NDLI: Analysis of Co-Occurrence Texture Statistics as a Function of Gray-Level Quantization for Classifying Breast Ultrasound

Content Provider	IEEE Xplore Digital Library
Author	Gomez, W. Pereira, W.C.A. Infantosi, A.F.C.
Copyright Year	1982
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.
Sponsorship	IEEE Engineering in Medicine and Biology Society IEEE Nuclear and Plasma Sciences Society IEEE Signal Processing Society IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society
Page Count	11
File Size	2436234
Starting Page	1889
Ending Page	1899
File Format	PDF
ISSN	02780062
Volume Number	31
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Quantization Lesions Breast Ultrasonic imaging Cancer Feature extraction Mutual information tumor classification Breast ultrasound co-occurrence matrix quantization level
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications Radiological and Ultrasound Technology Software

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