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Improvement of mammographic mass characterization using spiculation meausures and morphological features.
We are developing new computer vision techniques for characterization of breast masses on mammograms. We had previously developed a characterization method based on texture features. The goal of theExpand
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Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images
TLDR
The authors investigated the classification of regions of interest (ROI's) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a backpropagation neural network with two-dimensional (2-D) weight kernels that operate on images. The input images to the CNN were obtained from the ROI's using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods. Expand
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Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces.
We are developing computerized feature extraction and classification methods to analyze malignant and benign microcalcifications on digitized mammograms. Morphological features that described theExpand
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NCCN clinical practice guidelines in oncology: breast cancer screening and diagnosis.
These guidelines are a statement of evidence and consensus of the authors regarding their views of currently accepted approaches to treatment. Any clinician seeking to apply or consult theseExpand
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Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis.
A new rubber band straightening transform (RBST) is introduced for characterization of mammographic masses as malignant or benign. The RBST transforms a band of pixels surrounding a segmented massExpand
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Digital mammography imaging: breast tomosynthesis and advanced applications.
  • M. Helvie
  • Medicine
  • Radiologic clinics of North America
  • 1 September 2010
This article discusses recent developments in advanced derivative technologies associated with digital mammography. Digital breast tomosynthesis, its principles, development, and early clinicalExpand
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Improvement of computerized mass detection on mammograms: fusion of two-view information.
Recent clinical studies have proved that computer-aided diagnosis (CAD) systems are helpful for improving lesion detection by radiologists in mammography. However, these systems would be more usefulExpand
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Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space.
We studied the effectiveness of using texture features derived from spatial grey level dependence (SGLD) matrices for classification of masses and normal breast tissue on mammograms. One hundred andExpand
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Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization
TLDR
An automated mass segmentation method developed in our laboratory was quantitatively compared with manual segmentation by two expert radiologists (R1 and R2) using three similarity or distance measures on a data set of 100 masses. Expand
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Image feature selection by a genetic algorithm: application to classification of mass and normal breast tissue.
We investigated a new approach to feature selection, and demonstrated its application in the task of differentiating regions of interest (ROIs) on mammograms as either mass or normal tissue. TheExpand
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