@prefix dcat: <http://www.w3.org/ns/dcat#> .
@prefix dct: <http://purl.org/dc/terms/> .
@prefix foaf: <http://xmlns.com/foaf/0.1/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00673453v1> a dcat:Dataset ;
    dct:description """
              A new approach for texture characterization of tumors based on a 2D S-Transform. Previous methods have already been developed using transforms such as the wavelet transform or the Gabor transform. However, these transforms are either not frequency invariant (wavelet transform) or have a fixed resolution (Gabor transform) To solve these problems we employ a 2D S-Transform. The S-Transform, a generalization of the Short-time Fourier transform, provides a time-frequency distribution of a signal. Therefore one can obtain the frequency content of a pixel or of a tumor ROI by averaging pixel spectrums over the tumor . A tool has been developed that computes the S-Transform in real time for a pixel and in 2 or 3 seconds for a tumor, while previous methods take much longer time. To quantify the tumor texture we compute statistics based on pixel spectrums. The first statistic, a texture curve, is the frequency vs its average power at each pixel or over the entire tumor. The second statistic, for a tumor, is a KL-divergence to calculate the deviation of histograms, obtained at each frequency, from a normal distribution. Finally, a map of the area under the texture curve for a band of frequencies shows the average power at each pixel of the tumor. First experiments on images of 20 tumor-bearing patients (10 for the training set, 10 for the test set) using the texture curves allowed us to classify homogeneous and heterogeneous tumors with an accuracy of around 80%.
            """ ;
    dct:identifier "hal-00673453" ;
    dct:issued "2026-05-27T06:26:56.051994"^^xsd:dateTime ;
    dct:language "en" ;
    dct:modified "2026-05-27T06:26:56.051999"^^xsd:dateTime ;
    dct:publisher <https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> ;
    dct:title "Texture Characterization of Tumors" ;
    dcat:contactPoint [ a vcard:Organization ;
            vcard:fn "CCSD" ] ;
    dcat:distribution <https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00673453v1/resource/15fc56d7-11be-42e8-87f6-5197d9da6bab> ;
    dcat:keyword "infoeu-reposemanticsconferenceobject",
        "infoinfo-imcomputer-science-csmedical-imaging",
        "machine-learning",
        "poster-communications",
        "pseudo-progression",
        "stockwell-transform",
        "texture-characterization",
        "tumor" ;
    dcat:landingPage <Care%20About%20Cancer%2C%20Edmonton%2C%20AB%2C%20Canada%2C%2016/06/2011-18/06/2011> .

<Care%20About%20Cancer%2C%20Edmonton%2C%20AB%2C%20Canada%2C%2016/06/2011-18/06/2011> a foaf:Document .

<https://rec.harvest-normandie.data4citizen.com/dataset/oai-hal-hal-00673453v1/resource/15fc56d7-11be-42e8-87f6-5197d9da6bab> a dcat:Distribution ;
    dct:format "HTML" ;
    dct:issued "2026-05-27T06:26:56.151460"^^xsd:dateTime ;
    dct:modified "2026-05-27T06:26:56.026228"^^xsd:dateTime ;
    dct:title "Texture Characterization of Tumors" ;
    dcat:accessURL <https://hal.science/hal-00673453> .

<https://rec.harvest-normandie.data4citizen.com/organization/cce9db95-46d9-4dc2-84b6-764215d0a002> a foaf:Agent ;
    foaf:name "test_moissonnage_selune" .