Proceedings Article | 17 March 2008
KEYWORDS: Surface plasmons, Tumors, Image segmentation, Breast, Image classification, Computer aided diagnosis and therapy, Magnetic resonance imaging, 3D acquisition, 3D image enhancement, Statistical analysis
This study was designed to classify contrast enhancement curves using both three-time-points (3TP) method and
clustering approach at full-time points, and to introduce a novel evaluation method using perfusion volume fractions for
differentiation of malignant and benign lesions. DCE-MRI was applied to 24 lesions (12 malignant, 12 benign). After
region growing segmentation for each lesion, hole-filling and 3D morphological erosion and dilation were performed for
extracting final lesion volume. 3TP method and k-means clustering at full-time points were applied for classifying
kinetic curves into six classes. Intratumoral volume fraction for each class was calculated. ROC and linear discriminant
analyses were performed with distributions of the volume fractions for each class, pairwise and whole classes,
respectively. The best performance in each class showed accuracy (ACC), 84.7% (sensitivity (SE), 100%; specificity
(SP), 66.7% to a single class) to 3TP method, whereas ACC, 73.6% (SE, 41.7%; SP, 100% to a single class) to k-means
clustering. The best performance in pairwise classes showed ACC, 75% (SE, 83.3%; SP, 66.7% to four class pairs and
SE, 58.3%; SP, 91.7% to a single class pair) to 3TP method and ACC, 75% (SE, 75%; SP, 75% to a single class pair and
SE, 66.7%; SP, 83.3% to three class pairs) to k-means clustering. The performance in whole classes showed ACC, 75%
(SE, 83.3%; SP, 66.7%) to 3TP method and ACC, 75% (SE, 91.7%; 58.3%) to k-means clustering. The results indicate
that tumor classification using perfusion volume fractions is helpful in selecting meaningful kinetic patterns for
differentiation of malignant and benign lesions, and that two different classification methods are complementary to each
other.
