Journal ArticleDOI
Classifying mammographic lesions using computerized image analysis
TLDR
The classification of 3 common breast lesions, fibroadenomas, cysts, and cancers, was achieved using computerized image analysis of tumor shape in conjunction with patient age using a video camera and commercial frame grabber on a PC-based computer system.Abstract:
The classification of 3 common breast lesions, fibroadenomas, cysts, and cancers, was achieved using computerized image analysis of tumor shape in conjunction with patient age. The process involved the digitization of 69 mammographic images using a video camera and a commercial frame grabber on a PC-based computer system. An interactive segmentation procedure identified the tumor boundary using a thresholding technique which successfully segmented 57% of the lesions. Several features were chosen based on the gross and fine shape describing properties of the tumor boundaries as seen on the radiographs. Patient age was included as a significant feature in determining whether the tumor was a cyst, fibroadenoma, or cancer and was the only patient history information available for this study. The concept of a radial length measure provided a basis from which 6 of the 7 shape describing features were chosen, the seventh being tumor circularity. The feature selection process was accomplished using linear discriminant analysis and a Euclidean distance metric determined group membership. The effectiveness of the classification scheme was tested using both the apparent and the leaving-one-out test methods. The best results using the apparent test method resulted in correctly classifying 82% of the tumors segmented using the entire feature space and the highest classification rate using the leaving-one-out test method was 69% using a subset of the feature space. The results using only the shape descriptors, and excluding patient age resulted in correctly classifying 72% using the entire feature space (except age), and 51% using a subset of the feature space. >read more
Citations
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Journal ArticleDOI
Approaches for automated detection and classification of masses in mammograms
TL;DR: The methods for mass detection and classification for breast cancer diagnosis are discussed, and their advantages and drawbacks are compared.
Journal ArticleDOI
Measures of acutance and shape for classification of breast tumors
TL;DR: A region-based measure of image edge profile acutance is proposed which characterizes the transition in density of a region of interest (ROI) along normals to the ROI at every boundary pixel and indicates the importance of including lesion edge definition with shape information for classification of tumors.
Journal ArticleDOI
Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study.
Heang Ping Chan,Berkman Sahiner,Mark A. Helvie,Nicholas Petrick,Marilyn A. Roubidoux,Todd E. Wilson,Dorit D. Adler,Chintana Paramagul,Joel S. Newman,Sethumadavan Sanjay-Gopal +9 more
TL;DR: CAD may be useful for assisting radiologists in classification of masses and thereby potentially help reduce unnecessary biopsies, as predicted from the improved ROC curves.
Journal ArticleDOI
Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis
TL;DR: A new rubber band straightening transform (RBST) is introduced for characterization of mammographic masses as malignant or benign, and features extracted from the RBST images were found to be significantly more effective than those extracting from the original images.
References
More filters
Journal ArticleDOI
Considerations of sample and feature size
TL;DR: The design-set error rate for a two-class problem with multivariate normal distributions is derived as a function of the sample size per class (N) and dimensionality (L) and is demonstrated to be an extremely biased estimate of either the Bayes or test- set error rate.
Journal ArticleDOI
An approach to automated detection of tumors in mammograms
TL;DR: An automated system for detecting and classifying particular types of tumors in digitized mammograms is described, which uses a classification hierarchy to identify benign and malignant tumors.
Journal ArticleDOI
On techniques for detecting circumscribed masses in mammograms
S.-M. Lai,X. Li,W.F. Biscof +2 more
TL;DR: A method for detecting one type of breast tumor, circumscribed masses, in mammograms is presented, which relies on a combination of criteria used by experts, including the shape, brightness contrast, and uniform density of tumor areas.
Journal ArticleDOI
Why women resist screening mammography: patient-related barriers.
TL;DR: A woman's belief that her doctor believes in regular mammography was an important predictor of compliance, and the former were more likely to believe that mammography is unnecessary in the absence of symptoms and that it is inconvenient.
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Enhancement of Mammographic Features by Optimal Adaptive Neighborhood Image Processing
TL;DR: This procedure brings out the features in the image with little or no enhancement of the noise, and finds that adaptive Neighborhoods with surrounds whose width is a constant difference from the center yield improved enhancement over adaptive neighborhoods with a constant ratio of surround to center neighborhood widths.