Harris V. Georgiou

A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry

This paper presents a fully automated segmentation and classification scheme for mammograms, based on breast density estimation and detection of asymmetry. First, image preprocessing and segmentation techniques are applied, including a breast boundary extraction algorithm and an improved version of a pectoral muscle segmentation scheme. Features for breast density categorization are extracted, including a new fractal dimension-related feature, and support vector machines (SVMs) are employed for classification, achieving accuracy of up to 85.7%. Most of these properties are used to extract a new set of statistical features for each breast; the differences among these feature values from the two images of each pair of mammograms are used to detect breast asymmetry, using an one-class SVM classifier, which resulted in a success rate of 84.47%. This composite methodology has been applied to the miniMIAS database, consisting of 322 (MLO) mammograms -including 15 asymmetric pairs of images-, obtained via a (noisy) digitization procedure. The results were evaluated by expert radiologists and are very promising, showing equal or higher success rates compared to other related works, despite the fact that some of them used only selected portions of this specific mammographic database. In contrast, our methodology is applied to the complete miniMIAS database and it exhibits the reliability that is normally required for clinical use in CAD systems.

Multi-scaled morphological features for the characterization of mammographic masses using statistical classification schemes

OBJECTIVE A comprehensive signal analysis approach on the mammographic mass boundary morphology is presented in this article. The purpose of this study is to identify efficient sets of simple yet effective shape features, employed in the original and multi-scaled spectral representations of the boundary, for the characterization of the mammographic mass. These new methods of mass boundary representation and processing in more than one domain greatly improve the information content of the base data that is used for pattern classification purposes, introducing comprehensive spectral and multi-scale wavelet versions of the original boundary signals. The evaluation is conducted against morphological and diagnostic characterization of the mass, using statistical methods, fractal dimension analysis and a wide range of classifier architectures. METHODS AND MATERIALS This study consists of (a) the investigation of the original radial distance measurements under the complete spectrum of signal analysis, (b) the application of curve feature extractors of morphological characteristics and the evaluation of the discriminative power of each one of them, by means of statistical significance analysis and dataset fractal dimension, and (c) the application of a wide range of classifier architectures on these morphological datasets, in order to conduct a comparative evaluation of the efficiency and effectiveness of all architectures, for mammographic mass characterization. Radial distance signal was exploited using the discrete Fourier transform (DFT) and the discrete wavelet transform (DWT) as additional carrier signals. Seven uniresolution feature functions were applied over these carrier signals and multiple shape descriptors were created. Classification was conducted against mass shape type and clinical diagnosis, using a wide range of linear and non-linear classifiers, including linear discriminant analysis (LDA), least-squares minimum distance (LSMD), k-nearest neighbor (k-NN), radial basis function (RBF) and multi-layered perceptron (MLP) neural networks (NN), and support vector machines (SVM). Fractal analysis was employed as a dataset analysis tool in the feature selection phase. The discriminative power of the features produced by this composite analysis is subsequently analyzed by means of multivariate analysis of variance (MANOVA) and tested against two distinct classification targets, namely (a) the morphological shape type of the mass and (b) the histologically verified clinical diagnosis for each mammogram. RESULTS Statistical analysis and classification results have shown that the discrimination value of the features extracted from the DWT components and especially the DFT spectrum, are of great importance. Furthermore, much of the information content of the curve features in the case of DFT and DWT datasets is directly related to the texture and fine-scale details of the corresponding envelope signal of the spectral components. Neural classifiers outperformed all other methods (SVM not used because they are mainly two-class classifiers) with overall success rate of 72.3% for shape type identification, while SVM achieved the overall highest 91.54% for clinical diagnosis. Receiver operating characteristic (ROC) analysis has been employed to present the sensitivity and specificity of the results of this study.

A fully automated complete segmentation scheme for mammograms

This paper presents a fully automated complete segmentation method for mammographic images. Image preprocessing techniques are first applied to mammograms to remove the noise and then a breast boundary extraction algorithm is implemented, in order to distinguish breast tissue from the background. Next, an improved version of an existing pectoral muscle scheme is performed and a new nipple segmentation technique is applied, detecting the nipple when it is in profile. This improves the estimated breast boundary and serves as a key-point for further processing of the image. This composite method has been implemented and applied to miniMIAS, one of the most well-known mammographic databases. This database consists of 322 mediolateral oblique (MLO) view mammograms, obtained via a digitization procedure. The results are evaluated by an expert radiologist and are very promising. Accordingly, it is expected that this procedure can produce improved results, when applied to high-quality digital mammograms.

A game-theoretic approach to weighted majority voting for combining SVM classifiers

A new approach from the game-theoretic point of view is proposed for the problem of optimally combining classifiers in dichotomous choice situations. The analysis of weighted majority voting under the viewpoint of coalition gaming, leads to the existence of analytical solutions to optimal weights for the classifiers based on their prior competencies. The general framework of weighted majority rules (WMR) is tested against common rank-based and simple majority models, as well as two soft-output averaging rules. Experimental results with combined support vector machine (SVM) classifiers on benchmark classification tasks have proven that WMR, employing the theoretically optimal solution for combination weights proposed in this work, outperformed all the other rank-based, simple majority and soft-output averaging methods. It also provides a very generic and theoretically well-defined framework for all hard-output (voting) combination schemes between any type of classifier architecture.

Virtual Prototyping: From Concept to 3D Design and Prototyping in Hours

The use of 3D computer-aided design (CAD) solutions in the apparel industry is still lagging behind in comparison to other industries (such as, e.g. the automotive). The technological challenges in many aspects are more complex, two of which are more prominent, namely (a) the re-construction of a 3D (threedimensional) human body on a computer system, (b) the 3D simulation of soft materials (simulation of garments made of cloth). The goal of this section is to present a comprehensive methodology for garment design effected directly in 3D, to be used throughout the process of product design and product development. No physical prototypes will be necessary for this process as they will be re-placed by virtual prototypes. The aim is to offer a new approach to reduce the timeconsuming tasks of design and prototyping currently based on 2D CAD systems and physical samples.

