Computer aided detection of microcalcifications in digital mammograms

Computer-aided diagnosis of breast microcalcifications based on dual-tree complex wavelet transform

Background

Digital mammography is the most reliable imaging modality for breast carcinoma diagnosis and breast micro-calcifications is regarded as one of the most important signs on imaging diagnosis. In this paper, a computer-aided diagnosis (CAD) system is presented for breast micro-calcifications based on dual-tree complex wavelet transform (DT-CWT) to facilitate radiologists like double reading.

Methods

Firstly, 25 abnormal ROIs were extracted according to the center and diameter of the lesions manually and 25 normal ROIs were selected randomly. Then micro-calcifications were segmented by combining space and frequency domain techniques. We extracted three texture features based on wavelet (Haar, DB4, DT-CWT) transform. Totally 14 descriptors were introduced to define the characteristics of the suspicious micro-calcifications. Principal Component Analysis (PCA) was used to transform these descriptors to a compact and efficient vector expression. Support Vector Machine (SVM) classifier was used to classify potential micro-calcifications. Finally, we used the receiver operating characteristic (ROC) curve and free-response operating characteristic (FROC) curve to evaluate the performance of the CAD system.

Results

The results of SVM classifications based on different wavelets shows DT-CWT has a better performance. Compared with other results, DT-CWT method achieved an accuracy of 96% and 100% for the classification of normal and abnormal ROIs, and the classification of benign and malignant micro-calcifications respectively. In FROC analysis, our CAD system for clinical dataset detection achieved a sensitivity of 83.5% at a false positive per image of 1.85.

Conclusions

Compared with general wavelets, DT-CWT could describe the features more effectively, and our CAD system had a competitive performance.

A multiresolution diffused expectation-maximization algorithm for medical image segmentation

In this paper a new method for segmenting medical images is presented, the multiresolution diffused expectation-maximization (MDEM) algorithm. The algorithm operates within a multiscale framework, thus taking advantage of the fact that objects/regions to be segmented usually reside at different scales. At each scale segmentation is carried out via the expectation-maximization algorithm, coupled with anisotropic diffusion on classes, in order to account for the spatial dependencies among pixels. This new approach is validated via experiments on a variety of medical images and its performance is compared with more standard methods.

Digital Mammography: Current Capabilities and Obstacles

Digital mammography represents an exciting new technology for breast imaging and possibly breast screening. The decoupling of functional components in digital mammography translates into potential operational efficiencies compared with screen-film mammography (SFM). Digital mammography is a platform for advanced applications not possible with traditional SFM. However, for digital mammography to replace SFM in daily clinical practice, operational and clinical hurdles will have to be overcome.

Mammography and computerized decision systems: a review

Computer-aided diagnosis (CAD) systems in mammography have been developed and investigated for several years and recently they have left the research stage to enter the clinical stage. The purpose of this article is to review the present situation of mammography for breast cancer detection and the role played by CAD systems. Results from the recent literature show that CAD systems have the potential to improve the sensitivities of radiologists in the detection of malignant clustered microcalcifications and masses, while keeping specificities at acceptable levels. This leads to the conclusion that CAD systems can be incorporated into clinical practice as a double reading option to radiologists. However, some issues have yet to be tackled for CAD systems to gain better acceptance and more widespread use worldwide.

Advances in biomedical informatics for the management of cancer

Increased access to health care, and advances in education and technology have resulted in a larger proportion of the population having longer life expectancy. The strong correlation between age and cancer has resulted in a major healthcare problem for this century, and until recently cancer has defied any long-lasting cure. However, progress, especially in the field of biomedical informatics, promises a successful prediction and possibly a permanent cure for cancer within the next two decades. Biomedical informatics-with its roots in computer science, biomedical engineering, biostatistics, and mathematics-helps to bring the patient closer to the physician, facilitates access to specialist information and knowledge bases across the world, and makes it possible to identify genetic expression profiles for malignant or cancerous cells. This paper reviews the new research findings in biomedical informatics, working toward the ultimate goal of successfully predicting cancer, solving complex problems in prevention and treatment of cancer, and perhaps completely curing the scourge of cancer.