Breast calcifications: analysis of imaging properties

Diagnosing Breast Cancer with Microwave Technology: remaining challenges and potential solutions with machine learning

Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation.

Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters

PURPOSE

Statistical modeling of an ultrasound backscattered echo envelope is used for tissue characterization. However, in the presence of complex structures within the analyzed area, estimation of parameters is disturbed and unreliable, e.g., in the case of breast tumor classification. In order to improve the differentiation of breast lesions, the authors proposed a method based on the segmentation of homodyned K distribution parameter maps. Regions within lesions of different scattering properties were extracted and analyzed. In order to improve the classification, the best-performing features were selected from various regions and then combined.

METHODS

A radio-frequency data set consisting of 103 breast lesions was used in the authors' analysis. Maps of homodyned K distribution parameters were created using an algorithm based on signal-to-noise ratio, kurtosis, and skewness of fractional-order envelope moments. A Markov random field model was used to segment parametric maps. Features of different segments were extracted and evaluated based on bootstrapping and the receiver operating characteristic curve. To determine the best-performing feature subset, the authors applied the joint mutual information criterion.

RESULTS

It was found that there were individual features which performed better than the ones commonly used for lesion characterization, like the parameter obtained through averaging of values over the whole lesion. The authors selected and discussed the best-performing features. Properties of different extracted regions were important and improved the distinction between benign and malignant tumors. The best performance was obtained by combining four features with the area under the receiver operating curve of 0.84.

CONCLUSIONS

The study showed that the analysis of internal changes in lesion parametric maps leads to a better classification of breast tumors. The authors recommend combining multiple features for characterization, instead of using only one parameter, especially in the case of heterogeneous lesions.

Small-window parametric imaging based on information entropy for ultrasound tissue characterization

Constructing ultrasound statistical parametric images by using a sliding window is a widely adopted strategy for characterizing tissues. Deficiency in spatial resolution, the appearance of boundary artifacts, and the prerequisite data distribution limit the practicability of statistical parametric imaging. In this study, small-window entropy parametric imaging was proposed to overcome the above problems. Simulations and measurements of phantoms were executed to acquire backscattered radiofrequency (RF) signals, which were processed to explore the feasibility of small-window entropy imaging in detecting scatterer properties. To validate the ability of entropy imaging in tissue characterization, measurements of benign and malignant breast tumors were conducted (n = 63) to compare performances of conventional statistical parametric (based on Nakagami distribution) and entropy imaging by the receiver operating characteristic (ROC) curve analysis. The simulation and phantom results revealed that entropy images constructed using a small sliding window (side length = 1 pulse length) adequately describe changes in scatterer properties. The area under the ROC for using small-window entropy imaging to classify tumors was 0.89, which was higher than 0.79 obtained using statistical parametric imaging. In particular, boundary artifacts were largely suppressed in the proposed imaging technique. Entropy enables using a small window for implementing ultrasound parametric imaging.

Classification of benign and malignant breast tumors by 2-d analysis based on contour description and scatterer characterization

Ultrasound B-mode scanning based on the echo intensity has become an important clinical tool for routine breast screening. The efficacy of the Nakagami parametric image based on the distribution of the backscattered signals for quantifying properties of breast tissue was recently evaluated. The B-mode and Nakagami images reflect different physical characteristic of breast tumors: the former describes the contour features, and the latter reflects the scatterer arrangement inside a tumor. The functional complementation of these two images encouraged us to propose a novel method of 2-D analysis based on describing the contour using the B-mode image and the scatterer properties using the Nakagami image, which may provide useful clues for classifying benign and malignant tumors. To validate this concept, raw data were acquired from 60 clinical cases, and five contour feature parameters (tumor circularity, standard deviation of the normalized radial length, area ratio, roughness index, and standard deviation of the shortest distance) and the Nakagami parameters of benign and malignant tumors were calculated. The receiver operating characteristic curve and fuzzy c-means clustering were used to evaluate the performances of combining the parameters in classifying tumors. The clinical results demonstrated the presence of a tradeoff between the sensitivity and specificity when either using a single parameter or combining two contour parameters to discriminate between benign and malignant cases. However, combining the contour parameters and the Nakagami parameter produces sensitivity and specificity that simultaneously exceed 80%, which means that the functional complementation from the B-scan and the Nakagami image indeed enhances the performance in diagnosing breast tumors.

