Study of breast implants mammography examinations for identification of suitable image quality criteria

Purpose: To characterise the mammography technique used in breast cancer screening programmes for breast implants (BI) and to identify if the image quality (IQ) criteria available in literature are applicable to BI imaging. Methods: The study was conducted in two phases: literature review to find IQ criteria used in mammography combining keywords in several sources; and assessment of 1207 BI mammograms using the criteria that was identified previously to see if they were achieved or not. An observation grid was used to collect information about positioning, beam energy, compression force, and exposure mode. Descriptive statistics and Student’s t test and χ2 test were performed according to the nature of the variables. Results: Forty-seven out of 2188 documents were included in the analysis, with 13 items identified to assess the quality of positioning, 4 for sharpness, 3 for artefacts, and 2 for exposure parameters. After applying the criteria to BI mammograms, retroglandular fat was not included in 37.3% of the images. The “Pectoral-Nipple-Line” criterion was achieved in 35% of MLO/ML images. The placement of the implant (subpectoral/subglandular) or performing the Eklund had significant influence on the visible anatomy (p = < 0.005), alongside whether the breast was aligned to the detector’s centre. Conclusions: Some of the criteria used to assess standard mammograms were not applicable to BI due to implant overlap. The alignment of the image with the detector’s centre seems to have an impact on the amount of visible tissue. Further studies are necessary to define the appropriate protocol, technique, and suitable quality criteria to assess BI mammograms.publishersversionpublishe

INbreast: Toward a Full-field Digital Mammographic Database

Rationale and Objectives Computer-aided detection and diagnosis (CAD) systems have been developed in the past two decades to assist radiologists in the detection and diagnosis of lesions seen on breast imaging exams, thus providing a second opinion. Mammographic databases play an important role in the development of algorithms aiming at the detection and diagnosis of mammary lesions. However, available databases often do not take into consideration all the requirements needed for research and study purposes. This article aims to present and detail a new mammographic database. Materials and Methods Images were acquired at a breast center located in a university hospital (Centro Hospitalar de S. João [CHSJ], Breast Centre, Porto) with the permission of the Portuguese National Committee of Data Protection and Hospital's Ethics Committee. MammoNovation Siemens full-field digital mammography, with a solid-state detector of amorphous selenium was used. Results The new database—INbreast—has a total of 115 cases (410 images) from which 90 cases are from women with both breasts affected (four images per case) and 25 cases are from mastectomy patients (two images per case). Several types of lesions (masses, calcifications, asymmetries, and distortions) were included. Accurate contours made by specialists are also provided in XML format. Conclusion The strengths of the actually presented database—INbreast—relies on the fact that it was built with full-field digital mammograms (in opposition to digitized mammograms), it presents a wide variability of cases, and is made publicly available together with precise annotations. We believe that this database can be a reference for future works centered or related to breast cancer imaging

Comparison between two packages for pectoral muscle removal on mammographic images

Background: Pectoral muscle removal is a fundamental preliminary step in computer-aided diagnosis systems for full-field digital mammography (FFDM). Currently, two open-source publicly available packages (LIBRA and OpenBreast) provide algorithms for pectoral muscle removal within Matlab environment. Purpose: To compare performance of the two packages on a single database of FFDM images. Methods: Only mediolateral oblique (MLO) FFDM was considered because of large presence of pectoral muscle on this type of projection. For obtaining ground truth, pectoral muscle has been manually segmented by two radiologists in consensus. Both LIBRA’s and OpenBreast’s removal performance with respect to ground truth were compared using Dice similarity coefficient and Cohen-kappa reliability coefficient; Wilcoxon signed-rank test has been used for assessing differences in performances; Kruskal–Wallis test has been used to verify possible dependence of the performance from the breast density or image laterality. Results: FFDMs from 168 consecutive women at our institution have been included in the study. Both LIBRA’s Dice-index and Cohen-kappa were significantly higher than OpenBreast (Wilcoxon signed-rank test P &lt; 0.05). No dependence on breast density or laterality has been found (Kruskal–Wallis test P &gt; 0.05). Conclusion: Libra has a better performance than OpenBreast in pectoral muscle delineation so that, although our study has not a direct clinical application, these results are useful in the choice of packages for the development of complex systems for computer-aided breast evaluation

Wavelet-Based Automatic Breast Segmentation for Mammograms

As part of a first of its kind analysis of longitudinal mammograms, there are thousands of mammograms that need to be analyzed computationally. As a pre- processing step, each mammogram needs to be converted into a binary (black or white) spatial representation in order to delineate breast tissue from the pectoral muscle and image background, which is called a mammographic mask. The current methodology for completing this task is for a lab member to manually trace the outline of the breast, which takes approximately three minutes per mammogram. Thus, reducing the time cost and human subjectivity when completing this task for all mammograms in a large dataset is extremely valuable. In this thesis, an automated breast segmentation algorithm was adapted from a multi-scale gradient-based edge detection approach called the 2D Wavelet Transform Modulus Maxima (WTMM) segmentation method. This automated masking algorithm incorporates the first-derivative Gaussian Wavelet Transform to identify potential edge detection contour lines called maxima chains. The candidate chains are then transformed into a binary mask, which is then compared with the original manual delineation through the use of the Sorenson-Dice Coefficient (DSC). The analysis of 556 grayscale mammograms with this developed methodology produced a median DSC of 0.988 and 0.973 for craniocaudal (CC) and mediolateral oblique (MLO) grayscale mammograms respectively. Based on these median DSCs, in which a perfect overlap score is 1, it can be concluded a wavelet-based automatic breast segmentation algorithm is able to quickly segment the pectoral muscle and produce accurate binary spatial representations of breast tissue in grayscale mammograms

