Automatic and Unsupervised Identification of Specific Biochemical Features from Raman Mapping Data

Torti, Emanuele; Marcinnò, Beatrice; Vanna, Renzo; Morasso, Carlo; Picotti, Francesca; Villani, Laura; Leporati, Francesco

doi:10.1109/dsd.2019.00073

Cited by 2 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Spectral classification was carried out to assign specific biochemical features (i.e., to identify specific components) to each map data point (i.e., for each single spectrum acquired). For each spectrum, three different indices (i.e., monotonicity of the spectra, correlation with reference spectra, and specific peaks positions) were evaluated and used to perform spectral classification (24). Spectra described by a monotonic function are those for which the functions preserve the given order.…”

Section: Data Processing Of Raman Datamentioning

confidence: 99%

Raman Spectroscopy Reveals That Biochemical Composition of Breast Microcalcifications Correlates with Histopathologic Features

et al. 2020

Self Cite

View full text Add to dashboard Cite

Breast microcalcifications are a common mammographic finding. Microcalcifications are considered suspicious signs of breast cancer and a breast biopsy is required, however, cancer is diagnosed in only a few patients. Reducing unnecessary biopsies and rapid characterization of breast microcalcifications are unmet clinical needs. In this study, 473 microcalcifications detected on breast biopsy specimens from 56 patients were characterized entirely by Raman mapping and confirmed by X-ray scattering. Microcalcifications from malignant samples were generally more homogeneous, more crystalline, and characterized by a less substituted crystal lattice compared with benign samples. There were significant differences in Raman features corresponding to the phosphate and carbonate bands between the benign and malignant groups. In addition to the heterogeneous composition, the presence of whitlockite specifically emerged as marker of benignity in benign microcalcifications. The whole Raman signature of each microcal-cification was then used to build a classification model that distinguishes microcalcifications according to their overall biochemical composition. After validation, microcalcifications found in benign and malignant samples were correctly recognized with 93.5% sensitivity and 80.6% specificity. Finally, microcalcifications identified in malignant biopsies, but located outside the lesion, reported malignant features in 65% of in situ and 98% of invasive cancer cases, respectively, suggesting that the local microenvironment influences microcalcification features. This study confirms that the composition and structural features of microcalcifications correlate with breast pathology and indicates new diagnostic potentialities based on microcalcifications assessment.Significance: Raman spectroscopy could be a quick and accurate diagnostic tool to precisely characterize and distinguish benign from malignant breast microcalcifications detected on mammography.

show abstract

Section: Data Processing Of Raman Datamentioning

confidence: 99%

Raman Spectroscopy Reveals That Biochemical Composition of Breast Microcalcifications Correlates with Histopathologic Features

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Hyperspectral image classification aided by LiDAR data

Zheng

Zhao

et al. 2023

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

View full text Add to dashboard Cite

Spectral variability and shadow effects can limit the hyperspectral image (HSI) classification performance. Compared with HSI, the LiDAR data is an excellent complement with its abundant elevation information. In this study, a procedure including pre-processing, deep residual network classification and post-processing is investigated for classification of HSI aided by the LiDAR data to release the problem of identifying shaded objects and spectral variability. Specifically, three aspects with respect to spectral band selection using Archetypal Analysis (AA), feature level fusion based classification by deploying a residual network associated approach and label correction utilizing the elevation information, are explored to realize more accurate classification. Experiments on three public multi-source (hyperspectral and LiDAR) remote sensing datasets show more promising classification can be achieved via fusion two-source of remote sensing data than that using only independent hyperspectral image. In particular, on the Houston 2017 dataset, OA and Kappa achieved significant gains of 2.47% and 2.79% respectively after incorporating LiDAR information. Moreover, the results demonstrate the elevation information used independently in the post-processing stage can help with effective refinement of classification results.

show abstract

Automatic and Unsupervised Identification of Specific Biochemical Features from Raman Mapping Data

Cited by 2 publications

References 17 publications

Raman Spectroscopy Reveals That Biochemical Composition of Breast Microcalcifications Correlates with Histopathologic Features

Raman Spectroscopy Reveals That Biochemical Composition of Breast Microcalcifications Correlates with Histopathologic Features

Hyperspectral image classification aided by LiDAR data

Contact Info

Product

Resources

About