@article{3086014,
    title = "Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI:",
    author = "Gatos, I. and Tsantis, S. and Karamesini, M. and Spiliopoulos, S. and Karnabatidis, D. and Hazle, J.D. and Kagadis, G.C.",
    journal = "Medical Physics",
    year = "2017",
    volume = "44",
    number = "7",
    pages = "3695-3705",
    publisher = "Wiley-Blackwell",
    issn = "0094-2405",
    doi = "10.1002/mp.12291",
    keywords = "Classification (of information);  Clustering algorithms;  Copying;  Fuzzy clustering;  Image segmentation;  Magnetic resonance imaging;  Regression analysis;  Textures, Chronic liver disease;  Computer Aided Diagnosis(CAD);  Fuzzy C-means algorithms;  Hepatocellular carcinoma;  images analysis;  Multilinear regression analysis;  Probabilistic neural networks;  Receiver operating characteristic curve analysis, Computer aided diagnosis, Article;  artificial neural network;  cancer classification;  cancer diagnosis;  clinical article;  controlled study;  diagnostic accuracy;  diagnostic test accuracy study;  diffusion weighted imaging;  human;  image analysis;  image segmentation;  liver cell carcinoma;  liver metastasis;  liver parenchyma;  liver tumor;  nuclear magnetic resonance imaging;  nuclear magnetic resonance scanner;  radiodiagnosis;  sensitivity and specificity;  support vector machine;  algorithm;  diagnostic imaging;  liver cell carcinoma;  liver tumor;  receiver operating characteristic, contrast medium, Algorithms;  Carcinoma, Hepatocellular;  Contrast Media;  Humans;  Liver Neoplasms;  Magnetic Resonance Imaging;  ROC Curve;  Sensitivity and Specificity",
    abstract = "Purpose: To automatically segment and classify focal liver lesions (FLLs) on nonenhanced T2-weighted magnetic resonance imaging (MRI) scans using a computer-aided diagnosis (CAD) algorithm. Methods: 71 FLLs (30 benign lesions, 19 hepatocellular carcinomas, and 22 metastases) on T2-weighted MRI scans were delineated by the proposed CAD scheme. The FLL segmentation procedure involved wavelet multiscale analysis to extract accurate edge information and mean intensity values for consecutive edges computed using horizontal and vertical analysis that were fed into the subsequent fuzzy C-means algorithm for final FLL border extraction. Texture information for each extracted lesion was derived using 42 first- and second-order textural features from grayscale value histogram, co-occurrence, and run-length matrices. Twelve morphological features were also extracted to capture any shape differentiation between classes. Feature selection was performed with stepwise multilinear regression analysis that led to a reduced feature subset. A multiclass Probabilistic Neural Network (PNN) classifier was then designed and used for lesion classification. PNN model evaluation was performed using the leave-one-out (LOO) method and receiver operating characteristic (ROC) curve analysis. Results: The mean overlap between the automatically segmented FLLs and the manual segmentations performed by radiologists was 0.91 ± 0.12. The highest classification accuracies in the PNN model for the benign, hepatocellular carcinoma, and metastatic FLLs were 94.1%, 91.4%, and 94.1%, respectively, with sensitivity/specificity values of 90%/97.3%, 89.5%/92.2%, and 90.9%/95.6% respectively. The overall classification accuracy for the proposed system was 90.1%. Conclusions: Our diagnostic system using sophisticated FLL segmentation and classification algorithms is a powerful tool for routine clinical MRI-based liver evaluation and can be a supplement to contrast-enhanced MRI to prevent unnecessary invasive procedures. © 2017 American Association of Physicists in Medicine."
}