Tim C. Parr

Model-based detection of spiculated lesions in mammograms

Computer-aided mammographic prompting systems require the reliable detection of a variety of signs of cancer. In this paper we concentrate on the detection of spiculated lesions in mammograms. A spiculated lesion is typically characterized by an abnormal pattern of linear structures and a central mass. Statistical models have been developed to describe and detect both these aspects of spiculated lesions. We describe a generic method of representing patterns of linear structures, which relies on the use of factor analysis to separate the systematic and random aspects of a class of patterns. We model the appearance of central masses using local scale-orientation signatures based on recursive median filtering, approximated using principal-component analysis. For lesions of 16 mm and larger the pattern detection technique results in a sensitivity of 80% at 0.014 false positives per image, whilst the mass detection approach results in a sensitivity 80% at 0.23 false positives per image. Simple combination techniques result in an improved sensitivity and specificity close to that required to improve the performance of a radiologist in a prompting environment.

Finding orientated line patterns in digital mammographic images

In mammography the presence of subtle abnormalities such as stellate patterns and architectual distortions indicates possible malignancy. Radiologists do not always detect these abnormalities in screening mammograms; this has led to interest in computer‐aided mammographic interpretation where the radiologist is presented with computer‐generated ’prompts’ for abnormalities. A first step in this process is the detection of the “orientation”, “scale” and “strength” of linear structures in the mammograms. We discuss several generic methods for extracting this information from images and compare their per formance using synthetic images intended to simulate the appearance of mam mograms. We show significant differences in performance between the different methods. We also show results obtained for real mammograms.

Detection of Mammographic Microcalcifications Using a Statistical Model

Breast cancer is the leading cause of early mortality in women [1]. Reseach has shown that radiologists involved in screening mammograms for signs of early breast cancer can be aided by the provision of prompts to direct their attention towards potential abnormalities. In order for prompting to be successful in improving detection performance, the error rates of prompt generation algorithms must be strictly controlled [2]. Almost half of clinically occult breast cancers are due to the presence of microcalcifications [3]. In this paper, a new method is proposed to achieve the automatic detection of microcalcifications. A directional recursive median filtering (DRMF) technique at various scales and orientations is applied to the mammograms to obtain signatures at a pixel level which are characteristic of the local greylevel distribution [2], [4]. We have developed a Principal Component Analysis (PCA) statistical model based on the signatures [2], [4] which can be used for the detection of microcalcifications. A Receiver Operating Characteristic (ROC) study based on pixel classification is provided and the results are compared with approaches published in the literature [5], [6].

Prompting in Mammography: How Good Must Prompt Generators Be?

The UK breast screening programme generates in excess of one and a half million X-ray mammograms per year, all of which must be carefully searched for any signs of disease by experienced radiologists. Signs of early cancer are often small or subtle, and occur only infrequently. The consequences of errors are costly, and in many centres, films are read by two experienced radiologists in an attempt to improve accuracy.

Statistical modeling of lines and structures in mammograms

Computer-aided prompting systems require the reliable detection of a variety of mammographic signs of cancer. The emphasis of the work described in this paper is the correct classification of linear structures in mammograms, especially those associated with spiculated lesions. The detection of spiculated lesions can be based on the detection of the radiating pattern of linear structures associated with these lesions. The accuracy of automated stellate lesion detection algorithms can be improved by differentiating between the linear structures associated with lesions and those occurring in normal tissue. Statistical modeling, based on principal component analysis (PCA), has been developed for describing the cross-sectional profiles of linear structures, the motivation being that the shapes of intensity profiles may be characteristic of the type of structure. For the detection of spiculated lesions the main interest is to classify the linear structures into two classes, spicules and non-spicules. PCA models have been applied to whole mammograms to determine the probability that a particular type of linear structure (e.g. a spicule in this case) is present at any given location in the image.

Statistical modelling of oriented line patterns in mammograms

Malignant breast lesions in x-ray mammograms are often characterized by abnormal patterns of linear structures. Architectural distortions and stellate lesions are examples of patterns frequently presenting with an appearance of radiating linear structures. Attempts to automatically detect these abnormalities have generally concentrated on features of known importance, such as radiating linear structure concurrency, spread of focus and radial distance. We present an alternative statistically based representation that is both complete and uncommitted. Our representation places no emphasis on the features known to be important, yet clearly incorporates them. We present results for an experiment in which 92% of 9600 lesion/non-lesion pixels were classified correctly. Using a set of 150 high resolution digitized mammograms a lesion detection sensitivity of 80% was obtained at a specificity of 0.38 false positives per image.

Statistical Modelling of Lines and Structures in Mammograms

Computer-aided mammographic prompting systems require the reliable detection of a variety of signs of cancer. The emphasis of the work described is the correct classification of linear structures in mammograms. Statistical modelling, based on principal component analysis (PCA), has been developed for describing the cross-sectional profiles of linear structures, the motivation being that the shapes of intensity profiles may be characteristic of the type of structure. PCA models have been applied to whole mammograms to obtain images in which spicules, linear structures associated with stellate lesions, are emphasised. The aim is to improve the performance of automatic stellate lesion detection by concentrating on those structures most likely to be associated with lesions.

Comparing line detection methods for medical images

In medical image analysis there are various examples where the detection of linear structures can provide important information. These include mammography, blood vessel detection and the extraction of trebecular structure from bone images. There are several generic techniques which can be used to obtain linear structure information at a pixel level. Several of these techniques are compared on the basis of their performance in detecting line strength and orientation. In addition the authors comment on the possibility of using the same techniques in a multi-scale approach to also obtain line scale information. The methods discussed include those based on simple orientation bins, a multidirectional line operator, directional second order Gaussian derivatives, directional morphology, curvilinear structures detection and directional Fourier space.

Comparison of Methods for Combining Evidence for Spiculated Lesions

Breast cancer is a leading cause of fatality in women, with approximately 1 in 12 women affected by the disease during their lifetime [1], Mass screening of women using x-ray mammography is currently the most effective method of early detection of the disease; this is essential for successful treatment [2].