120 research outputs found

    Unsupervised clustering of hyperspectral images of brain tissues by hierarchical non-negative matrix factorization

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    International audienceHyperspectral images of high spatial and spectral resolutions are employed to perform the challenging task of brain tissue characterization and subsequent segmentation for visualization of in-vivo images. Each pixel is a high-dimensional spectrum. Working on the hypothesis of pure-pixels on account of high spectral resolution, we perform unsupervised clustering by hierarchical non-negative matrix factorization to identify the pure-pixel spectral signatures of blood, brain tissues, tumor and other materials. This subspace clustering was further used to train a random forest for subsequent classification of test set images constituent of in-vivo and ex-vivo images. Unsupervised hierarchical clustering helps visualize tissue structure in in-vivo test images and provides a inter-operative tool for surgeons. Furthermore the study also provides a preliminary study of the classification and sources of errors in the classification process

    Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

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    Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area

    Multiple Statistical Analysis Techniques Corroborate Intratumor Heterogeneity in Imaging Mass Spectrometry Datasets of Myxofibrosarcoma

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    MALDI mass spectrometry can generate profiles that contain hundreds of biomolecular ions directly from tissue. Spatially-correlated analysis, MALDI imaging MS, can simultaneously reveal how each of these biomolecular ions varies in clinical tissue samples. The use of statistical data analysis tools to identify regions containing correlated mass spectrometry profiles is referred to as imaging MS-based molecular histology because of its ability to annotate tissues solely on the basis of the imaging MS data. Several reports have indicated that imaging MS-based molecular histology may be able to complement established histological and histochemical techniques by distinguishing between pathologies with overlapping/identical morphologies and revealing biomolecular intratumor heterogeneity. A data analysis pipeline that identifies regions of imaging MS datasets with correlated mass spectrometry profiles could lead to the development of novel methods for improved diagnosis (differentiating subgroups within distinct histological groups) and annotating the spatio-chemical makeup of tumors. Here it is demonstrated that highlighting the regions within imaging MS datasets whose mass spectrometry profiles were found to be correlated by five independent multivariate methods provides a consistently accurate summary of the spatio-chemical heterogeneity. The corroboration provided by using multiple multivariate methods, efficiently applied in an automated routine, provides assurance that the identified regions are indeed characterized by distinct mass spectrometry profiles, a crucial requirement for its development as a complementary histological tool. When simultaneously applied to imaging MS datasets from multiple patient samples of intermediate-grade myxofibrosarcoma, a heterogeneous soft tissue sarcoma, nodules with mass spectrometry profiles found to be distinct by five different multivariate methods were detected within morphologically identical regions of all patient tissue samples. To aid the further development of imaging MS based molecular histology as a complementary histological tool the Matlab code of the agreement analysis, instructions and a reduced dataset are included as supporting information

    Signal and image processing methods for imaging mass spectrometry data

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    Imaging mass spectrometry (IMS) has evolved as an analytical tool for many biomedical applications. This thesis focuses on algorithms for the analysis of IMS data produced by matrix assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometer. IMS provides mass spectra acquired at a grid of spatial points that can be represented as hyperspectral data or a so-called datacube. Analysis of this large and complex data requires efficient computational methods for matrix factorization and for spatial segmentation. In this thesis, state of the art processing methods are reviewed, compared and improved versions are proposed. Mathematical models for peak shapes are reviewed and evaluated. A simulation model for MALDI-TOF is studied, expanded and developed into a simulator for 2D or 3D MALDI-TOF-IMS data. The simulation approach paves way to statistical evaluation of algorithms for analysis of IMS data by providing a gold standard dataset. [...

