&lt;title&gt;Regional mammogram registration technique for automated analysis of interval changes of breast lesions&lt;/title&gt;

Sanjay-Gopal, S.; Chan, Heang-Ping; Petrick, Nicholas; Wilson, Todd E.; Sahiner, Berkman; Helvie, Mark A.; Goodsitt, Mitchell M.

doi:10.1117/12.310825

Cited by 7 publications

(2 citation statements)

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“…However, like the subtraction of chest images, temporal image subtraction of mammograms is a difficult task due to tissue changes over time, inconsistencies in breast compression, skewing of the three-dimensional breast relative to the image projection during compression and lack of easy landmarks on the breast that can be used to facilitate optimal image registration. Previous attempts to develop acceptable subtraction methods for mammograms, including methods for both bilateral subtraction [9][10][11][12] and temporal subtraction [14][15][16], have had only partial success. As a result, the temporal subtraction of mammograms has not been employed clinically.…”

Section: Introductionmentioning

confidence: 99%

Automated Analysis and Display of Temporal Sequences of Mammograms

Good¹

2006

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Section: Introductionmentioning

confidence: 99%

Automated Analysis and Display of Temporal Sequences of Mammograms

Good¹

2006

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“…Gopal et al [8][9][10] and Hadjiiski et al 11 have developed a multistage technique that defines the transformation to locally map the position of the mass on a current mammogram to that of the prior mammogram. A local search for the mass is then performed on the prior mammogram.…”

Section: Introductionmentioning

confidence: 99%

Automated registration of breast lesions in temporal pairs of mammograms for interval change analysis—local affine transformation for improved localization

Hadjiiski¹,

Chan²,

Sahiner³

et al. 2001

Medical Physics

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Analysis of interval change is important for mammographic interpretation. The aim of this study is to evaluate the use of an automated registration technique for computer-aided interval change analysis in mammography. Previously we developed a regional registration technique for identifying masses on temporal pairs of mammograms. In the current study, we improved lesion registration by including a local alignment step. Initially, the lesion position on the prior mammogram was estimated based on the breast geometry. An initial fan-shaped search region was then defined on the prior mammogram. In the second stage, the location of the fan-shaped region on the prior mammogram was refined by warping, based on an affine transformation and simplex optimization in a local region. In the third stage, a search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within the search region. This technique was evaluated on 124 temporal pairs of mammograms containing biopsyproven masses. Eighty-seven percent of the estimated lesion locations resulted in an area overlap of at least 50% with the true lesion locations and an average distance of 2.4 +/- 2.1 mm between their centroids. The average distance between the estimated and the true centroid of the lesions on the prior mammogram over all 124 temporal pairs was 4.2 +/- 5.7 mm. The registration accuracy was improved in comparison with our previous study that used a data set of 74 temporal pairs of mammograms. This improvement in accuracy resulted from the improved geometry estimation and the local affine transformation.

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