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Early diagnosis of breast cancer can increase survival rate. The assessment process for breast screening follows a triple assessment model: appropriate imaging, clinical assessment and biopsy. Retrieving prior cases with similar cancer symptoms could be used to circumvent incompatibilities in breast cancer grading. Abnormal mass lesions in breast are often co-located with normal tissue, which makes it difficult to describe the whole image with a single binary code. Therefore, we propose an AI-based method to describe mass lesions in semantic abstracts/codes. These codes are used in a searching based method to retrieve similar cases in the archive. This simple and effective network is used for unifying classification and retrieval in a single learning process, while enforcing similar lesion types to have similar semantic codes in a compact form. An advantage of this approach is its scalability to large-scale image retrievals.

This paper presents a novel variant of Local Binary Patterns (LBP) so called Mean-Elliptical Local Binary Patterns (M-ELBP) for breast density classification. The basic idea of (E)LBP is that an image texture consists of micropatterns and the histogram of these micropatterns could give information about the local features in an image. To include intensity based information along with texture features, we improve the ELBP descriptor by including the mean intensity around the central pixel. We use multiple orientations which facilitates capturing sufficient micropattern features from the mammographic images. The M-ELBP is computed for the fibroglandular disk area instead of the whole breast region. A Bayesian network classifier was used for performing mammogram density classification and a stratified ten-fold cross-validation scheme was used for performance evaluation of the proposed method. The proposed method achieved an accuracy of 74% which is comparable with other methods in the literature using the same dataset.

Mammography is considered the most appropriate method to defeat breast cancer in population screening. Although mammography is used for the detection of micro-calcifications, the benign/malignant classification relies on subsequent histology assessment. We describe a novel method for the automatic classification of benign and malignant micro-calcifications directly from mammographic images, which uses the morphology as well as the distribution aspects of micro-calcifications and is directly associated with the BIRADS categorization of microcalcifications. The developed approach is building on Iwanowski’s morphology work, but is adding class-extractors and multi-scale aspects. Translation to other application areas is discussed and evaluation based on the MIAS and DDSM datasets show results in line with stateof-the-art micro-calcifications classification approaches.

Classification of benign and malignant masses in mammograms is a challenging problem. It has wide applications in the development of Computer Aided Diagnosis (CAD) systems, however many challenges still need to be addressed. Due to the risk associated with segmenting the mass region, focus is shifting from selecting the features just from the mass area, to the whole Region of Interest (RoI) containing that mass. Bag of Visual Words (BoVW) techniques are gaining attention for classification tasks in medical imaging by considering RoI as a set of local features. In general BoVW aims to construct a global descriptor based on the extracted local features. In this work, we investigate the performance of BoVW for the classification of benign and malignant mammographic masses. Several features have been explored as the local features and different methods are applied for building the code-book. Subsequently we propose a voting-based approach to encode the features. The proposed approach is evaluated on a subset of DDSM dataset. Initial results reveal classification accuracy as high as 87% and Area Under the Curve (AUC) as 0.93, which are better than the current state-of-the-art approaches applied to the same problem.

Existing methods for automated breast ultrasound lesions detection and recognition tend to be based on multi-stage processing, such as preprocessing, filtering/denoising, segmentation and classification. The performance of these processes is dependent on the prior stages. To improve the current state of the art, we have proposed an end-to-end breast ultrasound lesions detection and recognition using a deep learning approach. We implemented a popular semantic segmentation framework, i.e. Fully Convolutional Network (FCN-AlexNet) for our experiment. To overcome data deficiency, we used a pre-trained model based on ImageNet and transfer learning. We validated our results on two datasets, which consist of a total of 113 malignant and 356 benign lesions. We assessed the performance of the model using the following split: 70p for training data, 10p for validation data, and 20p testing data. The results show that our proposed method performed better on benign lesions, with a Dice score of 0.6879, when compared to the malignant lesions with a Dice score of 0.5525. When considering the number of images with Dice score > 0.5, 79p of the benign lesions were successfully segmented and correctly recognised, while 65p of the malignant lesions were successfully segmented and correctly recognised. This paper provides the first end-to-end solution for breast ultrasound lesion recognition. The future challenges for the proposed approaches are to obtain additional datasets and customize the deep learning framework to improve the accuracy of this method.