Bruk av kunstig intelligens i evaluering av posisjoneringsavhengig bildekvalitet innen mammografi

Masteroppgave for radiograf/bioingeniørRABD395MAMD-HELS

Measurement of Tumor Extent and Effects of Breast Compression in Digital Mammography and Breast Tomosynthesis

Breast cancer is the most common form of cancer affecting women in the western countries. Today x-ray digital mammography (DM) of the breast is commonly used for early detection of breast cancer. However, the sensitivity of mammography is limited, mainly due to the fact that a 3D volume is projected down to a 2D image. This problem can be partially solved by a tomographic technique. Breast tomosynthesis (BT) reduces the detrimental effect of the projected anatomy. Tumor size is an important predictor of prognosis and treatment effect. We hypothesized that the tumor outline would be better defined in BT and therefore tumor measurement in BT would be more accurate compared with DM. The results showed that breast tumor size measured on BT correlated better with the size measured by the pathologists on the surgical specimens compared with measurement on DM. Breast compression is important in mammography both to improve image quality and to reduce the radiation dose to the breast, but it also has a negative consequence as some women refrain from mammography due to the pain associated with the examination. Since BT is a 3D technique, it was hypothesized that less breast compression force can be applied. The results indicated that less compression force is possible without significantly compromising the diagnostic quality of the image and that the patient comfort was improved. An applied breast compression force as used in mammography results in a pressure distribution over the breast. The pressure distribution was assessed using thin pressure sensors attached to the compression plate. The results showed that the pressure distribution was heterogeneous in appearance and varied widely between different breasts. In almost half of the subjects most of the pressure was over the juxtathoracic part of the breast and the pectoral muscle with little or no pressure over the rest of the breast. Another concern regarding breast compression is the question whether the resulting pressure might damage tumors, causing a shedding of malignant cells into the blood system. Peripheral venous blood samples were drawn before and after breast compression and analyzed for circulating tumor cells. The study found no elevated number of circulating cancer cells in peripheral blood after breast compression. Future analysis of samples from veins draining the breast are needed to study if circulating tumor cells are being trapped in the lung capillaries

Breast Mass Characterization Using 3‐Dimensional Automated Ultrasound as an Adjunct to Digital Breast Tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135628/1/jum201332193.pd

Automated segmentation of radiodense tissue in digitized mammograms using a constrained Neyman-Pearson classifier

Breast cancer is the second leading cause of cancer related mortality among American women. Mammography screening has emerged as a reliable non-invasive technique for early detection of breast cancer. The radiographic appearance of the female breast consists of radiolucent (dark) regions and radiodense (light) regions due to connective and epithelial tissue. It has been established that the percentage of radiodense tissue in a patient\u27s breast can be used as a marker for predicting breast cancer risk. This thesis presents the design, development and validation of a novel automated algorithm for estimating the percentage of radiodense tissue in a digitized mammogram. The technique involves determining a dynamic threshold for segmenting radiodense indications in mammograms. Both the mammographic image and the threshold are modeled as Gaussian random variables and a constrained Neyman-Pearson criteria has been developed for segmenting radiodense tissue. Promising results have been obtained using the proposed technique. Mammograms have been obtained from an existing cohort of women enrolled in the Family Risk Analysis Program at Fox Chase Cancer Center (FCCC). The proposed technique has been validated using a set of ten images with percentages of radiodense tissue, estimated by a trained radiologist using previously established methods. This work is intended to support a concurrent study at the FCCC exploring the association between dietary patterns and breast cancer risk

Spatially varying threshold models for the automated segmentation of radiodense tissue in digitized mammograms

The percentage of radiodense (bright) tissue in a mammogram has been correlated to an increased risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially varying Neyman-Pearson threshold is used to calculate the percentage of radiodense tissue and compensate for the effects of tissue compression that occurs during a mammography procedure. Results demonstrating the efficacy of the technique are demonstrated by exercising the algorithm on two separate sets of mammograms - one obtained from Brigham Women\u27s Hospital, Harvard Medical School and the other set obtained from Fox Chase Cancer Center and digitized at Rowan University. The results of the algorithm compare favorably with a previously established manual segmentation technique

Mammography image quality: pectoral major muscle presentation in the mediolateral oblique view

ABSTRACT: The mammography technique is, currently, the most reliable imaging method for the diagnoses of breast cancer, and for this reason it is essential the production of mammograms with high quality and consistency. Considering that the goal of mammography is to maximize the visualization of breast tissue, the aim of this work is to explore the presentation of the pectoral major muscle in the mammographic image.  We intended to assess the relationship between the technical procedures of the image acquisition and the presentation of the muscle, according to the current mammography quality criteria. The mammograms were collected from two reference hospitals in Porto, and thereafter, the image processing and statistics analysis was performed to assess qualitative and quantitative indicators of the image quality. In general, analyzing both clinical institution and based on these indicators, the hospital that uses a fixed angulation of the potter-bucky apart the patient's body habitus presented better results. This indicates that the existence of failures due to positioning errors is smaller when compared to the quality standard assessed. It was found, therefore, that the angulation of the potter-bucky device has a relevant and statistical significance on the quality of the mammographic images produced