Role of image thermography in early breast cancer detection- Past, present and future
Introduction
Cancer is the condition in which cells grow irregularly and affect other parts of the body. Some of the cancers found commonly are breast, prostrate, lung, skin and pancreas. Cancer results in large number of mortalities worldwide [1], [2]. The instances of growth of breast cancer amongst women in the age group of 30- 40 years, has increased significantly over the last few decades in India [3]. The most frequently diagnosed cancer in women is breast cancer. Rates of diagnosis of breast cancer have been increasing drastically every year in nearly every region of the world. Proper identification of breast abnormality prior to the beginning of a cancerous growth is the only effective way of reducing mortality due to breast cancer. This review paper presents a chronological review of some of the papers related to breast thermography, an adjunct imaging modality for early cancer diagnosis. The main objective of the review paper is to analyze the improvement in accuracy of thermogram classification based on the selection of segmentation techniques, feature selection, feature extraction and types of classifiers used. The work also highlights the limitations of breast thermography and suggests methods of improving the sensitivity.
The paper is organized in 7 sections. Section 2 briefly explains the methodology used in the review of literature. Section 3 introduces the concept of breast thermography and the need of such imaging technique. Subsection 3 presents some of the available database of breast thermograms. Section 4 outlines the parts of intelligent diagnosing systems used in early cancer detection. This section also highlights the importance of segmentation with the recent advances. Section 5 presents a detailed progress in feature extraction and classification techniques used. Section 6 briefly describes future needs in the field. Section 7 discusses the implications of the study. Section 8 presents the main conclusions and recommendations based on them.
Section snippets
Sources
Research articles presented in the review paper are obtained from IEEEXplore, PubMed Central, Semantic Scholar, ResearchGate, ScienceDirect and other journal databases. Some of the articles were searched by going through the references of the searched papers.
Keywords
Various possible combinations of keywords such as “Infrared Thermography”, “Segmentation”, “Intelligent Cancer Classification”, “Machine Learning”, “Feature Extraction” and “Numerical Simulation” have been used for the search of articles.
Inclusion and exclusion criteria
In
Breast thermography
There are many types of cancer that can originate in any part of the breast. Breast cancer begins either in milk carrying ducts or the milk producing lobules of the breast region. The detailed structure of female breast is shown in Fig. 1.
Proper identification of breast abnormality prior to the beginning of a cancerous growth is the only effective way of reducing mortality due to breast cancer. Various imaging modalities have been developed to detect prior symptoms of breast cancer. Apart from
Machine learning based system for analysis of breast thermogram
Usage of intelligent Computer based system for analyzing the breast thermograms has emerged as an effective tool for providing the support to the radiologists. The process involves preprocessing of thermogram, extraction of region of interest or segmentation, extraction of features and classification. Fig. 4 illustrates the overall methodology involved in Cancer Diagnosis system used for thermographic images.
Breast thermograms obtained from a thermal camera may consist of some labels which are
Emerging trends in extraction of significant features and classification
The main process in Medical image processing is Feature Extraction. This involves choosing certain parameters, known as features that will be extracted from a breast thermogram, analyzing and comparing the features to obtain significant results. This will reduce complexity in classification and recognition of images. Feature extraction techniques help in overcoming some of the disadvantages of breast thermography such as (i) its inability in diagnosing small tumors, (ii) identify increase in
Future trends in breast thermography
Machine learning based system proves to be effective in classification and detection in the medical field, with necessary training skills, selection of more significant features and reduced false positives. Moreover, there is a future need to develop database with millions of thermographic images for improving the efficiency of classifiers. The future research work may also involve improving the efficiency of classifiers with the limited number of thermograms available. There is also the need
Discussion
This article attempts to present the key factors to improve the efficiency of existing thermography based diagnosis. Validation of segmentation techniques (some are enlisted in Table 1), by the physicians with ground–truth further improves the reliability of methods used. Performance of classifier is dependent on the choice of features to be extracted from a thermogram: statistical, textural, fractal or medically interpretable. Authors in the paper have used various feature selection methods to
Conclusion
The advent of computer-aided diagnostics in healthcare field has proved to be very effective in improving the role of thermography in detection of breast cancer. The study presents various segmentation, feature extraction and classification techniques used in breast thermography. High false positive and false negative values in thermography can be effectively reduced by suitable combinations of segmentation, feature extraction and classification techniques mentioned in the paper. Specific
Declaration of Competing Interest
The author(s) declare(s) that there is no conflict of interest regarding the publication of this paper.
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