Breast cancer is the most frequently diagnosed cancer in women. Early detection, precise identi cation of womenat risk, and application of appropriate disease prevention measures are by far the most e ective ways to overcomethe disease. Successful mammographic density segmentation is a key aspect in deriving correct tissue composition,ensuring an accurate mammographic risk assessment. However, mammographic densities have not yet been fullyincorporated with non-image based risk prediction models, ( e.g. the Gail and the Tyrer-Cuzick model), becauseof unreliable segmentation consistency and accuracy. This paper presents a novel multiresolution mammographicdensity segmentation, a concept of stack representation is proposed, and 3D texture features were extracted byadapting techniques based on classic 2D rst-order statistics. An unsupervised clustering technique was employedto achieve mammographic segmentation, in which two improvements were made; 1) consistent segmentation byincorporating an optimal centroids initialisation step, and 2) signi cantly reduced the number of missegmentationby using an adaptive cluster merging technique. A set of full eld digital mammograms was used in the evaluation.Visual assessment indicated substantial improvement on segmented anatomical structures and tissue speci careas, especially in low mammographic density categories. The developed method demonstrated an ability toimprove the quality of mammographic segmentation via clustering, and results indicated an improvement of 26%in segmented image with good quality when compared with the standard clustering approach. This in turn canbe found useful in early breast cancer detection, risk-strati ed screening, and aiding radiologists in the processof decision making prior to surgery and/or treatment.Keywords: BI-RADS, computer aided mammography, mammographic density segmentation

Various registration approaches have been adopted to correlate areas in mammograms and detect differences. These differences are likely to be associated with the development of an abnormality. Most of these methods rely on corresponding landmarks between images. Obtaining these landmarks turns out to be the most difficult task and plays an important role in the alignment accuracy. Manual landmark generation is a tedious and time-consuming task when the number of landmarks is large and, moreover, introduces variability in placing the landmarks (two consecutive experiments with the same images could have different landmarks and errors). Therefore, a large number of these methods are based on automatically extracting and corresponding landmarks from mammographic images. The landmarks include breast boundary, pectoral muscle, salient regions extracted using wavelets, isointensity contours or steerable filters, and crossing points of horizontal and vertical structures. This chapter presents an approach to extract correspondence in mammographic images based on anatomical features that appear as linear structures (i.e., fibrous tissue, ducts, and vessels). Correspondence is used here in two different applications: for mammographic image registration and for tracking linear structures. Registration is applied to temporal and contralateral mammograms of the same woman. A framework for the detection of abnormal structures based on registration is developed, and some initial promising results are given. Tracking of linear structures (see Section 14.6) is performed for the same breast over several screening rounds. This could be used to assess and model the development of architectural changes and abnormal structures. By being able to track regions back in time, the available information helps to improve early detection of subtle abnormalities that are initially missed by radiologists. One could argue that contralateral registration based on the linear structures in the left and right breasts should not be performed because linear structures present large differences. However, it is generally accepted that overall duct, vessel, and tissue structure shows similarities between left and right breasts of the same woman. Figure 14.1 illustrates those similarities in temporal and contralateral images. The aim is to use these similarities to guide the registration process.

Spoiled gradient echo volume MR scans were obtained from 5 growth hormone (GH) patients and 6 normal controls. The patients were scanned before treatment and after 3 and 6 months of GH therapy. The controls were scanned at similar intervals. A calibration phantom was scanned on the same day as each subject. The phantom images were registered with a 9 degree of freedom algorithm to measure scaling errors due to changes in scanner calibration. The second and third images were each registered with a 6 degree of freedom algorithm to the first (baseline) image by maximizing normalized mutual information, and transformed, with and without scaling error correction, using sinc interpolation. Each registered and transformed image had the baseline image subtracted to generate a difference image. Two neuro-radiologists were trained to detect structural change with difference images containing synthetic misregistration and scale changes. They carried out a blinded assessment of anatomical change for the unregistered; aligned and subtracted; and scale corrected, aligned and subtracted images. The results show a significant improvement in the detection of structural change and inter-observer agreement when aligned and subtracted images were used instead of unregistered ones. The structural change corresponded to an increase in brain: CSF ratio.

In this paper we present a new approach for the non-rigid registration of contrast-enhanced breast MRI. A hierarchical transformation model of the motion of the breast has been developed. The global motion of the breast is modelled by an affine transformation while the local breast motion is described by a free-form deformation based on B-splines. Normalized mutual information is used as a voxel-based similarity measure which is insensitive to intensity changes as the result of the contrast enhancement. Registration is achieved by minimizing a cost function which represents a combination of the cost associated with the smoothness of the transformation and the cost associated with the image similarity. The algorithm has been applied to the fully automated registration of 3D breast MRI in volunteers and patients. In particular, we have compared the results of the proposed non-rigid registration algorithm to those obtained using rigid and affine registration techniques. The results clearly indicate that the non-rigid registration algorithm is much better able to recover the motion and deformation of the breast than rigid or affine registration algorithms.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.