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Med Phys. 2017 Jul;44(7):3695-3705. doi: 10.1002/mp.12291. Epub 2017 May 29.

Focal liver lesions segmentation and classification in nonenhanced T2-weighted MRI.

Author information

1
Department of Medical Physics, School of Medicine, University of Patras, Rion, GR, 26504, Greece.
2
Department of Radiology, Magnitiki Patron Diagnostic Center, 105 Othonos-Amalias st, Patras, GR, 26222, Greece.
3
2nd Department of Radiology, School of Medicine, University of Athens, Athens, GR, 12461, Greece.
4
Department of Radiology, School of Medicine, University of Patras, Rion, GR, 26504, Greece.
5
Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.

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.

KEYWORDS:

chronic liver disease; classification; images analysis; magnetic resonance imaging; segmentation

PMID:
28432822
DOI:
10.1002/mp.12291
[Indexed for MEDLINE]

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