A complete software application for automatic registration of x‐ray mammography and magnetic resonance images. Issue 8 (30th November 2016)

• Record Type: Journal Article
• Authors: Solves‐Llorens, J. A.; Rupérez, M. J.; Monserrat, C.; Feliu, E.; García, M.; Lloret, M.
• Is Part Of: Medical physics. Volume 41:Issue 8(2014)Part 1
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-11-30
• Subjects: Mammography -- Clinical applications -- Anatomic imaging -- Segmentation -- Registration -- Cancer; affine transforms -- biomechanics -- biomedical MRI -- cancer -- data compression -- image coding -- image registration -- image segmentation -- mammography -- medical image processing -- physiological models -- skin -- tumours; breast imaging registration -- biomechanical modeling -- x‐ray mammography -- MRI; Involving electronic [emr] or nuclear [nmr] magnetic resonance, e.g. magnetic resonance imaging -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Image coding, e.g. from bit‐mapped to non bit‐mapped -- Compression; Expansion; Suppression of unnecessary data, e.g. redundancy reduction; Mammography -- Medical magnetic resonance imaging -- Medical X‐ray imaging -- Medical image segmentation -- Skin -- Radiologists -- Biomechanics -- Tissues; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4885957
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: This work presents a complete and automatic software application to aid radiologists in breast cancer diagnosis. The application is a fully automated method that performs a complete registration of magnetic resonance (MR) images and x‐ray (XR) images in both directions (from MR to XR and from XR to MR) and for both x‐ray mammograms, craniocaudal (CC), and mediolateral oblique (MLO). This new approximation allows radiologists to mark points in the MR images and, without any manual intervention, it provides their corresponding points in both types of XR mammograms and vice versa. Methods: The application automatically segments magnetic resonance images and x‐ray images using the C‐Means method and the Otsu method, respectively. It compresses the magnetic resonance images in both directions, CC and MLO, using a biomechanical model of the breast that distinguishes the specific biomechanical behavior of each one of its three tissues (skin, fat, and glandular tissue) separately. It makes a projection of both compressions and registers them with the original XR images using affine transformations and nonrigid registration methods. Results: The application has been validated by two expert radiologists. This was carried out through a quantitative validation on 14 data sets in which the Euclidean distance between points marked by the radiologists and the corresponding points obtained by the application were measured. The results showed a mean error of 4.2 ± 1.9 mm for the MRI to CC registration, 4.8 ± 1.3 mm for the MRI to MLO registration, and 4.1 ± 1.3 mm for the CC and MLO to MRI registration. Conclusions: A complete software application that automatically registers XR and MR images of the breast has been implemented. The application permits radiologists to estimate the position of a lesion that is suspected of being a tumor in an imaging modality based on its position in another different modality with a clinically acceptable error. The results show that the application can accelerate the mammographic screening process for high risk populations or for dense breasts.