Identification of the breast boundary in mammograms using active contour models

Computerized Analysis of Mammogram Images for Early Detection of Breast Cancer

Breast cancer is widespread worldwide and can be cured if diagnosed early. Using digital mammogram images and image processing with artificial intelligence can play an essential role in breast cancer diagnosis. As many computerized algorithms for breast cancer diagnosis have significant limitations, such as noise handling and varying or low contrast in the images, it can be difficult to segment the abnormal region. These challenges could be overcome by proposing a new pre-processing model, exploring its impact on the post-processing module, and testing it on an extensive database. In this research work, the three-step method is proposed and validated on large databases of mammography images. The first step corresponded to the database classification, followed by the second step, which removed the pectoral muscle from the mammogram image. The third stage utilized new image-enhancement techniques and a new segmentation module to detect abnormal regions in a well-enhanced image to diagnose breast cancer. The pre-and post-processing modules are based on novel image processing techniques. The proposed method was tested using data collected from different hospitals in the Qassim Health Cluster, Qassim Province, Saudi Arabia. This database contained the five categories in the Breast Imaging and Reporting and Data System and consisted of 2892 images; the proposed method is analyzed using the publicly available Mammographic Image Analysis Society database, which contained 322 images. The proposed method gives good contrast enhancement with peak-signal to noise ratio improvement of 3 dB. The proposed method provides an accuracy of approximately 92% on 2892 images of Qassim Health Cluster, Qassim Province, Saudi Arabia. The proposed method gives approximately 97% on the Mammographic Image Analysis Society database. The novelty of the proposed work is that it could work on all Breast Imaging and Reporting and Data System categories. The performance of the proposed method demonstrated its ability to improve the diagnostic performance of the computerized breast cancer detection method.

Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer

Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.

Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs

Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.

Fully automated breast boundary and pectoral muscle segmentation in mammograms

Breast and pectoral muscle segmentation is an essential pre-processing step for the subsequent processes in computer aided diagnosis (CAD) systems. Estimating the breast and pectoral boundaries is a difficult task especially in mammograms due to artifacts, homogeneity between the pectoral and breast regions, and low contrast along the skin-air boundary. In this paper, a breast boundary and pectoral muscle segmentation method in mammograms is proposed. For breast boundary estimation, we determine the initial breast boundary via thresholding and employ Active Contour Models without edges to search for the actual boundary. A post-processing technique is proposed to correct the overestimated boundary caused by artifacts. The pectoral muscle boundary is estimated using Canny edge detection and a pre-processing technique is proposed to remove noisy edges. Subsequently, we identify five edge features to find the edge that has the highest probability of being the initial pectoral contour and search for the actual boundary via contour growing. The segmentation results for the proposed method are compared with manual segmentations using 322, 208 and 100mammograms from the Mammographic Image Analysis Society (MIAS), INBreast and Breast Cancer Digital Repository (BCDR) databases, respectively. Experimental results show that the breast boundary and pectoral muscle estimation methods achieved dice similarity coefficients of 98.8% and 97.8% (MIAS), 98.9% and 89.6% (INBreast) and 99.2% and 91.9% (BCDR), respectively.

Neighbourhood search feature selection method for content-based mammogram retrieval

Content-based image retrieval plays an increasing role in the clinical process for supporting diagnosis. This paper proposes a neighbourhood search method to select the near-optimal feature subsets for the retrieval of mammograms from the Mammographic Image Analysis Society (MIAS) database. The features based on grey level cooccurrence matrix, Daubechies-4 wavelet, Gabor, Cohen-Daubechies-Feauveau 9/7 wavelet and Zernike moments are extracted from mammograms available in the MIAS database to form the combined or fused feature set for testing various feature selection methods. The performance of feature selection methods is evaluated using precision, storage requirement and retrieval time measures. Using the proposed method, a significant improvement is achieved in mean precision rate and feature dimension. The results show that the proposed method outperforms the state-of-the-art feature selection methods.

Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms

This paper presents a review of recent advances in the development of methods for segmentation of the breast boundary and the pectoral muscle in mammograms. Regardless of improvement of imaging technology, accurate segmentation of the breast boundary and detection of the pectoral muscle are still challenging tasks for image processing algorithms. In this paper, we discuss problems related to mammographic image preprocessing and accurate segmentation. We review specific methods that were commonly used in most of the techniques proposed for segmentation of mammograms and discuss their advantages and disadvantages. Comparative analysis of the methods reported on is made difficult by variations in the datasets and procedures of evaluation used by the authors. We attempt to overcome some of these limitations by trying to compare methods which used the same dataset and have some similarities in approaches to the breast boundary segmentation and detection of the pectoral muscle. In this paper, we will address the most often used methods for segmentation such as thresholding, morphology, region growing, active contours, and wavelet filtering. These methods, or their combinations, are the ones most used in the last decade by the majority of work published in this image processing domain.

Unsupervised segmentation of lung fields in chest radiographs using multiresolution fractal feature vector and deformable models

Segmenting lung fields in a chest radiograph is essential for automatically analyzing an image. We present an unsupervised method based on multiresolution fractal feature vector. The feature vector characterizes the lung field region effectively. A fuzzy c-means clustering algorithm is then applied to obtain a satisfactory initial contour. The final contour is obtained by deformable models. The results show the feasibility and high performance of the proposed method. Furthermore, based on the segmentation of lung fields, the cardiothoracic ratio (CTR) can be measured. The CTR is a simple index for evaluating cardiac hypertrophy. After identifying a suspicious symptom based on the estimated CTR, a physician can suggest that the patient undergoes additional extensive tests before a treatment plan is finalized.

Mammographic Breast Density in Chinese Women: Spatial Distribution and Autocorrelation Patterns

Mammographic breast density (MBD) is a strong risk factor for breast cancer. The spatial distribution of MBD in the breast is variable and dependent on physiological, genetic, environmental and pathological factors. This pilot study aims to define the spatial distribution and autocorrelation patterns of MBD in Chinese women aged 40–60. By analyzing their digital mammographic images using a public domain Java image processing program for segmentation and quantification of MBD, we found their left and right breasts were symmetric to each other in regard to their breast size (Total Breast Area), the amount of BMD (overall PD) and Moran's I values. Their MBD was also spatially autocorrelated together in the anterior part of the breast in those with a smaller breast size, while those with a larger breast size tend to have their MBD clustered near the posterior part of the breast. Finally, we observed that the autocorrelation pattern of MBD was dispersed after a 3-year observation period.

Mammogram segmentation using maximal cell strength updation in cellular automata

Breast cancer is the most frequently diagnosed type of cancer among women. Mammogram is one of the most effective tools for early detection of the breast cancer. Various computer-aided systems have been introduced to detect the breast cancer from mammogram images. In a computer-aided diagnosis system, detection and segmentation of breast masses from the background tissues is an important issue. In this paper, an automatic segmentation method is proposed to identify and segment the suspicious mass regions of mammogram using a modified transition rule named maximal cell strength updation in cellular automata (CA). In coarse-level segmentation, the proposed method performs an adaptive global thresholding based on the histogram peak analysis to obtain the rough region of interest. An automatic seed point selection is proposed using gray-level co-occurrence matrix-based sum average feature in the coarse segmented image. Finally, the method utilizes CA with the identified initial seed point and the modified transition rule to segment the mass region. The proposed approach is evaluated over the dataset of 70 mammograms with mass from mini-MIAS database. Experimental results show that the proposed approach yields promising results to segment the mass region in the mammograms with the sensitivity of 92.25% and accuracy of 93.48%.

Self-parameterized active contours based on regional edge structure for medical image segmentation

This work introduces a novel framework for unsupervised parameterization of region-based active contour regularization and data fidelity terms, which is applied for medical image segmentation. The work aims to relieve MDs from the laborious, time-consuming task of empirical parameterization and bolster the objectivity of the segmentation results. The proposed framework is inspired by an observed isomorphism between the eigenvalues of structure tensors and active contour parameters. Both may act as descriptors of the orientation coherence in regions containing edges. The experimental results demonstrate that the proposed framework maintains a high segmentation quality without the need of trial-and-error parameter adjustment.

Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM

This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128×128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99% sensitivity and 99% specificity, and on DDSM mammogram database, it obtained 97% sensitivity and 96% specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the other well-known texture descriptors namely gray-level co-occurrence matrix (GLCM) and discrete cosine transform (DCT).

