Mark D. Halling-Brown

The Effect of Image Processing on the Detection of Cancers in Digital Mammography

OBJECTIVE. The objective of our study was to investigate the effect of image processing on the detection of cancers in digital mammography images. MATERIALS AND METHODS. Two hundred seventy pairs of breast images (both breasts, one view) were collected from eight systems using Hologic amorphous selenium detectors: 80 image pairs showed breasts containing subtle malignant masses; 30 image pairs, biopsy-proven benign lesions; 80 image pairs, simulated calcification clusters; and 80 image pairs, no cancer (normal). The 270 image pairs were processed with three types of image processing: standard (full enhancement), low contrast (intermediate enhancement), and pseudo-film-screen (no enhancement). Seven experienced observers inspected the images, locating and rating regions they suspected to be cancer for likelihood of malignancy. The results were analyzed using a jackknife-alternative free-response receiver operating characteristic (JAFROC) analysis. RESULTS. The detection of calcification clusters was significantly affected by the type of image processing: The JAFROC figure of merit (FOM) decreased from 0.65 with standard image processing to 0.63 with low-contrast image processing (p = 0.04) and from 0.65 with standard image processing to 0.61 with film-screen image processing (p = 0.0005). The detection of noncalcification cancers was not significantly different among the image-processing types investigated (p > 0.40). CONCLUSION. These results suggest that image processing has a significant impact on the detection of calcification clusters in digital mammography. For the three image-processing versions and the system investigated, standard image processing was optimal for the detection of calcification clusters. The effect on cancer detection should be considered when selecting the type of image processing in the future.

The relationship between cancer detection in mammography and image quality measurements

PURPOSEnTo investigate the relationship between image quality measurements and the clinical performance of digital mammographic systems.nnnMETHODSnMammograms containing subtle malignant non-calcification lesions and simulated malignant calcification clusters were adapted to appear as if acquired by four types of detector. Observers searched for suspicious lesions and gave these a malignancy score. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). Images of a CDMAM contrast-detail phantom were adapted to appear as if acquired using the same four detectors as the clinical images. The resultant threshold gold thicknesses were compared to the FoMs using a linear regression model and an F-test was used to find if the gradient of the relationship was significantly non-zero.nnnRESULTSnThe detectors with the best image quality measurement also had the highest FoM values. The gradient of the inverse relationship between FoMs and threshold gold thickness for the 0.25mm diameter disk was significantly different from zero for calcification clusters (p=0.027), but not for non-calcification lesions (p=0.11). Systems performing just above the minimum image quality level set in the European Guidelines for Quality Assurance in Breast Cancer Screening and Diagnosis resulted in reduced cancer detection rates compared to systems performing at the achievable level.nnnCONCLUSIONSnThe clinical effectiveness of mammography for the task of detecting calcification clusters was found to be linked to image quality assessment using the CDMAM phantom. The European Guidelines should be reviewed as the current minimum image quality standards may be too low.

Breast cancer detection rates using four different types of mammography detectors

ObjectiveTo compare the performance of different types of detectors in breast cancer detection.MethodsA mammography image set containing subtle malignant non-calcification lesions, biopsy-proven benign lesions, simulated malignant calcification clusters and normals was acquired using amorphous-selenium (a-Se) detectors. The images were adapted to simulate four types of detectors at the same radiation dose: digital radiography (DR) detectors with a-Se and caesium iodide (CsI) convertors, and computed radiography (CR) detectors with a powder phosphor (PIP) and a needle phosphor (NIP). Seven observers marked suspicious and benign lesions. Analysis was undertaken using jackknife alternative free-response receiver operating characteristics weighted figure of merit (FoM). The cancer detection fraction (CDF) was estimated for a representative image set from screening.ResultsNo significant differences in the FoMs between the DR detectors were measured. For calcification clusters and non-calcification lesions, both CR detectors’ FoMs were significantly lower than for DR detectors. The calcification cluster’s FoM for CR NIP was significantly better than for CR PIP. The estimated CDFs with CR PIP and CR NIP detectors were up to 15xa0% and 22xa0% lower, respectively, than for DR detectors.ConclusionCancer detection is affected by detector type, and the use of CR in mammography should be reconsidered.Key Points• The type of mammography detector can affect the cancer detection rates.• CR detectors performed worse than DR detectors in mammography.• Needle phosphor CR performed better than powder phosphor CR.• Calcification clusters detection is more sensitive to detector type than other cancers.

