Improved Detection of Cancer in Screening Mammograms by Temporal Comparison

Ma, Fei; Bajger, Mariusz; Williams, Simon A.; Bottema, Murk J.

doi:10.1007/978-3-642-13666-5_101

Cited by 6 publications

(7 citation statements)

References 13 publications

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“…These feature were chosen for their ability to distinguish between the types of objects (masses, cysts, ducts, etc.) typically found in mammograms [23,24]. The threshold was set sufficiently low to only filter out obvious non-mass pieces, and hence many non-malignant anomalies were still included.…”

Section: Feature Descriptionmentioning

confidence: 99%

Incorporation of fuzzy spatial relation in temporal mammogram registration

Bajger

et al. 2015

Fuzzy Sets and Systems

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Section: Feature Descriptionmentioning

confidence: 99%

Incorporation of fuzzy spatial relation in temporal mammogram registration

Bajger

et al. 2015

Fuzzy Sets and Systems

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“…This paper presents a method of extracting temporal change information from temporal mammograms and apply the change information to the detection of malignant masses. This study is an extension of the framework introduced in [9], namely that of registering temporal mammograms based on spatial relation representation and graph matching. This paper is organized as follows.…”

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

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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 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.

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“…Usually, new abnormalities, or Regions of Interest (ROIs), changing rapidly between screenings, are more likely to be suspicious. On the contrary, ROIs that remain unchanged, are more likely benign and harmless [12] .…”

Section: Introductionmentioning

confidence: 99%

Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms

Loizidou

Skouroumouni

Nikolaou

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Cancer remains a major cause of morbidity and mortality globally, with 1 in 5 of all new cancers arising in the breast. The introduction of mammography for the radiological diagnosis of breast abnormalities, significantly decreased their mortality rates. Accurate detection and classification of breast masses in mammograms is especially challenging for various reasons, including low contrast and the normal variations of breast tissue density. Various Computer-Aided Diagnosis (CAD) systems are being developed to assist radiologists with the accurate classification of breast abnormalities. Methods: In this study, subtraction of temporally sequential digital mammograms and machine learning are proposed for the automatic segmentation and classification of masses. The performance of the algorithm was evaluated on a dataset created especially for the purposes of this study, with 320 images from 80 patients (two time points and two views of each breast) with precisely annotated mass locations by two radiologists. Results: Ninety-six features were extracted and ten classifiers were tested in a leave-one-patient-out and k-fold cross-validation process. Using Neural Networks, the detection of masses was 99.9% accurate. The classification accuracy of the masses as benign or suspicious increased from 92.6%, using the state-of-the-art temporal analysis, to 98%, using the proposed methodology. The improvement was statistically significant (p-value < 0.05). Conclusion: These results demonstrate the effectiveness of the subtraction of temporally consecutive mammograms for the diagnosis of breast masses. Clinical and Translational Impact Statement:The proposed algorithm has the potential to substantially contribute to the development of automated breast cancer Computer-Aided Diagnosis systems with significant impact on patient prognosis.

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Improved Detection of Cancer in Screening Mammograms by Temporal Comparison

Cited by 6 publications

References 13 publications

Incorporation of fuzzy spatial relation in temporal mammogram registration

Incorporation of fuzzy spatial relation in temporal mammogram registration

Temporal change analysis for computer aided mass detection in mammography

Automatic Breast Mass Segmentation and Classification Using Subtraction of Temporally Sequential Digital Mammograms

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