[引用][C] Automatic image feature extraction for diagnosis and prognosis of breast cancer
MJ Bottema, GN Lee, S Lu - Artificial Intelligence Techniques In …, 2000 - World Scientific
MJ Bottema, GN Lee, S Lu
Artificial Intelligence Techniques In Breast Cancer Diagnosis And Prognosis, 2000•World ScientificThe following sections are included: Introduction Image Modalities Mammography
Screening Mammography Clinical Mammography Detection and Classification of Masses
Detection of Stellate Lesions Detection and Classification of Microcalcifications Ultrasound
Fine Needle Aspiration Slides Image Features and Analysis Geometric Features The
Boundary Area, Volume, Radius, Height, Contrast The Curse of Perimeter2/4π Area Shape
Texture Features Run-Length Matrices Co-occurrence Matrices Markovian Statistics …
Screening Mammography Clinical Mammography Detection and Classification of Masses
Detection of Stellate Lesions Detection and Classification of Microcalcifications Ultrasound
Fine Needle Aspiration Slides Image Features and Analysis Geometric Features The
Boundary Area, Volume, Radius, Height, Contrast The Curse of Perimeter2/4π Area Shape
Texture Features Run-Length Matrices Co-occurrence Matrices Markovian Statistics …
Abstract
The following sections are included:
- Introduction
- Image Modalities
- Mammography
- Screening Mammography
- Clinical Mammography
- Detection and Classification of Masses
- Detection of Stellate Lesions
- Detection and Classification of Microcalcifications
- Ultrasound
- Fine Needle Aspiration Slides
- Image Features and Analysis
- Geometric Features
- The Boundary
- Area, Volume, Radius, Height, Contrast
- The Curse of Perimeter2/4π Area
- Shape
- Texture Features
- Run-Length Matrices
- Co-occurrence Matrices
- Markovian Statistics
- Example of Texture Arrays
- Fractal Dimension
- Morphology
- Snakes
- Wavelets
- Summary
- Acknowledgments
- References
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