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Color segmentation of infrared thermal images is an important factor in detecting the tumor region. The cancerous tissue with angiogenesis and inflammation emits temperature pattern different from the healthy one. In this paper, two color segmentation techniques, K-means and fuzzy c-means for color segmentation of infrared (IR) breast images are modeled and compared. Using the K-means algorithm in Matlab, some empty clusters may appear in the results. Fuzzy c-means is preferred because the fuzzy nature of IR breast images helps the fuzzy c-means segmentation to provide more accurate results with no empty cluster. Since breasts with malignant tumors have higher temperature than healthy breasts and even breasts with benign tumors, in this study, we look for detecting the hottest regions of abnormal breasts which are the suspected regions. The effect of IR camera sensitivity on the number of clusters in segmentation is also investigated. When the camera is ultra sensitive the number of clusters being considered may be increased.

This review paper discusses recent research achievements in medical thermography with concerns about the possibility of early breast cancer detection. With the advancements in infrared (IR) technology, image processing methods, and the pathophysiological-based knowledge of thermograms, IR screening is sufficiently mature to be utilized as a first-line complement to both health managing and clinical prognosis. In addition, it explains the performance and environmental conditions in identifying thermography for breast tumor imaging under strict indoor controlled environmental circumstances. An irregular thermogram is indicated as a significant biological risk marker for the presence or growth of breast tumors. Breast thermography is completely non-contact, with no form of radiation and compression. It is useful for all women of all ages, for pregnant and breastfeeding women, for women with implants, for women with dense or fibrocystic breasts, for women on hormone replacement therapy, and for pre or post menopausal women. Breast thermography is specifically worthwhile during the early stages of fast tumor growth, which is not yet recognizable by mammography as thermography is a physiological test while mammography is an anatomical one. Often, physiological changes precede anatomical changes. This early detection of irregular tissue liveliness gives breast thermography the potential to be greatly useful and economical as an imaging program and provides the opportunity to apply non-invasive treatment to reform breast tissue activity. The non-radiating nature of thermography also permits repeated images. Thus, changes can be compared over time and the results of protective approaches can be observed to ensure utmost care of breast cells.

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Early detection of breast cancer by means of thermal imaging has a long and extremely controversial history. Recently, the availability of highly sensitive infrared (IR) cameras which can produce high-resolution diagnostic images of the temperature and vascular changes of breasts, as well as a better knowledge of advanced image processing techniques, has generated a renewed interest. The objective of this study is to investigate fractal analysis of breast thermal images and to develop an algorithm for detecting benignity and malignancy of breast diseases. The study is based on IR images captured by thermal camera, in which the resolution of the results is within the state of the art of IR camera. A total of 7 malignant cases and 8 benign cases have been considered. The breast images were first segmented by fuzzy c-means clustering. Then the first hottest regions for each image were identified and the fractal dimension of those regions was computed. It is shown that the fractal dimension results significantly differ between malignant and benign patterns, suggesting that fractal analysis may potentially improve the reliability of thermography in breast tumor detection.