Ultrasonic Nakagami imaging: a strategy to visualize the scatterer properties of benign and malignant breast tumors

Previous studies have demonstrated the usefulness of the Nakagami parameter in characterizing breast tumors by ultrasound. However, physicians or radiologists may need imaging tools in a clinical setting to visually identify the properties of breast tumors. This study proposed the ultrasonic Nakagami image to visualize the scatterer properties of breast tumors and then explored its clinical performance in classifying benign and malignant tumors. Raw data of ultrasonic backscattered signals were collected from 100 patients (50 benign and 50 malignant cases) using a commercial ultrasound scanner with a 7.5 MHz linear array transducer. The backscattered signals were used to form the B-scan and the Nakagami images of breast tumors. For each tumor, the average Nakagami parameter was calculated from the pixel values in the region-of-interest in the Nakagami image. The receiver operating characteristic (ROC) curve was used to evaluate the clinical performance of the Nakagami image. The results showed that the Nakagami image shadings in benign tumors were different from those in malignant cases. The average Nakagami parameters for benign and malignant tumors were 0.69 +/- 0.12 and 0.55 +/- 0.12, respectively. This means that the backscattered signals received from malignant tumors tend to be more pre-Rayleigh distributed than those from benign tumors, corresponding to a more complex scatterer arrangement or composition. The ROC analysis showed that the area under the ROC curve was 0.81 +/- 0.04 and the diagnostic accuracy was 82%, sensitivity was 92% and specificity was 72%. The results showed that the Nakagami image is useful to distinguishing between benign and malignant breast tumors.

Preliminary feasibility study of an in-line phase contrast X-ray imaging prototype

In this study, a series of imaging experiments on biological specimens, including human breast core biopsies, lumpectomy, and chicken tissues, as well as standard phantoms, were performed in an effort to investigate the feasibility of an in-line phase contrast X-ray imaging prototype. The prototype system employed in the study consists of a microfocus X-ray source with tungsten target and a digital flat panel detector, and it can be operated in both conventional attenuation-based imaging mode and in-line phase contrast imaging mode. Biological specimens were imaged in the conventional mode and phase contrast mode with the same source-to-image-detector distance (SID), and phase contrast images exhibited both improved image quality compared with conventional images, and the overshooting patterns along the boundaries in the specimens, which revealed the occurrence of the edge enhancement effect provided by the phase contrast technique. In addition, the performance of the phase contrast mode and conventional mode was compared based on the American College of Radiology (ACR) phantom imaging and contrast detail mammography (CDMAM) phantom-based contrast detail analysis with two experimental settings: one with the same SID and the other with the same object entrance exposure. In both pairs of comparison under our experimental conditions, the phase contrast imaging mode exhibited improved image quality as compared to the conventional mode, which further supported the feasibility of the prototype.

Subsurface probing of calcifications with spatially offset Raman spectroscopy (SORS): future possibilities for the diagnosis of breast cancer