    Efficient quantitative hyperspectral image unmixing method for large-scale Raman micro-spectroscopy data analysis

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    Vibrational micro-spectroscopy is a powerful optical tool, providing a non-invasive label-free chemically specific imaging for many chemical and biomedical applications. However, hyperspectral image produced by Raman micro-spectroscopy typically consists of thousands discrete pixel points, each having individual Raman spectrum at thousand wavenumbers, and therefore requires appropriate image unmixing computational methods to retrieve non-negative spatial concentration and corresponding non-negative spectra of the image biochemical constituents. Here, we present a new efficient Quantitative Hyperspectral Image Unmixing (Q-HIU) method for large-scale Raman micro-spectroscopy data analysis. This method enables to simultaneously analyse multi-set Raman hyperspectral images in three steps: (i) Singular Value Decomposition with innovative Automatic Divisive Correlation which autonomously filters spatially and spectrally uncorrelated noise from data; (ii) a robust subtraction of fluorescent background from the data using a newly developed algorithm called Bottom Gaussian Fitting; (iii) an efficient Quantitative Unsupervised/Partially Supervised Non-negative Matrix Factorization method, which rigorously retrieves non-negative spatial concentration maps and spectral profiles of the samples' biochemical constituents with no a priori information or when one or several samples’ constituents are known. As compared with state-of-the-art methods, our approach allows to achieve significantly more accurate results and efficient quantification with several orders of magnitude shorter computational time as verified on both artificial and real experimental data. We apply Q-HIU to the analysis of large-scale Raman hyperspectral images of human atherosclerotic aortic tissues and our results show a proof-of-principle for the proposed method to retrieve and quantify the biochemical composition of the tissues, consisting of both high and low concentrated compounds. Along with the established hallmarks of atherosclerosis including cholesterol/cholesterol ester, triglyceride and calcium hydroxyapatite crystals, our Q-HIU allowed to identify the significant accumulations of oxidatively modified lipids co-localizing with the atherosclerotic plaque lesions in the aortic tissues, possibly reflecting the persistent presence of inflammation and oxidative damage in these regions, which are in turn able to promote the disease pathology. For minor chemical components in the diseased tissues, our Q-HIU was able to detect the signatures of calcium hydroxyapatite and β-carotene with relative mean Raman concentrations as low as 0.09% and 0.04% from the original Raman intensity matrix with noise and fluorescent background contributions of 3% and 94%, respectively

    Factor analysis of dynamic PET images

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    Thanks to its ability to evaluate metabolic functions in tissues from the temporal evolution of a previously injected radiotracer, dynamic positron emission tomography (PET) has become an ubiquitous analysis tool to quantify biological processes. Several quantification techniques from the PET imaging literature require a previous estimation of global time-activity curves (TACs) (herein called \textit{factors}) representing the concentration of tracer in a reference tissue or blood over time. To this end, factor analysis has often appeared as an unsupervised learning solution for the extraction of factors and their respective fractions in each voxel. Inspired by the hyperspectral unmixing literature, this manuscript addresses two main drawbacks of general factor analysis techniques applied to dynamic PET. The first one is the assumption that the elementary response of each tissue to tracer distribution is spatially homogeneous. Even though this homogeneity assumption has proven its effectiveness in several factor analysis studies, it may not always provide a sufficient description of the underlying data, in particular when abnormalities are present. To tackle this limitation, the models herein proposed introduce an additional degree of freedom to the factors related to specific binding. To this end, a spatially-variant perturbation affects a nominal and common TAC representative of the high-uptake tissue. This variation is spatially indexed and constrained with a dictionary that is either previously learned or explicitly modelled with convolutional nonlinearities affecting non-specific binding tissues. The second drawback is related to the noise distribution in PET images. Even though the positron decay process can be described by a Poisson distribution, the actual noise in reconstructed PET images is not expected to be simply described by Poisson or Gaussian distributions. Therefore, we propose to consider a popular and quite general loss function, called the β\beta-divergence, that is able to generalize conventional loss functions such as the least-square distance, Kullback-Leibler and Itakura-Saito divergences, respectively corresponding to Gaussian, Poisson and Gamma distributions. This loss function is applied to three factor analysis models in order to evaluate its impact on dynamic PET images with different reconstruction characteristics
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