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Twenty two textural features were calculated from small matrix samples selected from the ultrasound image matrix of the liver parenchyma. These features were used in the design a three level hierarchical decision tree classification scheme, employing the multilayer perceptron neural network classifier at 'diabetes mellitus' each hierarchical tree level. At the first tree level, classification 'diabetes mellitus' accuracy for distinguishing normal from abnormal livers was 93.7%, at the second level the accuracy for discriminating cirrhosis from fatty infiltration was 90.9%, and at the 'diabetes mellitus' third level the accuracy in distinguishing between low and high grading liver cirrhosis or fatty infiltration was 94.1% and 84.9% respectively. The proposed computer software system may be of value to the radiologists in assessing liver parenchyma disease.PMID: 10179720 [PubMed - indexed for MEDLINE] Display Summary Brief Abstract Citation MEDLINE XML UI List LinkOut ASN.1 Related Articles Cited Articles Cited in Books CancerChrom Links Domain Links 3D Domain Links GEO DataSet Links Gene Links Gene (GeneRIF) Links Genome Links Project Links GENSAT Links GEO Profile Links HomoloGene Links Nucleotide Links OMIA Links OMIM (calculated) Links OMIM (cited) Links BioAssay Links Compound Links Compound via MeSH Substance Links Substance via MeSH PMC Links Cited in PMC PopSet Links Probe Links Protein Links SNP Links Structure Links UniGene Links UniSTS Links Show
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