MedXViewer: an extensible web-enabled software package for medical imaging

MedXViewer (Medical eXtensible Viewer) is an application designed to allow workstation-independent, PACS-less viewing and interaction with anonymised medical images (e.g. observer studies). The application was initially implemented for use in digital mammography and tomosynthesis but the flexible software design allows it to be easily extended to other imaging modalities. Regions of interest can be identified by a user and any associated information about a mark, an image or a study can be added. The questions and settings can be easily configured depending on the need of the research allowing both ROC and FROC studies to be performed. The extensible nature of the design allows for other functionality and hanging protocols to be available for each study. Panning, windowing, zooming and moving through slices are all available while modality-specific features can be easily enabled e.g. quadrant zooming in mammographic studies. MedXViewer can integrate with a web-based image database allowing results and images to be stored centrally. The software and images can be downloaded remotely from this centralised data-store. Alternatively, the software can run without a network connection where the images and results can be encrypted and stored locally on a machine or external drive. Due to the advanced workstation-style functionality, the simple deployment on heterogeneous systems over the internet without a requirement for administrative access and the ability to utilise a centralised database, MedXViewer has been used for running remote paper-less observer studies and is capable of providing a training infrastructure and co-ordinating remote collaborative viewing sessions (e.g. cancer reviews, interesting cases).

Using image simulation to test the effect of detector type on breast cancer detection

Introduction: The effect that the image quality associated with different image receptors has on cancer detection in mammography was measured using a novel method for changing the appearance of images. Method: A set of 270 mammography cases (one view, both breasts) was acquired using five Hologic Selenia and two Hologic Dimensions X-ray sets: 160 normal cases, 80 cases with subtle real non-calcification malignant lesions and 30 cases with biopsy proven benign lesions. Simulated calcification clusters were inserted into half of the normal cases. The 270 cases (Arm 1) were converted to appear as if they had been acquired on three other imaging systems: caesium iodide detector (Arm 2), needle image plate computed radiography (CR) (Arm 3) and powder phosphor CR (Arm 4). Five experienced mammography readers marked the location of suspected cancers in the images and classified the degree of visibility of the lesions. Statistical analysis was performed using JAFROC. Results: The differences in the visibility of calcification clusters between all pairs of arms were statistically significant (p<0.05), except between Arms 1 and 2. The difference in the visibility of non-calcification lesions was smaller than for calcification clusters, but the differences were still significant except between Arms 1 and 2 and between Arms 3 and 4. Conclusion: Detector type had a significant impact on the visibility of all types of subtle cancers, with the largest impact being on the visibility of calcification clusters.

MEDXVIEWER: PROVIDING A WEB-ENABLED WORKSTATION ENVIRONMENT FOR COLLABORATIVE AND REMOTE MEDICAL IMAGING VIEWING, PERCEPTION STUDIES AND READER TRAINING

MedXViewer (Medical eXtensible Viewer) has been developed to address the need for workstation-independent, picture archiving and communication system (PACS)-less viewing and interaction with anonymised medical images. The aim of this paper is to describe the design and features of MedXViewer as well as to introduce the new features available in the latest release (version 1.2). MedXViewer currently supports digital mammography and tomosynthesis. The flexible software design used to develop MedXViewer allows it to be easily extended to support other imaging modalities. Regions of interest can be drawn by a user, and any associated information about a mark, an image or a study can be added. The questions and settings can be easily configured depending on the need of the research allowing both ROC and FROC studies to be performed. Complex tree-like questions can be asked where a given answer presents the user to new questions. The hanging protocol can be specified for each study. Panning, windowing, zooming and moving through slices are all available while modality-specific features can be easily enabled, e.g. quadrant zooming in digital mammography and tomosynthesis studies. MedXViewer can integrate with a web-based image database OPTIMAM Medical Image Database allowing results and images to be stored centrally. The software can, alternatively, run without a network connection where the images and results can be encrypted and stored locally on a machine or external drive. MedXViewer has been used for running remote paper-less observer studies and is capable of providing a training infrastructure and coordinating remote collaborative viewing sessions.

