Automatic detection of abnormalities in mammograms

A New Collaborative Classification Process for Microcalcification Detection Based on Graphs and Knowledge Propagation

In this paper, we propose a new collaborative process that aims to detect macrocalcifications from mammographic images while minimizing false negative detections. This process is made up of three main phases: suspicious area detection, candidate object identification, and collaborative classification. The main concept is to operate on the entire image divided into homogenous regions called superpixels which are used to identify both suspicious areas and candidate objects. The collaborative classification phase consists in making the initial results of different microcalcification detectors collaborate in order to produce a new common decision and reduce their initial disagreements. The detectors share the information about their detected objects and associated labels in order to refine their initial decisions based on those of the other collaborators. This refinement consists of iteratively updating the candidate object labels of each detector following local and contextual analyses based on prior knowledge about the links between super pixels and macrocalcifications. This process iteratively reduces the disagreement between different detectors and estimates local reliability terms for each super pixel. The final result is obtained by a conjunctive combination of the new detector decisions reached by the collaborative process. The proposed approach is evaluated on the publicly available INBreast dataset. Experimental results show the benefits gained in terms of improving microcalcification detection performances compared to existing detectors as well as ordinary fusion operators.

Current Applications and Future Impact of Machine Learning in Radiology

Recent advances and future perspectives of machine learning techniques offer promising applications in medical imaging. Machine learning has the potential to improve different steps of the radiology workflow including order scheduling and triage, clinical decision support systems, detection and interpretation of findings, postprocessing and dose estimation, examination quality control, and radiology reporting. In this article, the authors review examples of current applications of machine learning and artificial intelligence techniques in diagnostic radiology. In addition, the future impact and natural extension of these techniques in radiology practice are discussed.

A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN

BACKGROUND AND OBJECTIVES

Mammography analysis is an effective technology for early detection of breast cancer. Micro-calcification clusters (MCs) are a vital indicator of breast cancer, so detection of MCs plays an important role in computer aided detection (CAD) system, this paper proposes a new hybrid method to improve MCs detection rate in mammograms.

METHODS

The proposed method comprises three main steps: firstly, remove label and pectoral muscle adopting the largest connected region marking and region growing method, and enhance MCs using the combination of double top-hat transform and grayscale-adjustment function; secondly, remove noise and other interference information, and retain the significant information by modifying the contourlet coefficients using nonlinear function; thirdly, we use the non-linking simplified pulse-coupled neural network to detect MCs.

RESULTS

In our work, we choose 118 mammograms including 38 mammograms with micro-calcification clusters and 80 mammograms without micro-calcification to demonstrate our algorithm separately from two open and common database including the MIAS and JSMIT; and we achieve the higher specificity of 94.7%, sensitivity of 96.3%, AUC of 97.0%, accuracy of 95.8%, MCC of 90.4%, MCC-PS of 61.3% and CEI of 53.5%, these promising results clearly demonstrate that the proposed approach outperforms the current state-of-the-art algorithms. In addition, this method is verified on the 20 mammograms from the People's Hospital of Gansu Province, the detection results reveal that our method can accurately detect the calcifications in clinical application.

CONCLUSIONS

This proposed method is simple and fast, furthermore it can achieve high detection rate, it could be considered used in CAD systems to assist the physicians for breast cancer diagnosis in the future.

A Molecular View of Pathological Microcalcification in Breast Cancer

Breast microcalcification is a potential diagnostic indicator for non-palpable breast cancers. Microcalcification type I (calcium oxalate) is restricted to benign tissue, whereas type II (calcium hydroxyapatite) occurs both in benign as well as in malignant lesions. Microcalcification is a pathological complication of the mammary gland. Over the past few decades, much attention has been paid to exploit this property, which forms the basis for advances in diagnostic procedures and imaging techniques. The mechanism of its formation is still poorly understood. Hence, in this paper, we have attempted to address the molecular mechanism of microcalcification in breast cancer. The central theme of this communication is "how a subpopulation of heterogeneous breast tumor cells attains an osteoblast-like phenotype, and what activities drive the process of pathophysiological microcalcification, especially at the invasive or infiltrating front of breast tumors". The role of bone morphogenetic proteins (BMPs) and tumor associated macrophages (TAMs) along with epithelial to mesenchymal transition (EMT) in manipulating this pathological process has been highlighted. Therefore, this review offers a novel insight into the mechanism underlying the development of microcalcification in breast carcinomas.