Jelena Bozek

A Survey of Image Processing Algorithms in Digital Mammography

Mammography is at present the best available technique for early detection of breast cancer. The most common breast abnormalities that may indicate breast cancer are masses and calcifications. In some cases, subtle signs that can also lead to a breast cancer diagnosis, such as architectural distortion and bilateral asymmetry, are present. Breast abnormalities are defined with wide range of features and may be easily missed or misinterpreted by radiologists while reading large amount of mammographic images provided in screening programs. To help radiologists provide an accurate diagnosis, a computer-aided detection (CADe) and computer-aided diagnosis (CADx) algorithms are being developed. CADe and CADx algorithms help reducing the number of false positives and they assist radiologists in deciding between follow up and biopsy. This chapter gives a survey of image processing algorithms that have been developed for detection of masses and calcifications. An overview of algorithms in each step (segmentation step, feature extraction step, feature selection step, classification step) of the mass detection algorithms is given. Wavelet detection methods and other recently proposed methods for calcification detection are presented. An overview of contrast enhancement and noise equalization methods is given as well as an overview of calcification classification algorithms.

Breast border extraction and pectoral muscle detection using wavelet decomposition

Digital mammography is used more and more each day in comparison with screen film mammography (SFM). Main advantage of digital mammography for image processing is the use of images with few or no artifacts that can occur on SFM images. Finding breast border contour is therefore easier and gives more precise results. On the other hand, detection of pectoral muscle and breast abnormalities has almost the same results in both cases. The presence of pectoral muscle can affect results of lesion detection algorithms so it is recommended to have it removed from the image. Detection and segmentation of pectoral muscle can also help in image registration for further analysis of breast abnormalities such as bilateral asymmetry. Algorithm presented in this paper uses hybrid method for the pectoral muscle detection. Proposed method uses bit depth reduction and wavelet decomposition for finding pectoral muscle border. Algorithm has been tested on the set of 40 digital mammography images.

The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.

Multimodal surface matching with higher-order smoothness constraints.

&NA; In brain imaging, accurate alignment of cortical surfaces is fundamental to the statistical sensitivity and spatial localisation of group studies, and cortical surface‐based alignment has generally been accepted to be superior to volume‐based approaches at aligning cortical areas. However, human subjects have considerable variation in cortical folding, and in the location of functional areas relative to these folds. This makes alignment of cortical areas a challenging problem. The Multimodal Surface Matching (MSM) tool is a flexible, spherical registration approach that enables accurate registration of surfaces based on a variety of different features. Using MSM, we have previously shown that driving cross‐subject surface alignment, using areal features, such as resting state‐networks and myelin maps, improves group task fMRI statistics and map sharpness. However, the initial implementation of MSMs regularisation function did not penalize all forms of surface distortion evenly. In some cases, this allowed peak distortions to exceed neurobiologically plausible limits, unless regularisation strength was increased to a level which prevented the algorithm from fully maximizing surface alignment. Here we propose and implement a new regularisation penalty, derived from physically relevant equations of strain (deformation) energy, and demonstrate that its use leads to improved and more robust alignment of multimodal imaging data. In addition, since spherical warps incorporate projection distortions that are unavoidable when mapping from a convoluted cortical surface to the sphere, we also propose constraints that enforce smooth deformation of cortical anatomies. We test the impact of this approach for longitudinal modelling of cortical development for neonates (born between 31 and 43 weeks of post‐menstrual age) and demonstrate that the proposed method increases the biological interpretability of the distortion fields and improves the statistical significance of population‐based analysis relative to other spherical methods. HighlightsAdvances the Multimodal Surface Matching (MSM) method, for cortical surface registration of cortical surfaces, by improving control over the smoothness of the deformation.Enhances alignment of multimodal features, including the feature set used for the Human Connectome Projects parcellation of the human cerebral cortex.Also allows statistical modelling of longitudinal patterns of cortical growth.