A probabilistic approach for breast boundary extraction in mammograms

The extraction of the breast boundary is crucial to perform further analysis of mammogram. Methods to extract the breast boundary can be classified into two categories: methods based on image processing techniques and those based on models. The former use image transformation techniques such as thresholding, morphological operations, and region growing. In the second category, the boundary is extracted using more advanced techniques, such as the active contour model. The problem with thresholding methods is that it is a hard to automatically find the optimal threshold value by using histogram information. On the other hand, active contour models require defining a starting point close to the actual boundary to be able to successfully extract the boundary. In this paper, we propose a probabilistic approach to address the aforementioned problems. In our approach we use local binary patterns to describe the texture around each pixel. In addition, the smoothness of the boundary is handled by using a new probability model. Experimental results show that the proposed method reaches 38% and 50% improvement with respect to the results obtained by the active contour model and threshold-based methods respectively, and it increases the stability of the boundary extraction process up to 86%.

Objective models of compressed breast shapes undergoing mammography

PURPOSE

To develop models of compressed breasts undergoing mammography based on objective analysis, that are capable of accurately representing breast shapes in acquired clinical images and generating new, clinically realistic shapes.

METHODS

An automated edge detection algorithm was used to catalogue the breast shapes of clinically acquired cranio-caudal (CC) and medio-lateral oblique (MLO) view mammograms from a large database of digital mammography images. Principal component analysis (PCA) was performed on these shapes to reduce the information contained within the shapes to a small number of linearly independent variables. The breast shape models, one of each view, were developed from the identified principal components, and their ability to reproduce the shape of breasts from an independent set of mammograms not used in the PCA, was assessed both visually and quantitatively by calculating the average distance error (ADE).

RESULTS

The PCA breast shape models of the CC and MLO mammographic views based on six principal components, in which 99.2% and 98.0%, respectively, of the total variance of the dataset is contained, were found to be able to reproduce breast shapes with strong fidelity (CC view mean ADE = 0.90 mm, MLO view mean ADE = 1.43 mm) and to generate new clinically realistic shapes. The PCA models based on fewer principal components were also successful, but to a lesser degree, as the two-component model exhibited a mean ADE = 2.99 mm for the CC view, and a mean ADE = 4.63 mm for the MLO view. The four-component models exhibited a mean ADE = 1.47 mm for the CC view and a mean ADE = 2.14 mm for the MLO view. Paired t-tests of the ADE values of each image between models showed that these differences were statistically significant (max p-value = 0.0247). Visual examination of modeled breast shapes confirmed these results. Histograms of the PCA parameters associated with the six principal components were fitted with Gaussian distributions. The six-component model was also used to generate CC and MLO view mammogram breast shapes, using the mean PCA parameter values of these distributions and randomly generated values based on the fitted Gaussian distributions, which resemble clinically encountered breasts. A spreadsheet with the data necessary to apply this model is provided as the supplementary material.

CONCLUSIONS

Our PCA models of breast shapes in both mammographic views successfully reproduce analyzed breast shapes and generate new clinically relevant shapes. This work can aid in research applications which incorporate breast shape modeling, such as x-ray scatter correction, dosimetry, and image registration.

Pectoral muscle segmentation: a review

Mammograms are X-ray images of breasts which are used to detect breast cancer. The pectoral muscle is a mass of tissue on which the breast rests. During routine mammographic screenings, in medio-lateral oblique (MLO) views, the pectoral muscle turns up in the mammograms along with the breast tissues. The pectoral muscle has to be segmented from the mammogram for an effective automated computer aided diagnosis (CAD). This is due to the fact that pectoral muscles have pixel intensities and texture similar to that of breast tissues which can result in awry CAD results. As a result, a lot of effort has been put into the segmentation of pectoral muscles and finding its contour with the breast tissues. To the best of our knowledge, currently there is no definitive literature available which provides a comprehensive review about the current state of research in this area of pectoral muscle segmentation. We try to address this shortcoming by providing a comprehensive review of research papers in this area. A conscious effort has been made to avoid deviating into the area of automated breast cancer detection.

