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A New GLLD Operator for Mass Detection in Digital Mammograms.

Gargouri N, Dammak Masmoudi A, Sellami Masmoudi D, Abid R - Int J Biomed Imaging (2012)

Bottom Line: We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM).The area under the curve of the corresponding approach has been found to be A(z) = 0.95 for the mass detection step.A comparative study with previous approaches proves that our approach offers the best performances.

View Article: PubMed Central - PubMed

Affiliation: Computer Imaging and Electronic System Group, CEM Laboratory, Department of Electrical Engineering, Sfax Engineering School, University of Sfax, P.O. Box 1169, 3038 Sfax, Tunisia.

ABSTRACT
During the last decade, several works have dealt with computer automatic diagnosis (CAD) of masses in digital mammograms. Generally, the main difficulty remains the detection of masses. This work proposes an efficient methodology for mass detection based on a new local feature extraction. Local binary pattern (LBP) operator and its variants proposed by Ojala are a powerful tool for textures classification. However, it has been proved that such operators are not able to model at their own texture masses. We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM). The area under the curve of the corresponding approach has been found to be A(z) = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.

No MeSH data available.


Related in: MedlinePlus

Example of basic LBP operator.
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fig1: Example of basic LBP operator.

Mentions: Accordingly and referring to Figure 1, LBP [29] code may be computed as follows:(2)LBPP,R=∑p=0P−1s(gp−gc)2p,s(x)={1,x≥00,x<0,where gc corresponds to the gray value of the central pixel, gp corresponds to the value of its neighbors, and (p = 0,1,…, P − 1) and P, R correspond to the number of neighbors and to the radius of the neighborhood, respectively. The binary code is then represented with an 8-bit number. gp coordinates are ((R cos⁡(2πp/P) and R sin(2πp/P)). If neighbors are not in the image grids, their gray values may be estimated by interpolation. After identifying LBP pattern of each pixel (i, j), we associate LBP histogram to the whole image, with a given image size (N1∗N2) as(3)HLBP(k)=∑i=1 N1∑j=1N2f(LBPP,R(i,j),k), k∈[0,K],f(x,y)={1,x=y0,otherwise,where K corresponds to the maximum gray level value.


A New GLLD Operator for Mass Detection in Digital Mammograms.

Gargouri N, Dammak Masmoudi A, Sellami Masmoudi D, Abid R - Int J Biomed Imaging (2012)

Example of basic LBP operator.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3539378&req=5

fig1: Example of basic LBP operator.
Mentions: Accordingly and referring to Figure 1, LBP [29] code may be computed as follows:(2)LBPP,R=∑p=0P−1s(gp−gc)2p,s(x)={1,x≥00,x<0,where gc corresponds to the gray value of the central pixel, gp corresponds to the value of its neighbors, and (p = 0,1,…, P − 1) and P, R correspond to the number of neighbors and to the radius of the neighborhood, respectively. The binary code is then represented with an 8-bit number. gp coordinates are ((R cos⁡(2πp/P) and R sin(2πp/P)). If neighbors are not in the image grids, their gray values may be estimated by interpolation. After identifying LBP pattern of each pixel (i, j), we associate LBP histogram to the whole image, with a given image size (N1∗N2) as(3)HLBP(k)=∑i=1 N1∑j=1N2f(LBPP,R(i,j),k), k∈[0,K],f(x,y)={1,x=y0,otherwise,where K corresponds to the maximum gray level value.

Bottom Line: We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM).The area under the curve of the corresponding approach has been found to be A(z) = 0.95 for the mass detection step.A comparative study with previous approaches proves that our approach offers the best performances.

View Article: PubMed Central - PubMed

Affiliation: Computer Imaging and Electronic System Group, CEM Laboratory, Department of Electrical Engineering, Sfax Engineering School, University of Sfax, P.O. Box 1169, 3038 Sfax, Tunisia.

ABSTRACT
During the last decade, several works have dealt with computer automatic diagnosis (CAD) of masses in digital mammograms. Generally, the main difficulty remains the detection of masses. This work proposes an efficient methodology for mass detection based on a new local feature extraction. Local binary pattern (LBP) operator and its variants proposed by Ojala are a powerful tool for textures classification. However, it has been proved that such operators are not able to model at their own texture masses. We propose in this paper a new local pattern model named gray level and local difference (GLLD) where we take into consideration absolute gray level values as well as local difference as local binary features. Artificial neural networks (ANNs), support vector machine (SVM), and k-nearest neighbors (kNNs) are, then, used for classifying masses from nonmasses, illustrating better performance of ANN classifier. We have used 1000 regions of interest (ROIs) obtained from the Digital Database for Screening Mammography (DDSM). The area under the curve of the corresponding approach has been found to be A(z) = 0.95 for the mass detection step. A comparative study with previous approaches proves that our approach offers the best performances.

No MeSH data available.


Related in: MedlinePlus