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Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound
- Source :
- IEEE transactions on medical imaging. 31(10)
- Publication Year :
- 2012
-
Abstract
- In this paper, we investigated the behavior of 22 co-occurrence statistics combined to six gray-scale quantization levels to classify breast lesions on ultrasound (BUS) images. The database of 436 BUS images used in this investigation was formed by 217 carcinoma and 219 benign lesions images. The region delimited by a minimum bounding rectangle around the lesion was employed to calculate the gray-level co-occurrence matrix (GLCM). Next, 22 co-occurrence statistics were computed regarding six quantization levels (8, 16, 32, 64, 128, and 256), four orientations (0° , 45° , 90° , and 135°), and ten distances (1, 2,...,10 pixels). Also, to reduce feature space dimensionality, texture descriptors of the same distance were averaged over all orientations, which is a common practice in the literature. Thereafter, the feature space was ranked using mutual information technique with minimal-redundancy-maximal-relevance (mRMR) criterion. Fisher linear discriminant analysis (FLDA) was applied to assess the discrimination power of texture features, by adding the first m-ranked features to the classification procedure iteratively until all of them were considered. The area under ROC curve (AUC) was used as figure of merit to measure the performance of the classifier. It was observed that averaging texture descriptors of a same distance impacts negatively the classification performance, since the best AUC of 0.81 was achieved with 32 gray levels and 109 features. On the other hand, regarding the single texture features (i.e., without averaging procedure), the quantization level does not impact the discrimination power, since AUC = 0.87 was obtained for the six quantization levels. Moreover, the number of features was reduced (between 17 and 24 features). The texture descriptors that contributed notably to distinguish breast lesions were contrast and correlation computed from GLCMs with orientation of 90° and distance more than five pixels.
- Subjects :
- Databases, Factual
Feature vector
Feature extraction
Breast Neoplasms
Image texture
Statistics
Image Processing, Computer-Assisted
Humans
Computer vision
Electrical and Electronic Engineering
Mathematics
Radiological and Ultrasound Technology
Pixel
Contextual image classification
business.industry
Pattern recognition
Mutual information
Linear discriminant analysis
Computer Science Applications
Co-occurrence matrix
ROC Curve
Area Under Curve
Female
Artificial intelligence
Ultrasonography, Mammary
business
Software
Algorithms
Subjects
Details
- ISSN :
- 1558254X
- Volume :
- 31
- Issue :
- 10
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging
- Accession number :
- edsair.doi.dedup.....7e1895f9c7d47590e92d94d451eb146f