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Thermal Infrared (TIR) imaging of breasts involves a non-invasive, non-ionized, passive, safe and painless scan of the breasts. It is a graphing of the changes in breasts skin temperature using thermography. Thermograms are temperature distribution patterns with different colors to indicate temperature of the different regions within the tested breast, each color refers to a certain temperature range. In this work, three breast thermography images: one for normal case and two for cancerous cases, were employed to test the performance of the proposed segmentation methods: Region growing; clustering (K-means and FCM) algorithms and Histogram based enhancement technique to segment, detect and isolate the suspicious abnormal regions. These techniques were performed with the aid of suitable morphological operations to get the refined regions of interest. The results proved the efficiency of the proposed techniques to extract the abnormal (of high temperature) regions.

Utilizing image processing techniques for detecting breast abnormalities in thermography images