Lado M J, Tahoces P G, Méndez A J, Souto M, Vidal J J
Department of Radiology of the University of Santiago de Compostela, Spain.
Med Phys. 1999 Jul;26(7):1294-305. doi: 10.1118/1.598624.
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 (A1) 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 (A1=0.39+/-0.03) for fatty ROIs, and 62.17% and 5.82 false positives per image (A1=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 (A1=0.77+/-0.09) for fatty mammograms, and 72.85% of sensitivity at a false positive detection rate of 2.21 per image (A1=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.
已开发出一种用于检测数字乳腺钼靶片中簇状微钙化的计算机化方案。还对感兴趣区域（ROI）中的单个微钙化进行了检测。这些乳腺钼靶片先前已根据其乳腺组织分为脂肪型和致密型。选择了最合适的小波基和重构水平。为了选择小波基，使用不同类型的小波函数和小波系数的不同组合对40个微钙化轮廓进行了分解和重构。对于脂肪组织，选择了长度为8的symlets小波基。对于致密组织，则采用了具有四元素基的Daubechies小波。评估了两种检测单个微钙化的方法：（a）二维小波变换，和（b）一维小波变换。第二种技术产生了最佳结果，并用于检测完整乳腺钼靶片中的簇状微钙化。当使用二维小波变换检测单个微钙化时，对于脂肪型ROI，在每张图像假阳性率为7.13的情况下，灵敏度为71.11%。对于致密型ROI，灵敏度为60.76%，假阳性率为7.33。AFROC曲线下的面积（A1）分别为0.33±0.04和0.28±0.02。一维小波变换方法对于脂肪型ROI产生了80.44%的灵敏度和每张图像6.43个假阳性（A1 = 0.39±0.03），对于致密型ROI产生了62.17%的灵敏度和每张图像5.82个假阳性（A1 = 0.37±0.02）。对于在整个乳腺钼靶片中检测微钙化簇，对于脂肪型乳腺钼靶片，灵敏度为80.00%，每张图像有0.94个假阳性（A1 = 0.77±0.09），对于致密型乳腺钼靶片，在每张图像假阳性检测率为2.21时，灵敏度为72.85%（A1 = 0.64±0.07）。总体而言，在每张图像假阳性检测率为1.57时，获得了76.43%的灵敏度。
