Analysis of breast lesions on contrast-enhanced magnetic resonance images using high-dimensional texture features
2010
Haralick texture features derived from gray-level co-occurrence matrices (GLCM) were used to classify the character of
suspicious breast lesions as benign or malignant on dynamic contrast-enhanced MRI studies. Lesions were identified and
annotated by an experienced radiologist on 54 MRI exams of female patients where histopathological reports were
available prior to this investigation. GLCMs were then extracted from these 2D regions of interest (ROI) for four
principal directions (0°, 45°, 90° & 135°) and used to compute Haralick texture features. A fuzzy k-nearest neighbor (k-
NN) classifier was optimized in ten-fold cross-validation for each texture feature and the classification performance was
calculated on an independent test set as a function of area under the ROC curve. The lesion ROIs were characterized by
texture feature vectors containing the Haralick feature values computed from each directional-GLCM; and the classifier
results obtained were compared to a previously used approach where the directional-GLCMs were summed to a nondirectional
GLCM which could further yield a set of texture feature values. The impact of varying the inter-pixel
distance while generating the GLCMs on the classifier's performance was also investigated. Classifier's AUC was found
to significantly increase when the high-dimensional texture feature vector approach was pursued, and when features
derived from GLCMs generated using different inter-pixel distances were incorporated into the classification task. These
results indicate that lesion character classification accuracy could be improved by retaining the texture features derived
from the different directional GLCMs rather than combining these to yield a set of scalar feature values instead.
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