Incorporation of fuzzy spatial relation in temporal mammogram registration

Ma, Fei; Yu, Limin; Bajger, Mariusz; Bottema, Murk J.

doi:10.1016/j.fss.2015.03.021

Cited by 7 publications

(3 citation statements)

References 26 publications

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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 [16], namely that of registering temporal mammograms based on fuzzy spatial relation representation and graph matching. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

Computer aided mass detection in mammography with temporal change analysis

Liu

et al. 2015

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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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“…Sanjay-Gopal et al, Hadjiiski et al, and Filev et al designed computerized methods for interval change analysis, using a regional registration technique to identify corresponding lesions on temporal pairs of mammograms [19,39,40]. In a relatively recent study, Ma et al introduced a method that incorporates fuzzy sets, based on spatial relationships, along with graph matching [41]. Hybrid registration techniques for mammogram matching have also been proposed by Wirth et al, Timp and Karssemeijer, and Li et al [42][43][44].…”

Section: Registrationmentioning

confidence: 99%

A Review of Computer-Aided Breast Cancer Diagnosis Using Sequential Mammograms

et al. 2022

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Radiologists assess the results of mammography, the key screening tool for the detection of breast cancer, to determine the presence of malignancy. They, routinely, compare recent and prior mammographic views to identify changes between the screenings. In case a new lesion appears in a mammogram, or a region is changing rapidly, it is more likely to be suspicious, compared to a lesion that remains unchanged and it is usually benign. However, visual evaluation of mammograms is challenging even for expert radiologists. For this reason, various Computer-Aided Diagnosis (CAD) algorithms are being developed to assist in the diagnosis of abnormal breast findings using mammograms. Most of the current CAD systems do so using only the most recent mammogram. This paper provides a review of the development of methods to emulate the radiological approach and perform automatic segmentation and/or classification of breast abnormalities using sequential mammogram pairs. It begins with demonstrating the importance of utilizing prior views in mammography, through the review of studies where the performance of expert and less-trained radiologists was compared. Following, image registration techniques and their application to mammography are presented. Subsequently, studies that implemented temporal analysis or subtraction of temporally sequential mammograms are summarized. Finally, a description of the open access mammography datasets is provided. This comprehensive review can serve as a thorough introduction to the use of prior information in breast cancer CAD systems but also provides indicative directions to guide future applications.

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“…Many learning-based methods have been proposed for a similar problem, graph matching, such as Zanfir and Sminchisescu [20] , Wang et al [21] , Ma et al [22] , Wu et al [23] , Xu et al [24] , Guo et al [25] , Vento [26] . However, very few learning-based methods have been designed for subgraph matching, except for Sub-GMN [27] and NeuralMatch [28] to our best knowledge.…”

Section: Introductionmentioning

confidence: 99%