Breast skin-line detection using dynamic programming

In this paper, we present a novel method to extract the breast skin-line based on dynamic programming. Skin-line extraction is an important preprocessing step in CAD systems; however, it is a challenging problem due to the presence of noise, underexposed regions, which results in a low contrast area near the skin-air interface, and artifacts such as labels. Our proposal utilizes the stroma edge to constrain searching for the border. In order to cope with noise, we consider several candidate points for the border interface which are obtained by the Laplace operator applied in pre-defined directions in the mammogram. The breast contour is obtained from the candidate points using a dynamic programming algorithm. This utilizes a criterion of optimality to obtain the optimum contour by minimization of a cost function. The method was evaluated using 82 mammograms whose contour were manually extracted by a radiologist from the mini-MIAS database. The Polyline Distance Measure was evaluated for each contour selected with the proposed method, obtaining a mean error of 2.05 pixels and a standard deviation of 0.80.

Dynamic multiple thresholding breast boundary detection algorithm for mammograms

PURPOSE

Automated detection of breast boundary is one of the fundamental steps for computer-aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms.

METHODS

A large data set of 716 screen-film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary (MTBB-Initial) was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary (MTBB-Final). The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap measure (AOM).

RESULTS

In comparison with the authors' previously developed gradient-based breast boundary (GBB) algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels (4.8 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels (1.2 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test (p < 0.0001).

CONCLUSIONS

The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.

Segmentation of regions of interest in mammograms in a topographic approach

This paper presents a novel method for the segmentation of regions of interest in mammograms. The algorithm concurrently delineates the boundaries of the breast boundary, the pectoral muscle, as well as dense regions that include candidate masses. The resulting representation constitutes an analysis of the global structure of the object in the mammogram. We propose a topographic representation called the isocontour map, in which a salient region forms a dense quasi-concentric pattern of contours. The topological and geometrical structure of the image is analyzed using an inclusion tree that is a hierarchical representation of the enclosure relationships between contours. The "saliency" of a region is measured topologically as the minimum nesting depth. Features at various scales are analyzed in multiscale isocontour maps, and we demonstrate that the multiscale scheme provides an efficient way of achieving better delineations. Experimental results demonstrate that the proposed method has potential as the basis for a prompting system in mammogram mass detection.

An automated approach for estimation of breast density

Breast density is a strong risk factor for breast cancer; however, no standard assessment method exists. An automated breast density method was modified and compared with a semi-automated, user-assisted thresholding method (Cumulus method) and the Breast Imaging Reporting and Data System four-category tissue composition measure for their ability to predict future breast cancer risk. The three estimation methods were evaluated in a matched breast cancer case-control (n = 372 and n = 713, respectively) study at the Mayo Clinic using digitized film mammograms. Mammograms from the craniocaudal view of the noncancerous breast were acquired on average 7 years before diagnosis. Two controls with no previous history of breast cancer from the screening practice were matched to each case on age, number of previous screening mammograms, final screening exam date, menopausal status at this date, interval between earliest and latest available mammograms, and residence. Both Pearson linear correlation (R) and Spearman rank correlation (r) coefficients were used for comparing the three methods as appropriate. Conditional logistic regression was used to estimate the risk for breast cancer (odds ratios and 95% confidence intervals) associated with the quartiles of percent breast density (automated breast density method, Cumulus method) or Breast Imaging Reporting and Data System categories. The area under the receiver operator characteristic curve was estimated and used to compare the discriminatory capabilities of each approach. The continuous measures (automated breast density method and Cumulus method) were highly correlated with each other (R = 0.70) but less with Breast Imaging Reporting and Data System (r = 0.49 for automated breast density method and r = 0.57 for Cumulus method). Risk estimates associated with the lowest to highest quartiles of automated breast density method were greater in magnitude [odds ratios: 1.0 (reference), 2.3, 3.0, 5.2; P trend < 0.001] than the corresponding quartiles for the Cumulus method [odds ratios: 1.0 (reference), 1.7, 2.1, and 3.8; P trend < 0.001] and Breast Imaging Reporting and Data System [odds ratios: 1.0 (reference), 1.6, 1.5, 2.6; P trend < 0.001] method. However, all methods similarly discriminated between case and control status; areas under the receiver operator characteristic curve were 0.64, 0.63, and 0.61 for automated breast density method, Cumulus method, and Breast Imaging Reporting and Data System, respectively. The automated breast density method is a viable option for quantitatively assessing breast density from digitized film mammograms.

