Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms

Table 4

Number of ROIs resulted in FP reduction using curvelet-based LBP (without sparse) & ANN classification at training and validation stage.

Class	Dataset used	Benign/malignant mass			Nonmass/benign mass			Total (#) images	TPR (true-positive rate) = TP/#lesions	FPPI (false-positive per image) = FP/# images
		Previous stage	Selected (TP)	Lost (FN)	Previous stage	Selected (TN)	Lost (FP)
Normal vs abnormal	MIAS	108 ∗ 2 = 216	203	13	273	257	16	315	(203/216) = 0.94	(16/315) = 0.05
	DDSM	140 ∗ 4 = 560	465	95	1203	1095	108	240	(465/560) = 0.83	(108/240) = 0.45
Benign vs malignant	MIAS	49	49	0	59	57	2	108	(49/49) = 1.00	(2/108) = 0.02
	DDSM	46 ∗ 2 = 92	91	1	94	91	3	140	(91/92) = 0.99	(3/140) = 0.02
Normal vs malignant	MIAS	49 ∗ 4 = 196	184	12	273	254	19	256	(184/196) = 0.94	(19/256) = 0.07
	DDSM	46 ∗ 4 = 184	180	4	1203	1143	60	146	(180/184) = 0.98	(60/146) = 0.41
	TMCH: Scanner1	107 ∗ 4 = 428	416	12	605	551	54	217	(416/428) = 0.97	(54/217) = 0.25
	TMCH: Scanner2	65 ∗ 4 = 260	255	5	232	214	18	135	(255/260) = 0.98	(18/135) = 0.13

^∗Augmentation of image.