Premium
False‐positive reduction technique for detection of masses on digital mammograms: Global and local multiresolution texture analysis
Author(s) -
Wei Datong,
Chan HeangPing,
Petrick Nicholas,
Sahiner Berkman,
Helvie Mark A.,
Adler Dorit D.,
Goodsitt Mitchell M.
Publication year - 1997
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.598011
Subject(s) - digital mammography , reduction (mathematics) , texture (cosmology) , artificial intelligence , medical imaging , computer vision , mammography , computer science , digital imaging , pattern recognition (psychology) , mathematics , image processing , nuclear medicine , digital image , medicine , image (mathematics) , breast cancer , cancer , geometry
We investigated the application of multiresolution global and local texture features to reduce false‐positive detection in a computerized mass detection program. One hundred and sixty‐eight digitized mammograms were randomly and equally divided into training and test groups. From these mammograms, two datasets were formed. The first dataset (manual) contained four regions of interest (ROIs) selected manually from each of the mammograms. One of the four ROIs contained a biopsy‐proven mass and the other three contained normal parenchyma, including dense, mixed dense/fatty, and fatty tissues. The second dataset (hybrid) contained the manually extracted mass ROIs, along with normal tissue ROIs extracted by an automated Density‐Weighted Contrast Enhancement (DWCE) algorithm as false‐positive detections. A wavelet transform was used to decompose an ROI into several scales. Global texture features were derived from the low‐pass coefficients in the wavelet transformed images. Local texture features were calculated from the suspicious object and the peripheral subregions. Linear discriminant models using effective features selected from the global, local, or combined feature spaces were established to maximize the separation between masses and normal tissue. Receiver Operating Characteristic (ROC) analysis was conducted to evaluate the classifier performance. The classification accuracy using global features were comparable to that using local features. With both global and local features, the average area, A z , under the test ROC curve, reached 0.92 for the manual dataset and 0.96 for the hybrid dataset, demonstrating statistically significant improvement over those obtained with global or local features alone. The results indicated the effectiveness of the combined global and local features in the classification of masses and normal tissue for false‐positive reduction.