Paper Info
Title
A heuristic approach to automated nipple detection in digital mammograms.
Abstract
In this paper, a heuristic approach to automated nipple detection in digital mammograms is presented. A multithresholding algorithm is first applied to segment the mammogram and separate the breast region from the background region. Next, the problem is considered separately for craniocaudal (CC) and mediolateral-oblique (MLO) views. In the simplified algorithm, a search is performed on the segmented image along a band around the centroid and in a direction perpendicular to the pectoral muscle edge in the MLO view image. The direction defaults to the horizontal (perpendicular to the thoracic wall) in case of CC view images. The farthest pixel from the base found in this direction can be approximated as the nipple point. Further, an improved version of the simplified algorithm is proposed which can be considered as a subclass of the Branch and Bound algorithms. The mean Euclidean distance between the ground truth and calculated nipple position for 500 mammograms from the Digital Database for Screening Mammography (DDSM) database was found to be 11.03 mm and the average total time taken by the algorithm was 0.79 s. Results of the proposed algorithm demonstrate that even simple heuristics can achieve the desired result in nipple detection thus reducing the time and computational complexity.
Year
DOI
Venue
2013
10.1007/s10278-013-9575-x
J. Digital Imaging
Keywords
Field
DocType
Breast cancer,Nipple detection,Mammography,DDSM database,Multithresholding
Mammography,Computer vision,Branch and bound,Heuristic,Computer science,Euclidean distance,Ground truth,Artificial intelligence,Pixel,Centroid,Computational complexity theory
Journal
Volume
Issue
ISSN
26
5
1618-727X
Citations 
PageRank 
References 
7
0.76
10
Authors
5
Name
Order
Citations
PageRank
Mainak Jas1243.24
Sudipta Mukhopadhyay217926.21
Jayasree Chakraborty37213.01
Anup Sadhu4154.65
Niranjan Khandelwal58210.37