1. The document presents a novel system for detecting hot spots in digital infrared thermal images to track breast cancer metastasis. It uses statistical feature extraction and classification methods like SVM and neural networks. 2. The system segments images into 16 color planes mapped to temperature values. It extracts 42 features from the red, green, and blue planes and classifies images with 75% accuracy using SVM and 66.67% using neural networks. 3. Future work includes improving classification accuracy by using more features and different classifiers as well as correlating temperature maps with physiological parameters to better detect breast cancer early.

1. A novel automated hot spot detection system for tracking metastasis in Digital Infrared Thermal Images Madhumitha Raghu Divya.N N.Sriraam B.Venkatraman P.Manoj Niranjan

6. Validation Carcinoma (Ca) Breast – Right Upper medial quadrant mass. Suggestive of Ca by USG and mammography and confirmed by Fine Needle Aspiration Cytology. Mass detected by mammography

7. Validation FNAC - Results Highly cellular smear with Pleumorphism increased nucleo cytoplasmic ratio and prominent nuclei which is suggestive of carcinoma.

12. Extraction of tumour portions-Output Gray scale extracted portion Red plane –Extracted portion of an Carcinoma image

14. Observation S.NO. FEATURES CLASSIFICATION PERCENTAGE 1. Entropy Difference between left and right sides of Red and Green Planes 77% 2. Entropy Difference between left and right sides of Red and Blue Planes 88.889% 3. Entropy Difference between left and right sides of Green and Blue Planes 83.33% 4. Red Plane – Maximum kurtosis of left and right sides 77.778% 5. Green Plane – Maximum kurtosis of left and right sides 77.778% 6. Red Plane – Maximum Eigen values of left and right sides 66.67% 7. Blue Plane – Maximum Eigen values of left and right sides 72.2%

16. Progress of the work Analysis Inference 1. Gray scale processing Elimination – As no significant trend observed 2. RGB color processing Work to be extended for all the 16 colors 3. 16 color segmentation following color temperature mapping As it involves temperature which co-relates with the body temperature, the feature matrix can be used as inputs to the classifier.