Bilateral Asymmetry Detection in Digital Mammography Using B-Spline Interpolation

Digital mammography allows detection of breast abnormalities in early stage of breast cancer development. One of the abnormalities that may indicate breast cancer in its early stage is bilateral asymmetry. This paper presents computer-aided detection algorithm for bilateral asymmetry that uses B-spline interpolation for breast alignment. Alignment of the right and left breast is important step in computer-aided detection algorithm in order to allow comparison of corresponding points in right and left breast. Differential analysis of breasts is based on simple subtraction technique. The results are highlighted with color in each image and presented on a computer monitor thereby indicating radiologist the regions that need to have a second look and be further investigated. We found that our approach can be a very useful tool for radiologists in detection and diagnosis of bilateral asymmetry.

Construction of a neonatal cortical surface atlas using multimodal surface matching

In this paper we propose a method for constructing a spatiotemporal cortical surface atlas of neonatal brains aged between 38 and 42 weeks of gestation at the time of scan. The method is based on a spherical registration approach: multimodal surface matching (MSM) registration, where cortical folding patterns were used to drive alignment. Cortical surfaces from 44 subjects were projected onto spheres and grouped into 5 weeks, with all surfaces within each group co-registered using pairwise MSM registration in order to avoid bias in the atlas towards any of the subjects. Finally, warps were projected to the anatomical surfaces to allow averaging of white matter surfaces, and folding metrics, in the template space. Our approach improves the sharpness of the templates from what can be achieved using affine alignment alone.

Construction of a neonatal cortical surface atlas using Multimodal Surface Matching in the Developing Human Connectome Project

&NA; We propose a method for constructing a spatio‐temporal cortical surface atlas of neonatal brains aged between 36 and 44 weeks of post‐menstrual age (PMA) at the time of scan. The data were acquired as part of the Developing Human Connectome Project (dHCP), and the constructed surface atlases are publicly available. The method is based on a spherical registration approach: Multimodal Surface Matching (MSM), using cortical folding for driving the alignment. Templates have been generated for the anatomical cortical surface and for the cortical feature maps: sulcal depth, curvature, thickness, T1w/T2w myelin maps and cortical regions. To achieve this, cortical surfaces from 270 infants were first projected onto the sphere. Templates were then generated in two stages: first, a reference space was initialised via affine alignment to a group average adult template. Following this, templates were iteratively refined through repeated alignment of individuals to the template space until the variability of the average feature sets converged. Finally, bias towards the adult reference was removed by applying the inverse of the average affine transformations on the template and de‐drifting the template. We used temporal adaptive kernel regression to produce age‐dependant atlases for 9 weeks (36–44 weeks PMA). The generated templates capture expected patterns of cortical development including an increase in gyrification as well as an increase in thickness and T1w/T2w myelination with increasing age. HighlightsCreation of spatio‐temporal cortical surface atlas of the developing brain (36‐44 weeks PMA).Atlas captures patterns of cortical development in the neonatal dHCP population.Includes surface features: sulcal depth, curvature, thickness, T1w/T2w myelin, cortical labels.

Comparison of lesion size using area and volume in full field digital mammograms

The size of a lesion is a feature often used in computer-aided detection systems for classification between benign and malignant lesions. However, size of a lesion presented by its area might not be as reliable as volume of a lesion. Volume is more independent of the view (CC or MLO) since it represents three dimensional information, whereas area refers only to the projection of a lesion on a two dimensional plane. Furthermore, volume might be better than area for comparing lesion size in two consecutive exams and for evaluating temporal change to distinguish benign and malignant lesions. We have used volumetric breast density estimation in digital mammograms to obtain thickness of dense tissue in regions of interest in order to compute volume of lesions. The dataset consisted of 382 mammogram pairs in CC and MLO views and 120 mammogram pairs for temporal analysis. The obtained correlation coefficients between the lesion size in the CC and MLO views were 0.70 (0.64-0.76) and 0.83 (0.79-0.86) for area and volume, respectively. Two-tailed z-test showed a significant difference between two correlation coefficients (p=0.0001). The usage of area and volume in temporal analysis of mammograms has been evaluated using ROC analysis. The obtained values of the area under the curve (AUC) were 0.73 and 0.75 for area and volume, respectively. Although a higher AUC value for volume was found, this difference was not significant (p=0.16).