A Tool for Temporal Comparison of Mammograms: Image Toggling and Dense-Tissue-Preserving Registration

Hasegawa, Akira; Neemuchwala, Huzefa; Tsunoda-Shimizu, Hiroko; Honda, Shinya; Shimura, Kazuo; Sato, Minoru; Koyama, Tomomi; Kikuchi, Mari; Hiramatsu, Sonoe

doi:10.1007/978-3-540-70538-3_62

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…One approach is to simulate rigid or affine deformations between breasts in temporal mammograms. Hasegawa et al used deformation of a B-spline control point grid to register dense regions of mammogram after a rigid body alignment [10]. Richard and Cohen used a variational formulation to simulate smooth deformation between mammogram pairs and applied the method to bilateral pairs Richard280 [18].…”

Section: Introductionmentioning

confidence: 99%

Computer aided mass detection in mammography with temporal change analysis

Liu

et al. 2015

ComSIS

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This paper presents a method to extract change information from temporal mammogram pairs and to incorporate the temporal change information in the malignant mass classification. In this method, a temporal mammogram registration framework which is based on spatial relations between regions of interest and graph matching was used to create correspondences between regions of current mammogram and regions of previous mammogram. 18 image features were then used to capture the differences (temporal changes) between the matched regions. To assess the contribution of temporal change information to the mass detection, 5 methods were designed to combine mass classification on image features measured on single regions and mass classification on temporal features to improve overall mass classification. The method was tested on 95 pairs of temporal mammograms using k-fold cross validation procedure. The experimental results showed that, when combining two classification results using linear combination or by taking minimum value, the Az score of overall classification performance increased from 0.8843 to 0.8989 and 0.8863 respectively. The results demonstrated that registering temporal mammograms, measuring temporal changes from matched regions and incorporating the change information in the mass classification improves the overall mass detection.

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Section: Introductionmentioning

confidence: 99%

Computer aided mass detection in mammography with temporal change analysis

Liu

et al. 2015

ComSIS

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“…Another possible digital tool to compare serial images is image toggling [4]. In other words, the same view of the same breast from a prior study can be displayed at the same position on the monitor, replacing the current image and allowing the reader to detect the differences in the serial studies.…”

Section: Introductionmentioning

confidence: 99%

A novel image toggle tool for comparison of serial mammograms: automatic density normalization and alignment—development of the tool and initial experience

et al. 2014

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“…One approach is to simulate rigid or affine deformations between breasts in temporal mammograms. Hasegawa et al used deformation of a B-spline control point grid to register dense regions of mammogram after a rigid body alignment [2]. Richard and Cohen used a variational formulation to simulate smooth deformation between mammogram pairs and applied the method to bilateral pairs Richard280 [3].…”

Section: Introductionmentioning

confidence: 99%

Temporal change analysis for computer aided mass detection in mammography

Liu

et al. 2014

2014 7th International Conference on Biomedical Engineering and Informatics

View full text Add to dashboard Cite

This paper presents a method to extract change information from temporal mammogram pairs and to incorporate the temporal change information in the malignant mass classification. In this method, a temporal mammogram registration framework which is based on spatial relations between regions of interest and graph matching was used to create correspondences between regions of current mammogram and regions of previous mammogram. 18 image features were then used to capture the differences (temporal changes) between the matched regions. To assess the contribution of temporl change information to the mass detection, 4 methods were designed to combine mass classification on image features measured on single regions and mass classification on temporal features to improve overall mass classification. The method was tested on 95 pairs of temporal mammograms using k-fold cross validation procedure. The experimental results showed that, when combining two classification results using linear combination or by taking minimum value, the A z score of overall classification performance increased from 0.8843 to 0.8958 and 0.8962 respectively. The results demonstrated that registering temporal mammograms, measuring temporal changes from matched regions and incorporating the change information in the mass classification improves the overall mass detection.

show abstract

A Tool for Temporal Comparison of Mammograms: Image Toggling and Dense-Tissue-Preserving Registration

Cited by 6 publications

References 2 publications

Computer aided mass detection in mammography with temporal change analysis

Computer aided mass detection in mammography with temporal change analysis

A novel image toggle tool for comparison of serial mammograms: automatic density normalization and alignment—development of the tool and initial experience

Temporal change analysis for computer aided mass detection in mammography

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