Breast calcifications are often the only mammographic features indicating the presence of a cancerous lesion. Calcium oxalate (type I) may be found in and around benign lesions, however calcium hydroxyapatite (type II) is usually found within proliferative lesions, which can include both benign and malignant pathologies. However, the composition of type II calcifications has been demonstrated to vary between benign and malignant proliferative lesions, and could be an indicator for the possible disease state. Raman spectroscopy has previously been demonstrated as a powerful tool for non-destructive analysis of tissues, utilising laser light to probe chemical composition. Raman spectroscopy is traditionally a surface technique. However, we have recently developed methods that permit its application for obtaining sample composition to clinically relevant depths of many mm. We report the first demonstration of spatially offset Raman spectroscopy (SORS) for potential in vivo breast analysis. This study evaluates the possibility of utilising SORS for measuring calcification composition through varying thicknesses of tissues (2 to 10 mm), which is about one to two orders of magnitude deeper than has been possible with conventional Raman approaches. SORS can be used to distinguish non-invasively between calcification types I and II (and carbonate substitution of phosphate in calcium hydroxyapatite) within tissue of up to 10 mm deep. This result secures the first step in taking this technique forward for clinical applications seeking to use Raman spectroscopy as an adjunct to mammography for early diagnosis of breast cancer, by utilising both soft tissue and calcification signals. Non-invasive elucidation of calcification composition, and hence type, associated with benign or malignant lesions, could eliminate the requirement for biopsy in many patients.

Prospects for the diagnosis of breast cancer by noninvasive probing of calcifications using transmission Raman spectroscopy

Breast calcifications can be found in both benign and malignant lesions, and the composition of these calcifications can indicate the possible disease state. As current practices such as mammography and histopathology examine the morphology of the specimen, they cannot reliably distinguish between the two types of calcification, which frequently are the only mammographic features that indicate the presence of a cancerous lesion. Raman spectroscopy is an optical technique capable of obtaining biochemical information of a sample in situ. We demonstrate for the first time the noninvasive recovery of Raman spectra of calcified materials buried within a chicken breast tissue slab 16 mm thick, achieved using transmission Raman spectroscopy. The spectra of both calcium hydroxyapatite (HAP) and calcium oxalate monohydrate (COM) are obtained and chemically identified. The experimental geometry and gross insensitivity of the Raman signal to the depth of the calcified lesion makes the concept potentially well suited for probing human female breasts, in conjunction with existing mammography or ultrasound, to provide complementary data in the early diagnosis of breast cancer.

Mammographic masses characterization based on localized texture and dataset fractal analysis using linear, neural and support vector machine classifiers

OBJECTIVE

Localized texture analysis of breast tissue on mammograms is an issue of major importance in mass characterization. However, in contrast to other mammographic diagnostic approaches, it has not been investigated in depth, due to its inherent difficulty and fuzziness. This work aims to the establishment of a quantitative approach of mammographic masses texture classification, based on advanced classifier architectures and supported by fractal analysis of the dataset of the extracted textural features. Additionally, a comparison of the information content of the proposed feature set with that of the qualitative characteristics used in clinical practice by expert radiologists is presented.

METHODS AND MATERIAL

An extensive set of textural feature functions was applied to a set of 130 digitized mammograms, in multiple configurations and scales, constructing compact datasets of textural "signatures" for benign and malignant cases of tumors. These quantitative textural datasets were subsequently studied against a set of a thorough and compact list of qualitative texture descriptions of breast mass tissue, normally considered under a typical clinical assessment, in order to investigate the discriminating value and the statistical correlation between the two sets. Fractal analysis was employed to compare the information content and dimensionality of the textural features datasets with the qualitative information provided through medical diagnosis. A wide range of linear and non-linear classification architectures was employed, including linear discriminant analysis (LDA), least-squares minimum distance (LSMD), K-nearest-neighbors (K-nn), radial basis function (RBF) and multi-layer perceptron (MLP) artificial neural network (ANN), as well as support vector machine (SVM) classifiers. The classification process was used as the means to evaluate the inherent quality and informational content of each of the datasets, as well as the objective performance of each of the classifiers themselves in real classification of mammographic breast tumors against verified diagnosis.

