using independent component analysis of localized backscattering response

A blind separation technique based on Independent Component Analysis (ICA) is proposed for breast tumor delineation and pathologic diagnosis. Tissue morphology is determined by fitting local measures of tissue reflectance to a Mie theory approximation, parameterizing the scattering power, scattering amplitude and average scattering irradiance. ICA is applied on the scattering parameters by spatial analysis using the Fast ICA method to extract more determinant features for an accurate diagnostic. Neither training, nor comparisons with reference parameters are required. Tissue diagnosis is provided directly following ICA application to the scattering parameter images. Surgically resected breast tissues were imaged and identified by a pathologist. Three different tissue pathologies were identified in 29 samples and classified as not-malignant, malignant and adipose. Scatter plot analysis of both ICA results and optical parameters where obtained. ICA subtle ameliorates those cases where optical parameters scatter plots were not linearly separable. Furthermore, observing the mixing matrix of the ICA, it can be decided when the optical parameters themselves are diagnostically powerful. Moreover, contrast maps provided by ICA correlate with the pathologic diagnosis. The time response of the diagnostic strategy is therefore enhanced comparing with complex classifiers, enabling near real-time assessment of pathology during breast-conserving surgery.