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

Table 5

Number of ROIs resulted in FP reduction using sparse curvelet coefficient-based LBP & 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	201	15	273	265	8	315	(201/216) = 0.93	(8/315) = 0.02
	DDSM	140 ∗ 4 = 560	516	44	1203	1155	48	240	(516/560) = 0.92	(48/240) = 0.2
Benign vs malignant	MIAS	49	48	1	59	59	1	108	(48/49) = 0.98	(1/108) = 0.01
	DDSM	46 ∗ 2 = 92	89	3	94	89	5	140	(89/92) = 0.97	(5/140) = 0.03
Normal vs malignant	MIAS	49 ∗ 4 = 196	192	4	273	259	14	256	(192/196) = 0.98	(14/256) = 0.05
	DDSM	46 ∗ 4 = 184	182	2	1203	1167	36	146	(182/184) = 0.99	(36/146) = 0.25
	TMCH: Scanner1	107 ∗ 4 = 428	424	4	605	593	12	217	(424/428) = 0.99	(12/217) = 0.05
	TMCH: Scanner2	65 ∗ 4 = 260	260	0	232	232	0	135	(260/260) = 1.00	(0/135) = 0

^∗Augmentation of image.