RESULTS

Textural features extracted at larger scales and sampling box sizes proved to be more content-rich than their equivalents at smaller scales and sizes. Fractal analysis on the dimensionality of the textural datasets verified that reduced subsets of optimal feature combinations can describe the original feature space adequately for classification purposes and at least the same detail and quality as the list of qualitative texture descriptions provided by a human expert. Non-linear classifiers, especially SVMs, have been proven superior to any linear equivalent. Breast mass classification of mammograms, based only on textural features, achieved an optimal score of 83.9%, through SVM classifiers.

Visualisation of calcifications and thin collagen strands in human breast tumour specimens by the diffraction-enhanced imaging technique: a comparison with conventional mammography and histology

Six excised human breast tissue specimens carrying benign and malignant tumours were examined with the diffraction-enhanced imaging technique. Diffraction-enhanced images were compared with diagnostic screen-film mammograms and the correlation with histological information of the specimens was established. The enhanced visibility of calcifications, some of which were smaller than 0.15 mm in diameter, is reported in detail. Fine details of the structures such as strands of collagen and contours between glandular and adipose tissue, which are barely visible at the contrast detection limit in the conventional absorption-based mammograms, are clearly visible in the diffraction-enhanced images. Microscopic study of the stained histopathological sections unequivocally confirms the correlation of the radiographic findings with the morphologic changes in specimens. An increased soft tissue contrast and a combination of information obtained with disparate diffraction-enhanced images provide better visibility of mammographically indistinguishable features. This kind of additional structural information of the breast tissue is required to improve assessment accuracy and earlier detection of the breast lesions. These advances in image quality make the method a very promising candidate for mammography.

Improved image contrast of calcifications in breast tissue specimens using diffraction enhanced imaging

The contrast of calcifications in images of breast tissue specimens using a synchrotron-based diffraction enhanced imaging (DEI) apparatus has been measured and is compared to the contrast in images acquired using a conventional synchrotron-based radiographic imaging modality. DEI is an imaging modality which derives image contrast from x-ray absorption, refraction and small-angle scatter-rejection (extinction), unlike conventional radiographic techniques, which can only derive contrast from absorption. DEI is accomplished by inserting an analyser crystal in the beam path between the sample and the detector. Two of the three breast tissue specimens contained calcifications associated with cancer, while a third contained benign calcifications. Results of the image analysis indicate that the DEI contrast of images taken with the analyser crystal tuned to the peak of its rocking curve, was as much as 19 times that of the conventional radiograph, with an average of 5.5 for all calcifications. This improved image contrast for even near-pixel-size calcifications suggests potential utility for DEI in breast imaging.

Computer-aided diagnosis of mammographic microcalcification clusters

Computer-aided diagnosis techniques in medical imaging are developed for the automated differentiation between benign and malignant lesions and go beyond computer-aided detection by providing cancer likelihood for a detected lesion given image and/or patient characteristics. The goal of this study was the development and evaluation of a computer-aided detection and diagnosis algorithm for mammographic calcification clusters. The emphasis was on the diagnostic component, although the algorithm included automated detection, segmentation, and classification steps based on wavelet filters and artificial neural networks. Classification features were selected primarily from descriptors of the morphology of the individual calcifications and the distribution of the cluster. Thirteen such descriptors were selected and, combined with patient's age, were given as inputs to the network. The features were ranked and evaluated for the classification of 100 high-resolution, digitized mammograms containing biopsy-proven, benign and malignant calcification clusters. The classification performance of the algorithm reached a 100% sensitivity for a specificity of 85% (receiver operating characteristic area index Az = 0.98 +/- 0.01). Tests of the algorithm under various conditions showed that the selected features were robust morphological and distributional descriptors, relatively insensitive to segmentation and detection errors such as false positive signals. The algorithm could exceed the performance of a similar visual analysis system that was used as basis for development and, combined with a simple image standardization process, could be applied to images from different imaging systems and film digitizers with similar sensitivity and specificity rates.

