A wavelet-based algorithm for detecting clustered microcalcifications in digital mammograms
| Autor: | Juan J. Vidal, María J. Lado, Pablo G. Tahoces, Miguel Souto, Arturo J. Méndez |
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| Rok vydání: | 1999 |
| Předmět: |
Databases
Factual Biopsy Image processing Breast Diseases Wavelet Image Processing Computer-Assisted False positive paradox medicine Humans Mammography Breast Mathematics medicine.diagnostic_test business.industry Calcinosis Wavelet transform Pattern recognition General Medicine Image segmentation nervous system diseases Computer-aided diagnosis Female Artificial intelligence False positive rate business Algorithms |
| Zdroj: | Medical Physics. 26:1294-1305 |
| ISSN: | 0094-2405 |
| DOI: | 10.1118/1.598624 |
| Popis: | A computerized scheme to detect clustered microcalcifications in digital mammograms has been developed. Detection of individual microcalcifications in regions of interest (ROIs) was also performed. The mammograms were previously classified into fatty and dense, according to their breast tissue. The most appropriate wavelet basis and reconstruction levels were selected. To select the wavelet basis, 40 profiles of microcalcifications were decomposed and reconstructed using different types of wavelet functions and different combinations of wavelet coefficients. The symlets with a basis of length 8 were chosen for fatty tissue. For dense tissue, the Daubechies’ wavelets with a four-element basis were employed. Two methods to detect individual microcalcifications were evaluated: (a) two-dimensional wavelet transform, and (b) one-dimensional wavelet transform. The second technique yielded the best results, and was used to detect clustered microcalcifications in the complete mammogram. When detecting individual microcalcifications by using two-dimensional wavelet transform we have obtained, for fatty ROIs, a sensitivity of 71.11% at a false positive rate of 7.13 per image. For dense ROIs the sensitivity was 60.76% and the false positive rate, 7.33. The areas (A 1 ) under the AFROC curves were 0.33±0.04 and 0.28±0.02, respectively. The one-dimensional wavelet transform method yielded 80.44% of sensitivity and 6.43 false positives per image (A 1 =0.39±0.03) for fatty ROIs, and 62.17% and 5.82 false positives per image (A 1 =0.37±0.02) for dense ROIs. For the detection of clusters of microcalcifications in the entire mammogram, the sensitivity was 80.00% with 0.94 false positives per image (A 1 =0.77±0.09) for fatty mammograms, and 72.85% of sensitivity at a false positive detection rate of 2.21 per image (A 1 =0.64±0.07) for dense mammograms. Globally, a sensitivity of 76.43% at a false positive detection rate of 1.57 per image was obtained. |
| Databáze: | OpenAIRE |
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