Background Breast cancer is the most common malignancy in women. It is often characterized by a lack of early symptoms, which results in late detection of the disease. Detection at advanced stages of the decease implies the treatment is more difficult and uncertain. The appropriate screening programs have been conducted within the organized preventive examinations and have made significant contributions to the early breast cancer detection. Objective It is necessary to improve the screening process in order to reduce the percentage of female population that is not covered by screening programs and increase the number of early-detected breast cancers. The improvement of the screening program may be reflected in the following: more efficient determination of the list of the women who have to undergo preventive examination, introduction of screening program in thermography as a diagnostic method applied in pre-screening stage, more efficient analysis of mammograms and continuous follow up of patients. Methods The identification of target population for breast cancer screening program has been based on the age of women. The improvement of the early breast cancer diagnosis process proposed in this paper is reflected in more efficient determination of the group of women who have to undergo preventive examination based on the factors affecting the occurrence of breast cancer. Inclusion of the pre-screening phase in which thermal imaging could be applied and software support to mammographic detection of tumor are suggested. Results This paper describes the breast cancer, current screening program and techniques for early-stage breast cancer detection, module of medical information system MEDIS.NET for creating screening list based on the analysis of risk factors affecting the occurrence of breast cancer, mammography and role of thermal imaging in the process of early breast cancer detection. It also presents an overview on important achievements in computer-aided detection and diagnosis of breast cancer in mammography and thermography. Conclusions Based on the obtained results, dynamics of preventive examinations for particular groups of women that is different from the standard two-year examinations, can be successfully defined. It can be concluded that the use of a computer system for tumor diagnosis in mammogram based on various methods of image processing can help doctors in decision-making, while the use of thermal imaging in the pre-screening phase would significantly reduce the list of women for screening mammograms.

Early diagnosis and detection of breast cancer.

Background & objectives: Non-invasive and non-ionizing medical imaging techniques are safe as these can be repeatedly used on as individual and are applicable across all age groups. Breast thermography is a non-invasive and non-ionizing medical imaging that can be potentially used in breast cancer detection and diagnosis. In this study, we used breast thermography to estimate the tumour contour from the breast skin surface temperature. Methods: We proposed a framework called infrared thermography based image construction (ITBIC) to estimate tumour parameters such as size and depth from cancerous breast skin surface temperature data. Markov Chain Monte Carlo method was used to enhance the accuracy of estimation in order to reflect clearly realistic situation. Results: We validated our method experimentally using Watermelon and Agar models. For the Watermelon experiment error in estimation of size and depth parameters was 1.5 and 3.8 per cent respectively. For the Agar model it was 0 and 8 per cent respectively. Further, thermal breast screening was done on female volunteers and compared it with the magnetic resonance imaging. The results were positive and encouraging. Interpretation & conclusions: ITBIC is computationally fast thermal imaging system and is perhaps affordable. Such a system will be useful for doctors or radiologists for breast cancer diagnosis.

Framework for estimating tumour parameters using thermal imaging

Integrated Internet of Things with cloud developed for data integrity problems on supply chain management

Estimation of tumor parameters using neural networks for inverse bioheat problem.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9811918

A novel technique for the detection of Covid-19 patients with the applications of three-way decisions using variance-based criterion

Study on image indexing and retrieval concepts have evolved the research further on Content Based Image Retrieval (CBIR). The CBIR system analyses the image content features such as shape, texture, and colour. In recent systems, these low level features are combined with high level semantics in order to achieve high performance. Analyzing the image content will increase the time of feature extraction and complexity of the retrieval process. The proposed system describes the content of an image region using Color and texture as visual features. An ontology model is constructed to analyze the contents. Content Analysis comprises image segmentation and annotation, which scrutinizes images by subdividing them into entities, selecting primary entities, and finally extracting feature signifiers possessed by these entities. To explore the mechanism of content analysis system, a new fused framework using content and model based annotations was proposed. The proposed integrated framework for CBIR system was experimented and the performance was assessed over a large image database with number of features, compared against traditional approaches from the literature. From the experimental results, it is evident that the proposed system performs substantially better and faster than the existing systems.

Ontology Based Image Retrieval by Utilizing Model Annotations and Content

Breast Thermography is one of the scanning techniques used for breast cancer detection. Looking at breast thermal image it is difficult to interpret parameters of tumor such as depth, size and location which are useful for diagnosis and treatment of breast cancer. In our previous work (ITBIC) we proposed a framework for estimation of tumor size using clever algorithms and the radiative heat transfer model. In this paper, we expand it to incorporate the more realistic Pennes bio-heat transfer model and Markov Chain Monte Carlo (MCMC) method, and analyze it’s performance in terms of computational speed, accuracy, robustness against noisy inputs, ability to make use of prior information and ability to estimate multiple parameters simultaneously. We discuss the influence of various parameters used in its implementation. We apply this method on clinical data and extract reliable results for the first time using breast thermography.

S. Suresh

Papers

Ontology Based Image Retrieval by Utilizing Model Annotations and Content

Improved infrared thermography based image construction for biomedical applications using Markov Chain monte carlo method