Measurement of image contrast using diffraction enhanced imaging

Refraction contrast of simple objects obtained using diffraction enhanced imaging (DEI) was studied and compared to conventional radiographic contrast. Lucite cylinders and nylon wires were imaged using monochromatic synchrotron radiation at the National Synchrotron Light Source (http://nslsweb. nsls.bnl.gov/nsls/Default.htm) at the Brookhaven National Laboratory. The DEI images were obtained by placing a silicon analyser crystal tuned to the [333] diffraction plane in the beam path between the sample and the detector. To compare the DEI images with conventional radiographic images requires a consistent definition of refraction and absorption contrast. Conventional definitions of contrast favour conventional radiography and DEI contrast is defined to emphasize the specific characteristics of DEI. The proposed definitions were then used to find the DEI gain (the ratio of the DEI contrast with respect to the conventional image contrast). The results presented here show that the DEI gain is consistently greater than 1, indicating that DEI provides more contrast information than conventional radiography.

Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography

INTRODUCTION/OBJECTIVE

The purpose of this study was to evaluate a computer based method for differentiating malignant from benign clustered microcalcifications, comparing it with the performance of three physicians.

METHODS AND MATERIAL

Materials for the study are 240 suspicious microcalcifications on mammograms from 220 female patients who underwent breast biopsy, following hook wire localization under mammographic guidance. The histologic findings were malignant in 108 cases (45%) and benign in 132 cases (55%). Those clusters were analyzed by a computer program and eight features of the calcifications (density, number, area, brightness, diameter average, distance average, proximity average, perimeter compacity average) were quantitatively estimated by a specific artificial neural network. Human input was limited to initial identification of the calcifications. Three physicians-observers were also evaluated for the malignant or benign nature of the clustered microcalcifications.

RESULTS

The performance of the artificial network was evaluated by receiver operating characteristics (ROC) curves. ROC curves were also generated for the performance of each observer and for the three observers as a group. The ROC curves for the computer and for the physicians were compared and the results are:area under the curve (AUC) value for computer is 0.937, for physician-1 is 0.746, for physician-2 is 0.785, for physician-3 is 0.835 and for physicians as a group is 0.810. The results of the Student's t-test for paired data showed statistically significant difference between the artificial neural network and the physicians' performance, independently and as a group.

DISCUSSION AND CONCLUSION

Our study showed that computer analysis achieves statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications. This method, after further evaluation and improvement, may help radiologists and breast surgeons in better predictive estimation of suspicious clustered microcalcifications and reduce the number of biopsies for non-palpable benign lesions.

A novel approach to microcalcification detection using fuzzy logic technique

Breast cancer continues to be a significant public health problem in the United States. Approximately, 182,000 new cases of breast cancer are diagnosed and 46,000 women die of breast cancer each year. Even more disturbing is the fact that one out of eight women in the United States will develop breast cancer at some point during her lifetime. Since the cause of breast cancer remains unknown, primary prevention becomes impossible. Computer-aided mammography is an important and challenging task in automated diagnosis. It has great potential over traditional interpretation of film-screen mammography in terms of efficiency and accuracy. Microcalcifications are the earliest sign of breast carcinomas and their detection is one of the key issues for breast cancer control. In this study, a novel approach to microcalcification detection based on fuzzy logic technique is presented. Microcalcifications are first enhanced based on their brightness and nonuniformity. Then, the irrelevant breast structures are excluded by a curve detector. Finally, microcalcifications are located using an iterative threshold selection method. The shapes of microcalcifications are reconstructed and the isolated pixels are removed by employing the mathematical morphology technique. The essential idea of the proposed approach is to apply a fuzzified image of a mammogram to locate the suspicious regions and to interact the fuzzified image with the original image to preserve fidelity. The major advantage of the proposed method is its ability to detect microcalcifications even in very dense breast mammograms. A series of clinical mammograms are employed to test the proposed algorithm and the performance is evaluated by the free-response receiver operating characteristic curve. The experiments aptly show that the microcalcifications can be accurately detected even in very dense mammograms using the proposed approach.