An automatic correction method for the heel effect in digitized mammography images

The most significant radiation field nonuniformity is the well-known Heel effect. This nonuniform beam effect has a negative influence on the results of computer-aided diagnosis of mammograms, which is frequently used for early cancer detection. This paper presents a method to correct all pixels in the mammography image according to the excess or lack on radiation to which these have been submitted as a result of the this effect. The current simulation method calculates the intensities at all points of the image plane. In the simulated image, the percentage of radiation received by all the points takes the center of the field as reference. In the digitized mammography, the percentages of the optical density of all the pixels of the analyzed image are also calculated. The Heel effect causes a Gaussian distribution around the anode-cathode axis and a logarithmic distribution parallel to this axis. Those characteristic distributions are used to determine the center of the radiation field as well as the cathode-anode axis, allowing for the automatic determination of the correlation between these two sets of data. The measurements obtained with our proposed method differs on average by 2.49 mm in the direction perpendicular to the anode-cathode axis and 2.02 mm parallel to the anode-cathode axis of commercial equipment. The method eliminates around 94% of the Heel effect in the radiological image and the objects will reflect their x-ray absorption. To evaluate this method, experimental data was taken from known objects, but could also be done with clinical and digital images.

Effect of adaptive-neighborhood contrast enhancement on the extraction of the breast skin-line in mammograms

Extraction of the breast skin-line is crucial in computeraided analysis of mammograms. This paper presents an analysis of the effect of adaptive-neighborhood contrast enhancement (ANCE) [1] on skin-line extraction. ANCE is used to enhance the parenchyma of the breast and suppress the background noise. Suppression of the background noise can improve skin-line extraction. Our skin-line extraction method is based on the work by Ojala et al. [2]. We use the Hausdorff distance [3, 4] for quantitative comparison of the skin-lines. Our work shows that ANCE improves the skin-line extraction due to its ability of suppressing noise while improving the contrast. We have defined an improvement factor based on the Hausdorff distance. The metric allows us to spot automatically the mammograms with significant improvement in the detection of the skin-line because of ANCE. We tested 83 images from the MIAS database [5], with the ground-truth skin-lines hand-drawn by a radiologist [6]. The average Hausdorff distance improvement with ANCE was 11 pixels (2.2 mm).

Gabor filters and phase portraits for the detection of architectural distortion in mammograms

Architectural distortion is a subtle abnormality in mammograms, and a source of overlooking errors by radiologists. Computer-aided diagnosis (CAD) techniques can improve the performance of radiologists in detecting masses and calcifications; however, most CAD systems have not been designed to detect architectural distortion. We present a new method to detect and localise architectural distortion by analysing the oriented texture in mammograms. A bank of Gabor filters is used to obtain the orientation field of the given mammogram. The curvilinear structures (CLS) of interest (spicules and fibrous tissue) are separated from confounding structures (pectoral muscle edge, parenchymal tissue edges, breast boundary, and noise). The selected core CLS pixels and the orientation field are filtered and downsampled, to reduce noise and also to reduce the computational effort required by the subsequent methods. The downsampled orientation field is analysed to produce three phase portrait maps: node, saddle, and spiral. The node map is further analysed in order to detect the sites of architectural distortion. The method was tested with 19 mammograms containing architectural distortion. In a preliminary experiment, a sensitivity of 84% was obtained at 7.8 false positives per image.

Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling

The paper presents a technique for the segmentation of the fibro-glandular disc in mammograms based upon a statistical model of breast density. The density function of the model was represented by a mixture of up to four weighted Gaussians, each one corresponding to a specific density class in the breast. The parameters of the model and the number of tissue classes in the breast were determined using the expectation-maximisation algorithm and the minimum description length method. Grey-level statistics of the pectoral muscle were used to determine the tissue categories that are likely to represent the fibro-glandular disc. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS database. The results of the segmented fibro-glandular disc were assessed by a radiologist using the original and the segmented images, with reference to a ranking table categorising the results of segmentation as: 1: excellent; 2: good; 3: average; 4: poor; and 5: complete failure. Of the 84 cases analysed, 64.3% were rated as excellent, 16.7% were rated as good, 10.7% were rated as average, and 4.7% were rated as poor; only 3.6% of the cases were rated as a complete failure with regard to segmentation of the fibro-glandular disc.