Temporal Mammograms Research Articles

Neoadjuvant chemotherapy response evaluation in breast cancer based on mammogram registration and tumor segmentation

The standard approach for assessing the response of breast tumors to neoadjuvant chemotherapy is to monitor gross changes in tumor size as measured by physical exam and/or conventional imaging such as mammography. However, the deformable nature of the breast and variation in the imaging procedure, make it difficult to match the shape of breasts between serials of temporal mammograms, particularly when the tumor is shrinking due to the treatment. In this paper we propose a method for assessing residual tumor size following neoadjuvant chemotherapy by analyzing changes between pre-treatment and post-treatment mammograms. Our method consists of three steps: (1) pre-treatment and post-treatment mammograms were first registered in order to circumvent the problem of patient repositioning and breast deformation. (2) Tumors at corresponding locations were segmented using region growing segmentation, (3) and based on changes in tumor sizes the response rate is quantified. The proposed method has been tested on 6 breast cancer patients undergoing neoadjuvant chemotherapy, and the experimental results demonstrate that our approach may improve the ability of mammography to evaluate breast tumor response.

Incorporation of fuzzy spatial relation in temporal mammogram registration

This paper presents a method for incorporating fuzzy sets based spatial relations in registering temporal mammogram pairs. In the proposed method, four spatial relations, to the right of, to the left of, below, and above, are considered. The histogram of all possible angles between all pairs of points in a pair of regions of interest (ROIs) is treated as a fuzzy set and spatial relations between the pair of ROIs are characterized by measuring to what degree this fuzzy set approaches the four spatial relations. Based on the spatial relations, association of ROIs of temporal mammogram pairs is then treated as a graph matching problem and registration of temporal mammogram pairs is realized by finding the common subgraph between two graphs representing a pair of temporal mammograms. 95 pairs of real temporal mammograms are used to test the proposed method. 70.8% of matched ROI pairs are visually identified as “good” matches. When the registration results are incorporated in a cancer detection scheme, the Az score (area under the ROC curve) is improved from 0.846 to 0.852. The results demonstrated that registration of temporal mammograms based on fuzzy spatial relations improves the overall detection efficiency.

Computer aided mass detection in mammography with temporal change analysis

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.

Gray-scale and geometric registration of full-field digital and film-screen mammograms

During the transition from traditional film-screen (FS) mammography to full-field digital (FFD) mammography, images from both modalities are used in hospitals and in mammography screening centers, as comparison of mammograms from subsequent examinations of a client is an important part of the diagnostic procedure. A parametric method is presented to register a FS mammogram and a FFD mammogram of the same woman with respect to geometry and gray-scales. The main motivation for the study is to lessen irrelevant differences between mammograms due to acquisition. First, a technique like this might increase the radiologist's ability to detect relevant differences like abnormal growth in breast tissue that signal breast cancer. Second, applications may be found in subtraction radiology or in computer-aided detection of abnormalities in temporal mammograms. The proposed method is based on a parametric model of the most important aspects of acquisition, which relates the pixel values of two images. This encompasses (1) breast positioning; (2) breast compression; (3) exposure time; (4) incident radiation intensity; and (5a) film properties and digitization for FS mammograms, or (5b) detector response for FFD mammograms. The method does not require a priori knowledge about specific settings of acquisition; the parameters are estimated from the two mammograms themselves.

HYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCER

Biomedical Engineering: Applications, Basis and CommunicationsVol. 19, No. 06, pp. 359-374 (2007) No AccessHYBRID REGISTRATION OF CORRESPONDING MAMMOGRAM IMAGES FOR AUTOMATIC DETECTION OF BREAST CANCERYih-Chih Chiou, Chern-Sheng Lin, and Cheng-Yu LinYih-Chih ChiouDepartment of Mechanical Engineering, Chung Hua University, Hsinchu, Taiwan, R. O. C. Search for more papers by this author , Chern-Sheng LinDepartment of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, R. O. C.Corresponding author: Chern Sheng Lin, Professor, Department of Automatic Control Engineering, Feng Chia University, Taichung, Taiwan, R. O. C. Search for more papers by this author , and Cheng-Yu LinDepartment of Mechanical Engineering, Chung Hua University, Hsinchu, Taiwan, R. O. C. Search for more papers by this author https://doi.org/10.4015/S101623720700046XCited by:2 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail AbstractMammogram registration is a critical step in automatic detection of breast cancer. Much research has been devoted to registering mammograms using either feature-matching or similarity measure. However, a few studies have been done on combining these two methods. In this research, a hybrid mammogram registration method for the early detection of breast cancer is developed by combining feature-based and intensity-based image registration techniques. Besides, internal and external features were used simultaneously during the registration to obtain a global spatial transformation. 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FiguresReferencesRelatedDetailsCited By 2LYMPHATIC INFILTRATION DETECTION IN BREAST CANCER H&E IMAGE PRIOR TO LYMPHADENECTOMYYung-Lung Kuo, Chien-Chuan Ko, and Ming-Ji Lee6 August 2014 | Biomedical Engineering: Applications, Basis and Communications, Vol. 26, No. 04Visual Perception Driven Registration of MammogramsA. Boucher, F. Cloppet, N. Vincent and P. Jouve1 Aug 2010 Recommended Vol. 19, No. 06 Metrics History Accepted 13 November 2007 KeywordsMutual informationMammogram registrationThin-plate splinesFeature matchingPDF download