Segmentation and numerical analysis of microcalcifications on mammograms using mathematical morphology

The top-hat and watershed algorithms of mathematical morphology have been applied to detect automatically and segment microcalcifications on mammograms digitized to a pixel resolution of 40 microns using a CCD camera. The database comprised 38 cases from the breast assessment clinic in Liverpool. For all cases, both craniocaudal (CC) and lateral oblique (LO) views were available. 19 cases were proven to be benign and 19 malignant based on cytology and histology. Malignant clusters contained more microcalcifications (14 malignant, 10 benign), occupied a larger area (37 mm2, 9 mm2) and had longer cluster perimeters than benign clusters (33.2 mm, 15.5 mm). Malignant microcalcifications exhibited a wider variety of shapes and were more heterogeneous in terms of image signal intensity than benign microcalcifications. Further mathematical morphology algorithms were applied to describe microcalcification shape in terms of the presence or absence of infoldings, elongation, narrow irregularities and wide irregularities. The three largest microcalcifications were selected for each case and, using a "leave-one-out" approach, each microcalcification was classified in respect of its five nearest neighbours as either malignant or benign. The area under the curve of a receiver operating characteristic (ROC) analysis of the proportion of the three microcalcifications which agreed with the true diagnosis increased from 0.73 (CC) and 0.63 (LO) to 0.79 when both views were considered. Next, each cluster in turn was ranked according to its agreement with the database as a whole over 21 features. An ROC analysis was performed to investigate the effect on sensitivity and specificity of the proportion of the nine nearest neighbours that agreed with the true classification. The largest area under the ROC curve was 0.84 produced by the four features of proportion of irregular microcalcifications, proportion of round microcalcifications, number of microcalcifications in the cluster and the interquartile range of microcalcification area. The shape of microcalcifications is confirmed as being of overriding importance in classifying cases as either malignant or benign. This observation motivates a further study enhanced by using magnified views digitized to a higher resolution by a laser scanner. This will enable the reliable assessment of the shape of a greater number of microcalcifications in each cluster, which is likely to increase further the discriminating power of the image analysis routines and lead to the development of an expert system for automatic mammographic screening.

Visibility of microcalcifications in computed and screen-film mammography

Due to the clinically and technically demanding nature of breast x-ray imaging, mammography still remains one of the few essentially film-based radiological imaging techniques in modern medical imaging. There are a range of possible benefits available if a practical and economical direct digital imaging technique can be introduced to routine clinical practice. There has been much debate regarding the minimum specification required for direct digital acquisition. One such direct digital system available is computed radiography (CR), which has a modest specification when compared with modern screen-film mammography (SFM) systems. This paper details two psychophysical studies in which the detection of simulated microcalcifications with CR has been directly compared to that with SFM. The first study found that under scatter-free conditions the minimum detectable size of microcalcification was approximately 130 microns for both SFM and CR. The second study found that SFM had a 4.6% higher probability of observers being able to correctly identify the shape of 350 microns diameter test details; there was no significant difference for-either larger or smaller test details. From the results of these studies it has been demonstrated that the modest specification of CR, in terms of limiting resolution, does not translate into a dramatic difference in the perception of details at the limit of detectability. When judging the imaging performance of a system it is more important to compare the signal-to-noise ratio transfer spectrum characteristics, rather than simply the modulation transfer function.

Microcalcification clustering parameters in breast disease: a morphometric analysis of radiographs of excision specimens

