Breast cancer is the second leading cause of cancer death among women preceded by
cervix cancer. It has been reported that at the early stage of detection there is 85% chance of getting
cured, whereas only 10% chance at later stage diagnosis. The current screening modalities are
expensive, they have intricate imaging measures and they are unhealthy due to radiation exposure.
Therefore, a screening tool that is non-invasive, has no connection with the body, free from radiation,
such as Medical Thermography is necessary. It is reported that the sensitivity and specificity of
medical thermography are high largely in dense breast tissues. The clinical interpretation primarily
depends on the asymmetrical analysis of these thermograms subjectively. The appearance of an
asymmetric thermal image may indicate the pathological conditions. For earlier detection of breast
cancer, it is essential to identify the advanced methods in image processing techniques which enhance
the significance of diagnostics. In that analysis, the required breast region is unglued from the
background image. The segmented image is separated into symmetrical left and right breast tissues.
The statistical and histogram features extracted from both regions are used to identify the abnormal
thermograms using machine learning algorithms. From literature, it is reported that the thermal
images are inherently low contrast images and have low single to noise ratio. Moreover, they are
amorphous in nature and no clear edges are seen. The difficulty lies in the detection of lower breast
boundaries and inframammary folds. So, in general, the first attempt is made in improving the signal
to noise ratio and contrast of the image which helps to extract the true regions of breast tissues.
Then, asymmetry analysis of the normal and abnormal breast tissues is performed using different
techniques. This work demonstrates the review of a few image processing methods or the development
which are elaborated in the detection of breast cancer from thermal images.