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In Computer methods and programs in biomedicine

One of the most prevalent cancers among women is the breast cancer. Accurate diagnosis of breast cancer at an early stage can reduce the mortality associated with this disease. Infrared Breast Thermography, which is a screening tool used to measure the temperature distribution of breast tissue, is a suitable adjunct tool to mammography. Breast thermography has many advantages as it is non-invasive, safe and painless. Thermographic image and usage of artificial neural networks have improved the accuracy of thermography in early diagnosis of breast abnormality. This paper presents survey based on the main steps of computer aided detection systems: image acquisition protocols, segmentation techniques, feature extraction and classification methods, used in the field of breast thermography over the past few decades. The detailed survey emphasizes on the improved reliability of breast thermography .This has become possible with the utilization of machine learning techniques for correct classification of breast thermograms. Numerical Simulation can be used as a supporting method to overcome high false positive rates in thermographic diagnosis. The paper also presents future recommendations to utilize recent machine learning advances in real time.

Singh Deepika, Singh Ashutosh Kumar


Classification, Feature extraction, Infrared breast thermography, Numerical simulation, Region of interest, Segmentation