X-ray microradiography of surgically excised breast specimens offers the possibility of morphological characterization of calcifications. When combined with digital imaging techniques there exists added potential for obtaining valuable basic quantitative morphometric information regarding differences between microcalcifications in tissues exhibiting evidence of fibrocystic change, benign and malignant tumours. A total of 157 excised breast specimens from 84 patients were microradiographed using a Softex Super Soft X-ray unit and Kodak AA high resolution industrial film. A Quantimet 570C image analysis system was used to digitize and analyse the microradiographs. Of the 157 microradiographs, 51 (from 30 patients) revealed microcalcification clusters. The existence of significant differences between the three identified categories of tissue were indicated by clustering parameters. These included the number of particles per cluster, area of clusters, maximum distance to nearest neighbour, and geometric mean distance to nearest neighbour. The distribution pattern index (DPI), another of the clustering parameters used in this study, has been observed to be a particularly powerful discriminator. The value for fibrocystic change was found to be significantly smaller (0.514) than that for benign tumour (0.796) whilst that for benign tumour was observed to be significantly larger than that for malignant tumour (0.604) at a p-value of less than 0.05 (Kruskal-Wallis one-way analysis of variance).

Mammographically indeterminate microcalcifications--can we do any better?

In the first round of the mammographic screening program of the South Australian Breast X-ray Service, 80 (0.73%) of the first 10,848 women screened over 18 months were referred after primary and second-stage assessment for definitive histology because of microcalcifications. Obvious mass lesions associated with calcification were excluded from this study, as were women whose calcification was regarded as sufficiently benign to warrant routine rescreening in two years. After classic patterns of malignant microcalcification were excluded, a large group (75%) remained, whose calcifications fell into the indeterminate grades of radiological suspicion. Of these, only 15% proved to have cancer, and in one third of these the cancer was mammographically occult. A high rate of discordant readings was noted in lesions which ultimately proved benign. Neither family history, distribution of calcification nor the presence of a faint soft tissue density proved to be unfailingly reliable predictors of benign or malignant histology in this group. Vigorous pursuit of histopathological correlation and performance statistics are urged to monitor and minimise the proportion of women who remain in this indeterminate group and to follow their natural history. Current mammographic techniques are still inadequate for the provision of definitive information on microcalcification in all cases, but a sustained commitment will reduce the number proceeding to histology for benign disease.

Early detection of breast cancer: mammography

Detection of early breast cancer utilizing mammography can be accomplished through more frequent and earlier use of the test as well as exploring techniques to improve sensitivity and specificity. Efforts at educating primary care physicians about the role of mammography as well as training technologists and radiologists in performing optimum examinations and correctly interpreting them is of prime importance. The use of computers may aid in increasing the sensitivity of the examinations and may also provide improved feature analysis for the radiologist, thus enhancing the separation of benign and malignant disease. New technologies also show promise to improve the accuracy of mammography. Direct digital mammography and digital enhancement of standard mammograms are actively being investigated. The use of MRI and specifically 31P spectroscopy shows initial promise to increase the specificity of mammography. Several clinical trials are also under way to assess a possible niche which fine needle aspiration biopsy (FNAB) may occupy in the quest for early breast cancer detection. Any future collaborative work between Japan and Western countries should take into account the potential of these new methods for improving the sensitivity and specificity of mammography.

Extraction of quantitative blur measures for circumscribed lesions in mammograms

This study investigates ways of improving lesion diagnosis in mammograms by deriving quantitative descriptions of the lesion periphery. The descriptions are derived by computer image analysis methods. The degree of blur at lesion boundaries is of prime concern, as poorly outlined lesions can indicate malignancy. The need for quantitative analysis arises from psychological evidence suggesting that the human visual system cannot precisely estimate the degree of blur. To help find suitable measures a set of 'artificial' lesions has been generated by convolving a step-like edge with a set of Gaussian functions G(sigma) where sigma characterizes the degree of blur. From these generated lesion images the parameters sigma are derived by the process involving deconvolution. As the edge changes are most important in radial directions, the measures of sigma are calculated for each radial profile of the lesion. The derived individual values correspond very closely to those used to generate the lesions. Statistical measures obtained from them allow distinction between edges which are blurred to different extents and yet are impossible to differentiate visually. The artificial lesions will be combined with mammographic data, and similar measures derived. The work will be validated on real lesions for which the histological findings are known from performed biopsies.