Multiresolution Detection of Spiculated Lesions in Digital Mammograms

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Author

S Liu, C Babbs, E Delp

Tech report number

CERIAS TR 2001-141

Entry type

article

Abstract

In this paper we present a novel multiresolution scheme for the detection of spiculated lesions in digital mammograms. First, a multiresolution representation of the original mammogram is obtained using a linear phase nonseparable 2-D wavelet transform. A set of features is then extracted at each resolution in the wavelet pyramid for every pixel. This approach addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear in different sizes. Detection is performed from the coarsest resolution to the finest resolution using a binary tree classifier. This top-down approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. Experimental results using the MIAS image database have shown that this algorithm is capable of detecting spiculated lesions of very different sizes at low false positive rates.

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Date

2001 – 06

URL

ftp://skynet.ecn.purdue.edu/pu ... ammo-spiculated/paper.pdf

Journal

IEEE Transactions on Image Processing

Key alpha

Delp

Number

Pages

874-884

Volume

Publication Date

2001-06-00

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@Article{ Delp,
	title = "Multiresolution Detection of Spiculated Lesions in Digital Mammograms",
	author = "S Liu, C Babbs, E Delp",
	year = "2001",
	month = "06",
	journal = "IEEE Transactions on Image Processing",
	number = "6",
	pages = "874-884",
	volume = "10",
	abstract = "In this paper we present a novel multiresolution scheme for the detection of spiculated lesions in digital mammograms. First, a multiresolution representation of the original mammogram is obtained using a linear phase nonseparable 2-D wavelet transform. A set of features is then extracted at each resolution in the wavelet pyramid for every pixel. This 
approach addresses the difficulty of predetermining the neighborhood size for feature extraction to characterize objects that may appear in different sizes. Detection is performed from 
the coarsest resolution to the finest resolution using a binary tree classifier. This top-down 
approach requires less computation by starting with the least amount of data and propagating detection results to finer resolutions. Experimental results using the MIAS image 
database have shown that this algorithm is capable of detecting spiculated lesions of very different sizes at low false positive rates. ",
	url = "ftp://skynet.ecn.purdue.edu/pub/dist/delp/mammo-spiculated/paper.pdf",
}