In this paper we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists on the combination of two different methods. The first one, based on filtering techniques and gaussianity statistical tests, finds out the most obvious signals. The second one is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. In the false-positive reduction step we separate false signals from microcalcifications by means of a Support Vector Machine (SVM) classifier. Our algorithm yields a sensitivity of 95% with 0.6 false positive cluster per image on the 40 images of the Nijmegen database.

Automatic detection of clustered microcalcifications using a combined method and an SVM classifier

In this thesis, we introduce a mammogram analysis system developed for the automatic segmentation and analysis of mammograms. This original system has been designed to aid radiologists to detect breast cancer on mammograms. The system embodies attributedriven segmentation in which the attributes of an image are extracted progressively in a step-by-step, hierarchical fashion. Global, low-level attributes obtained in the early stages are used to derive local, high-level attributes in later stages, leading to increasing refinement and accuracy in image segmentation and analysis. The proposed system can be characterized as:  a bootstrap engine driven by the attributes of the images;  a solid framework supporting the process of hierarchical segmentation;  a universal platform for the development and integration of segmentation and analysis techniques; and  an extensible database in which knowledge about the image is accumulated. Central to this system are three major components: 1. a series of applications for attribute acquisition; 2. a standard format for attribute normalization; and 3. a database for attribute storage and data exchange between applications. The first step of the automatic process is to segment the mammogram hierarchically into several distinctive regions that represent the anatomy of the breast. The adequacy and quality of the mammogram are then assessed using the anatomical features obtained from segmentation. Further image analysis, such as breast density classification and lesion detection, may then be carried out inside the breast region. Several domain-specific algorithms have therefore been developed for the attribute acquisition component in the system. These include: 1. automatic pectoral muscle segmentation; 2. adequacy assessment of positioning and exposure; and

Attribute-driven segmentation and analysis of mammograms

Quality control in mammographic facilities has to be done periodically in order to ensure that the mammographic chain works properly. In particular global image quality is evaluated from a mammographic phantom film. A phantom is an object with the same anatomic shape and radiological response as an average dense-fleshed breast in which are embedded structures that mimic clinically relevant features such as microcalcifications, masses and fibers. This evaluation is done by visual observation of a phantom film and a global score is given depending on the number of objects seen by several observers. This paper presents the main results of a feasibility study of breast phantom scoring using digital image processing. First breast phantom films were digitized. For each category of structures, subimages were extracted from the digitized phantom. A noise reduction method was used as a pre-processing step. A local contrast enhancement was then applied. At last image segmentation was done. Noise reduction and contrast enhancement steps were both based on a direct contrast modification technique. Segmentation step was adapted to each embedded object. Nine digitized phantom films were studied and results show that an evaluation of mammographic facilities could be done using digital image processing.

Feasibility study of automating breast phantom scoring using image processing techniques

/pdf/attribute-driven-segmentation-and-analysis-of-mammograms-670toxrbog.pdf

Attribute-Driven Segmentation and Analysis of Mammograms

In this study different types of customized phantoms were used to examine a long term reproducibility check of a full field digital mammography system. These phantoms can be distinguished into three types: a PMMA (polimethylmethacrylate) object for an easy signal-to-noise ratio measurement in a defined region, a phantom with particular details of various dimensions (TORMAS) and a phantom for a contrast-detail analysis (CDMAM). In our center the ARTINIS ANALYZER software has been used since 2011 to weekly perform a CDMAM phantom analysis. This software is based on the original CDCOM algorithm suggested by the EUREF, the European Reference Organization for assured quality breast screening and diagnostic services. However the CDMAM phantom is a contrast-detail object designed for image quality type tests and not for long term reproducibility control. Since 2013, as suggested by the guidelines of Veneto region, a TORMAS weekly acquisition and analysis of data has also been performed. The purpose of this study is to discuss the data obtained by using the two different detail phantoms, CDMAM and TORMAS. These results can provide some correlations among the analyzed parameters comparing them with the ones emerging from the PMMA phantom analysis. The study reveals that the basic PMMA phantom is the object that provides the necessary information about the stability of the exposure parameters and the reproducibility of a mammographic equipment in terms of image quality indexes.

/pdf/long-term-reproducibility-check-of-a-full-field-digital-2tde69kgy4.pdf

Long Term Reproducibility Check of a Full Field Digital Mammography System

Quality control in mammography is based upon subjective interpretation of the image quality of a test phantom. In order to suppress subjectivity due to the human observer, automated computer analysis of the Leeds TOR(MAM) test phantom is investigated. Texture analysis via grey-level co-occurrence matrices is used to detect structures in the test object. Scoring of the substructures in the phantom is based on grey-level differences between regions and information from grey-level co-occurrence matrices. The results from scoring groups of particles within the phantom are presented.

/pdf/automated-quality-assurance-applied-to-mammographic-imaging-36w7v4c48l.pdf

Automated quality assurance applied to mammographic imaging

Objective:To present the results following a UK national patient dose audit of paediatric CT examinations, to propose updated UK national diagnostic reference levels (DRLs) and to analyse current p...

Analysis and results from a UK national dose audit of paediatric CT examinations.

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Automated quality assurance applied to mammographic imaging

Analysis and results from a UK national dose audit of paediatric CT examinations.