Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system

Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as hard cases from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15% true positive (TP) MCCs under 1.85% of false positives (FP), whereas the SUSAN/NN method achieves 94.44% of TP at the cost of 1.85% for FP. The comparison of these two methods suggests WHMT/WML for the computer aided diagnostic prompting. It also certifies the low false positive rates for both methods, meaning less biopsy tests per patient

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Computer-aided diagnosis in mammography : correlation of regions in multiple standard mammographic views of the same breast.

Thesis (Ph.D.)-University of KwaZulu-Natal, 2006.Abstract available in PDF file

The 1993 Space and Earth Science Data Compression Workshop

The Earth Observing System Data and Information System (EOSDIS) is described in terms of its data volume, data rate, and data distribution requirements. Opportunities for data compression in EOSDIS are discussed

Interstitial diagnosis and treatment of breast tumours

This thesis exploits the interaction of light with breast tissue for diagnosis and therapy. Optical biopsy is an experimental technique, based on Elastic Scattering Spectroscopy (ESS), being developed for characterising breast tissue. An optical probe interrogates tissue with a white light pulse, with spectral analysis of the reflected light. 264 spectral measurements (50 patients) were obtained from a range of breast tissues and axillary lymph nodes and correlated with conventional histology of biopsies from the same sites. Algorithms for spectral analysis were developed using ANN (Artificial Neural Network), HCA (Hierarchical Cluster Analysis) and MBA (Model Based Analysis). The sensitivity and specificity for cancer detection in breast and lymph nodes were: [diagram]. Interstitial Laser Photocoagulation (ILP) involves image guided, thermal coagulation of lesions within the breast using laser energy delivered via optical fibres positioned percutaneously under local anaesthetic. Two groups were studied: 1) Nineteen patients with benign fibroadenomas underwent ILP and the results compared with 11 treated conservatively. Thirteen ILP patients (14 fibroadenomas) and 6 controls (11 fibroadenomas) have reached their one-year review: [diagram]. These differences are statistically significant (P<0.001). 2)Six patients with primary breast cancers underwent ILP (with pre- and post-ILP contrast enhanced MRI) within 3 weeks of diagnosis and were then treated with Tamoxifen. Four underwent surgery at 3 months, two showing complete tumour ablation. MRI was reasonably accurate at detecting residual tumour. In conclusion: a) optical biopsy is a promising 'real time' diagnostic tool for breast disease. b) ILP could provide a simple and safe alternative to surgery for fibroadenomas. c) ILP with MRI monitoring may be an alternative to surgery in the management of some patients with localised primary breast cance