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A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)
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A novel automated hot spot detection system for tracking metastasis in digital infrared thermal images (1)

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  • 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
  • 2. Introduction
    • Thermography is a non invasive screening technique.
    • Normal and Abnormal images cannot be compared – As metabolism varies from one person to another. Hence, Left and Right asymmetries of the same person can only compared.
    • Red and White regions – Hot spots
    • Green regions – Moderate temperature
    • Blue Regions – Cold regions
  • 3. Overview of Breast Cancer
    • The incidence of breast cancer in India is on the rise and is rapidly becoming the number one cancer in females pushing the cervical cancer to the second spot. The seriousness of the situation is apparent after going through recent data from Indian Council of Medical Research (ICMR). 
    • It is reported that one in 22 women in India is likely to suffer from breast cancer during her lifetime. The reason for the same can be traced to the fact that there is no definitive cause for the spread of breast cancer and there is lack of awareness about the disease in rural areas with minimal or lack of knowledge about the screening procedures and the treatment available.
  • 4.  
  • 5. Current status of breast cancer detection
    • As an adjunct tool in the in the breast health screening process, infrared imaging has a significant role to play. Owing to infrared imaging ’s unique ability to image the metabolic aspects of the breast, extremely early warning signals (up to 10 years before any other detection method) have been observed in long term studies.
    • It is for this reason that an abnormal infrared image is the single most important marker of high risk for existence of or future development of breast cancer.
    • This, combined with proven sensitivity, specificity and prognostic value of technology, places infrared imaging as one of the major front line methods of breast cancer screening.
  • 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.
  • 8.  
  • 9. Gray Scale Processing-Inference
    • The skewness plot of left and right sides is found to be laterally inverted for normal images indicating symmetry. This is not seen in abnormal images. The peak kurtosis value is limited to 14 in normal images and exceeds 16 in abnormal ones.
    • Since some amount of detail is lost in the gray scale conversion process, there is no pattern observed for distinguishing normal from abnormal. Hence, this process does not yield significant results.
  • 10. RGB Processing -Inference
    • Red, Green and Blue planes are alone not sufficient for analysis since various nuances in colour are inherent. Hence, the 3 plane segregation is extended to 16 plane segmentation as the images obtained have 16 different color shades indicating varying extent of metabolism.
  • 11. Temperature mapping
    • The temperature values - obtained from clinic and the color intensities - correlated to the temperature values.
    • R1=33.6 , R2=33.3 R3=32.7 R4=32.3
    • G1=32.2,G2=31.4,G3=30.6,G4=29.6
    • B1=28,B2=27.35,B3=26.4,B4=25.45,B6=23.5
    • W1=34, W2=33.8
    • Feature Extraction
    • For each of the 16 planes , kurtosis, skewness, root mean square error ratio, eigen values and entropy difference – computed
  • 12. Extraction of tumour portions-Output Gray scale extracted portion Red plane –Extracted portion of an Carcinoma image
  • 13. SVM Classification
    • They are a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis.
    • A support vector machine constructs a hyper plane or set of hyper planes in a high or infinite dimensional space, which can be used for classification.
  • 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%
  • 15. Feed Forward Classifier
    • The feed forward classifier is trained using 42 features which includes the entropy difference, maximum root mean square error ,Eigen values, maximum and minimum skewness and Kurtosis for the left and right regions of the image for each of the three corresponding red ,green and blue planes.
    • The classification rate was found to be 75 % using 8 training and testing samples each.
  • 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.
  • 17. Conclusion
    • Results indicate that skewness is not an effective parameter in distinguishing normal from abnormal images. Entropy difference between contra lateral sides is very efficient in this discrimination followed by kurtosis, Eigen values and root mean square error.
    • This work proposes an approach to diagnose breast cancer based on infrared images. Firstly, color segmentation into 16 distinct planes is done. This is mapped into its corresponding temperature matrix. Statistical Features are extracted from the resultant matrix for red, green and blue components respectively.
  • 18. Conclusion
    • A total of 42 features (14 per plane) are extracted.
    • Normal and Abnormal images cannot be compared owing to variations in age, size and extent of metabolism. Hence, left and right sides of the same person is compared and in-homogeneities are analyzed. The classification rate using SVM is 75% and the same using feed forward neural network is 66.67%. This approach will be effectual and feasible and would be of great practical value in diagnosing breast cancer at its early stage.
    •  
  • 19. Future Direction
    • Future work would include more number of features used, improving the sensitivity of this technique by using different types of classifiers. Correlation of the red, blue and green planes with physiological parameters of hormonal and nerve activity and venous blood flow. A standardized scale for color-temperature mapping – To be able to generalize the results.
  • 20. References
    • Xianwu Tang et al “Asymmetry Analysis of Breast Thermograms with Morphological Image Segmentation” .
    • J. Koay et al “Analysis of Breast Thermography with an Artificial Neural Network”.
    • E.Y.K. Ng et al “Advanced Technique in Breast Thermography Analysis”.
    • Mikhail S. Tarkov et al “Evaluation of a Thermogram Heterogeneity Based on the Wavelet Haar Transform”.
    • Gerald Schaefer et al “Breast Cancer Classification Using Statistical Features and Fuzzy Classification of Thermograms”.

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