Automated collection of medical images for research from heterogeneous systems: trials and tribulations

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. Over the past two decades both diagnostic and therapeutic imaging have undergone a rapid growth, the ability to be able to harness this large influx of medical images can provide an essential resource for research and training. Traditionally, the systematic collection of medical images for research from heterogeneous sites has not been commonplace within the NHS and is fraught with challenges including; data acquisition, storage, secure transfer and correct anonymisation. Here, we describe a semi-automated system, which comprehensively oversees the collection of both unprocessed and processed medical images from acquisition to a centralised database. The provision of unprocessed images within our repository enables a multitude of potential research possibilities that utilise the images. Furthermore, we have developed systems and software to integrate these data with their associated clinical data and annotations providing a centralised dataset for research. Currently we regularly collect digital mammography images from two sites and partially collect from a further three, with efforts to expand into other modalities and sites currently ongoing. At present we have collected 34,014 2D images from 2623 individuals. In this paper we describe our medical image collection system for research and discuss the wide spectrum of challenges faced during the design and implementation of such systems.

The oncology medical image database (OMI-DB)

Many projects to evaluate or conduct research in medical imaging require the large-scale collection of images (both unprocessed and processed) and associated data. This demand has led us to design and implement a flexible oncology image repository, which prospectively collects images and data from multiple sites throughout the UK. This Oncology Medical Image Database (OMI-DB) has been created to support research involving medical imaging and contains unprocessed and processed medical images, associated annotations and data, and where applicable expert-determined ground truths describing features of interest. The process of collection, annotation and storage is almost fully automated and is extremely adaptable, allowing for quick and easy expansion to disparate imaging sites and situations. Initially the database was developed as part of a large research project in digital mammography (OPTIMAM). Hence the initial focus has been digital mammography; as a result, much of the work described will focus on this field. However, the OMI -DB has been designed to support multiple modalities and is extensible and expandable to store any associated data with full anonymisation. Currently, the majority of associated data is made up of radiological, clinical and pathological annotations extracted from the UK’s National Breast Screening System (NBSS). In addition to the data, software and systems have been created to allow expert radiologists to annotate the images with interesting clinical features and provide descriptors of these features. The data from OMI-DB has been used in several observer studies and more are planned. To date we have collected 34,104 2D mammography images from 2,623 individuals.

Effect of image processing version on detection of non-calcification cancers in 2D digital mammography imaging

Image processing (IP) is the last step in the digital mammography imaging chain before interpretation by a radiologist. Each manufacturer has their own IP algorithm(s) and the appearance of an image after IP can vary greatly depending upon the algorithm and version used. It is unclear whether these differences can affect cancer detection. This work investigates the effect of IP on the detection of non-calcification cancers by expert observers. Digital mammography images for 190 patients were collected from two screening sites using Hologic amorphous selenium detectors. Eighty of these cases contained non-calcification cancers. The images were processed using three versions of IP from Hologic – default (full enhancement), low contrast (intermediate enhancement) and pseudo screen-film (no enhancement). Seven experienced observers inspected the images and marked the location of regions suspected to be non-calcification cancers assigning a score for likelihood of malignancy. This data was analysed using JAFROC analysis. The observers also scored the clinical interpretation of the entire case using the BSBR classification scale. This was analysed using ROC analysis. The breast density in the region surrounding each cancer and the number of times each cancer was detected were calculated. IP did not have a significant effect on the radiologists’ judgment of the likelihood of malignancy of individual lesions or their clinical interpretation of the entire case. No correlation was found between number of times each cancer was detected and the density of breast tissue surrounding that cancer.

Image processing can cause some malignant soft-tissue lesions to be missed in digital mammography images

AIMnTo investigate the effect of image processing on cancer detection in mammography.nnnMETHODS AND MATERIALSnAn observer study was performed using 349 digital mammography images of women with normal breasts, calcification clusters, or soft-tissue lesions including 191 subtle cancers. Images underwent two types of processing: FlavourA (standard) and FlavourB (added enhancement). Six observers located features in the breast they suspected to be cancerous (4,188 observations). Data were analysed using jackknife alternative free-response receiver operating characteristic (JAFROC) analysis. Characteristics of the cancers detected with each image processing type were investigated.nnnRESULTSnFor calcifications, the JAFROC figure of merit (FOM) was equal to 0.86 for both types of image processing. For soft-tissue lesions, the JAFROC FOM were better for FlavourA (0.81) than FlavourB (0.78); this difference was significant (p=0.001). Using FlavourA a greater number of cancers of all grades and sizes were detected than with FlavourB. FlavourA improved soft-tissue lesion detection in denser breasts (p=0.04 when volumetric density was over 7.5%) CONCLUSIONS: The detection of malignant soft-tissue lesions (which were primarily invasive) was significantly better with FlavourA than FlavourB image processing. This is despite FlavourB having a higher contrast appearance often preferred by radiologists. It is important that clinical choice of image processing is based on objective measures.