A Multi-Element Detector System for Intelligent Imaging: I-ImaS

I-ImaS is a European project aiming to produce new, intelligent X-ray imaging systems using novel APS sensors to create optimal diagnostic images. Initial systems concentrate on mammography and encephalography. Later development will yield systems for other types of radiography such as industrial QA and homeland security. The I-ImaS system intelligence, due to APS technology and FPGAs, allows real-time analysis of data during image acquisition, giving the capability to build a truly adaptive imaging system with the potential to create images with maximum diagnostic information within given dose constraints. A companion paper deals with the DAQ system and preliminary characterization. This paper considers the laboratory X-ray characterization of the detector elements of the I-ImaS system. The characterization of the sensors when tiled to form a strip detector will be discussed, along with the appropriate correction techniques formulated to take into account the misalignments between individual sensors within the array. Preliminary results show that the detectors have sufficient performance to be used successfully in the initial mammographic and encephalographic I-ImaS systems under construction and this paper will further discuss the testing of these systems and the iterative processes used for intelligence upgrade in order to obtain the optimal algorithms and settings.

Adaptive image content-based exposure control for scanning applications in radiography

I-ImaS (Intelligent Imaging Sensors) is a European project which has designed and developed a new adaptive X-ray imaging system using on-line exposure control, to create locally optimized images. The I-ImaS system allows for real-time image analysis during acquisition, thus enabling real-time exposure adjustment. This adaptive imaging system has the potential of creating images with optimal information within a given dose constraint and to acquire optimally exposed images of objects with variable density during one scan. In this paper we present the control system and results from initial tests on mammographic and encephalographic images. Furthermore, algorithms for visualization of the resulting images, consisting of unevenly exposed image regions, are developed and tested. The preliminary results show that the same image quality can be achieved at 30-70% lower dose using the I-ImaS system compared to conventional mammography systems.

A scanning system for intelligent imaging: I-ImaS

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce adaptive x-ray imaging systems using Monolithic Active Pixel Sensors (MAPS) to create optimal diagnostic images. Initial systems concentrate on mammography and cephalography. The on-chip intelligence available to MAPS technology will allow real-time analysis of data during image acquisition, giving the capability to build a truly adaptive imaging system with the potential to create images with maximum diagnostic information within given dose constraints. In our system, the exposure in each image region is optimized and the beam intensity is a function not only of tissue thickness and attenuation, but also of local physical and statistical parameters found in the image itself. Using a linear array of detectors with on-chip intelligence, the system will perform an on-line analysis of the image during the scan and then will optimize the X-ray intensity in order to obtain the maximum diagnostic information from the region of interest while minimizing exposure of less important, or simply less dense, regions. This paper summarizes the testing of the sensors and their electronics carried out using synchrotron radiation, x-ray sources and optical measurements. The sensors are tiled to form a 1.5D linear array. These have been characterised and appropriate correction techniques formulated to take into account misalignments between individual sensors. Full testing of the mammography and cephalography I-ImaS prototypes is now underway and the system intelligence is constantly being upgraded through iterative testing in order to obtain the optimal algorithms and settings.

Design and Characterization of the I-ImaS Multi-Element X-Ray Detector System

I-ImaS (Intelligent Imaging Sensors) is a European project aiming to produce new, intelligent X-ray imaging systems using novel APS sensors to create optimal diagnostic images. Initial systems have been constructed for medical imaging; specifically mammography and dental encephalography. However, the I-ImaS system concept could be applied to all areas of X-ray imaging, including homeland security and industrial QA. The I-ImaS system intelligence is implemented by the use of APS technology and FPGAs, allowing real-time analysis of data during image acquisition. This gives the system the capability to perform as an on-the-fly adaptive imaging system, with the potential to create images with maximum diagnostic information within given dose constraints. The I-ImaS system uses a scanning linear array of scintillator-coupled 1.5-D CMOS Active Pixel Sensors to create a full 2-D X-ray image of an object. This paper describes the parameters considered when choosing the scintillator elements of the detectors. A study of the positioning of the sensors to form a linear detector is also considered, along with a discussion of the potential losses in image quality associated with creating a linear sensor by tiling many smaller sensors. Preliminary results show that the detectors have sufficient performance to be used successfully in the initial mammographic and encephalographic I-ImaS systems that are currently under construction.

The I-Imas project: end-users driven specifications for the design of a novel digital medical imaging system

The I-Imas (Intelligent Imaging Sensors) is an EU project whose objective is to design and develop intelligent imaging sensors and evaluate their use within an adaptive medical imaging system specifically tailored to Mammography and Dental Radiology. The system will employ an in line scanning technology approach and proposes the use of CMOS active pixels sensors. The I-Imas sensor will have the capability of processing the data on every pixel and be able to dynamically respond in real time to changing conditions during imaging recording. The result will be to minimise the radiation exposure to areas of low diagnostic information content while extracting the highest diagnostic information from region of high interest. The first phase of the I-Imas project deals with the characterisation of the key features in a medical image that carry the highest content of diagnostic information. With this objective in mind an End-Users Survey has been carried out. We have been distributed a questionnaire to experts in the field of mammography and dental radiology (the dental radiology results will be presented elsewhere): medical physicists, radiologists, radiographers and dentists. From this survey we have collected information about the most useful specifications to be implemented in the I-Imas imaging system. This paper discusses the results from the End-Users survey and considers design implications for the I-Imas sensors