Improvement of mammographic mass characterization using spiculation measures and morphological features

Medical Physics

Volumn 28, Issue 7, 2001, Pages 1455-1465

  Sahiner, B ^a   Chan, H P ^a   Petrick, N ^a   Helvie, M A ^a   Hadjiiski, L M ^a  

^a UNIVERSITY OF MICHIGAN MEDICAL SCHOOL   (United States)

Author keywords

Breast mass characterization; Computer aided diagnosis; Mammography; Morphological features; Segmentation

Indexed keywords

COMPUTER AIDED DIAGNOSIS; DISCRIMINANT ANALYSIS; IMAGE SEGMENTATION; STATISTICAL TESTS; TEXTURES;

BREAST MASS; CHARACTERIZATION METHODS; CLASSIFICATION ACCURACY; COMPUTER VISION TECHNIQUES; EFFECTIVE APPROACHES; LINEAR DISCRIMINANT ANALYSIS; MORPHOLOGICAL FEATURES; RECEIVER OPERATING CHARACTERISTIC CURVES;

MAMMOGRAPHY;

ARTICLE; BREAST TUMOR; CANCER CLASSIFICATION; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; HUMAN; MAJOR CLINICAL STUDY; MAMMOGRAPHY; MORPHOLOGY; PRIORITY JOURNAL;

EID: 0034917794     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.1381548     Document Type: Article

References (46)

1. Nonpalpable breast lesions: Accuracy of prebiopsy mammographic diagnosis
2. Nonpalpable breast lesions: Recommendations for biopsy based on suspicion of carcinoma at mammography
3. Biopsy of occult breast lesions: Analysis of 1261 abnormalities
4. Improvement of radiologists' characterization of mammographic masses by computer-aided diagnosis: An ROC study
5. Classifying mammographic lesions using computer-aided image analysis
6. Quantitative classification of breast tumors in digitized mammograms
7. Discrimination between benign and malignant breast tumors using a region-based measure of edge profile acutance
8. Method for characterizing masses in digital mammograms
9. Automated computerized classification of malignant and benign masses on digitized mammograms
10. Computerized characterization of masses on mammograms: The rubber band straightening transform and texture analysis
11. Enhanced interpretation of diagnostic images
12. Computer-aided diagnosis of breast cancer: Artificial neural network approach for optimized merging of mammographic features
13. Artificial neural network: Improving the quality of breast biopsy recommendations
14. Breast cancer: Prediction with artificial neural network based on BIRADS standardized lexicon
15. Mammographic feature analysis
16. Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images
17. Computer-aided classification of mammographic masses and normal tissue: Linear discriminant analysis in texture feature space
18. Classification of mass and normal breast tissue: An artificial neural network with morphological features
19. Image feature selection by a genetic algorithm: Application to classification of mass and normal breast tissue on mammograms
20. Snakes: Active contour models
21. Image segmentation by a deformable contour model incorporating region analysis
22. A fast algorithm for active contours and curvature estimation
23. Computer-aided breast cancer diagnosis
24. Detection of stellate distortions in mammograms
25. Generic neighborhood operators
26. Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms
27. Application of shape analysis to mammographic calcifications
28. Classification of masses on mammograms using a rubber-band straightening transform and feature analysis
29. Characterization of masses on mammograms: Significance of the use of the rubber-band straightening transform
30. Texture features for image classification
31. Texture classification using gray level run lengths
32. Maximum-likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data
33. A new approach for testing the significance for differences between ROC curves measured from correlated data?
34. Computerized analysis of mammographic microcalcifications in morphological and texture feature space
35. Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification
36. Feature selection and classifier performance in computer-aided diagnosis: The effect of finite sample size
37. Artificial neural networks in mammography: Application to decision making in the diagnosis of breast cancer
38. Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging
39. Improving the distinction between benign and malignant breast lesions: The value of sonographic texture analysis
40. Feature selection in the pattern classification problem of digital chest radiograph segmentation
41. Improvement in specificity of ultrasonography for diagnosis of breast tumors by means of artificial intelligence
42. Computerized analysis of mammographic parenchymal patterns for breast cancer risk assessment: Feature selection