Computer-aided detection and classification of microcalcifications in mammograms: a survey.

DOI10.1016/S0031-3203(03)00192-4zbMath1058.68621MaRDI QIDQ1425991

D. Massart

Publication date: 14 March 2004

Published in: Pattern Recognition (Search for Journal in Brave)

zbMATH Keywords

SegmentationMammographyContrast enhancementMicrocalcificationsComputer-aided detectionFalse negative (FN)False positive (FP)Features extractionFree-response receiver operating characteristic (FROC)Microcalcification clusters (MCCs) detection and classificationReceiver operating characteristic (ROC)

Mathematics Subject Classification ID

Biomedical imaging and signal processing (92C55) Pattern recognition, speech recognition (68T10)

Related Items (9)

DENOISING AND ENHANCEMENT OF MAMMOGRAPHIC IMAGES UNDER THE ASSUMPTION OF HETEROSCEDASTIC ADDITIVE NOISE BY AN OPTIMAL SUBBAND THRESHOLDING ⋮ Dual energy method for breast imaging: a simulation study ⋮ Breast microcalcification diagnosis using deep convolutional neural network from digital mammograms ⋮ A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs ⋮ Enhancement and diagnosis of breast cancer in mammography images using histogram equalization and genetic algorithm ⋮ Microcalcification detection using fuzzy logic and scale space approaches. ⋮ BREAST ULTRASOUND IMAGE SEGMENTATION BASED ON PARTICLE SWARM OPTIMIZATION AND THE CHARACTERISTICS OF BREAST TISSUE ⋮ A technical review of convolutional neural network-based mammographic breast cancer diagnosis ⋮ Automated breast cancer detection and classification using ultrasound images: a survey

Cites Work

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• Fast fuzzy clustering
• Fractal functions and wavelet expansions based on several scaling functions
• Segmentation of bright targets using wavelets and adaptive thresholding
• Feature-based fuzzy classification for interpretation of mammograms

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