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Early Thermographic Screening of Breast Abnormality in Women with Dense Breast by Thermal, Fractal, and Statistical Analysis

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Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery (MIABID 2022, AIIIMA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13602))

Abstract

Objectives: Breast cancer is the prime reason for cancer mortality in women across the globe and India. Early screening to identify breast abnormality before the actual symptoms are visible is the only effective way of reducing associated mortality and this can be accomplished by creating a large breast thermographic dataset with supporting clinical findings. Breast thermography, which is a non-invasive, painless, non-toxic, and cost-effective method, can potentially be used for early screening, especially to identify lesions in dense breasts. Methods: The current study is a single-center preliminary thermography-based study of 5 malignant and 7 benign female subjects that highlights the correlation of thermal, statistical, and fractal features obtained from thermograms (malignant and benign), with the clinical characteristics of the patients. Results: A comparison of the mean surface temperatures of 12 subjects shows that the contralateral breast temperature difference is more than 0.5 ℃ for malignant cases, whereas, for benign cases, the value is between 0 to 0.2 ℃. Also, the fractal dimension of the hot spot boundary in the malignant breast side is greater than the corresponding fractal dimension on the contralateral side. Conclusion: This preliminary analysis indicates that thermography can identify malignancy and suspicious benign cases requiring follow-up after screening to reduce the chances of breast cancer progression.

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Acknowledgment

The authors would sincerely like to thank the female subjects who gave consent for the present study from Kriti Scanning Centre and Kamla Nehru Memorial Hospital, Prayagaraj, India. The authors also want to thank Prof Krishna Mishra, FNASc for her invaluable scientific counsel regarding the study. The authors also acknowledge Dr. Kushagra Agrawal (Kriti Scanning center, Prayagraj, India) for related medical guidance on breast cancer patients. The authors would like to give special thanks to Dr. Debotosh Bhattacharjee and Dr. Usha Rani Gogoi for sharing the DBT-TU-JU Breast thermogram Database for correlation with the present study and valuable guidance. The authors would like to acknowledge Electronics and Communication Engineering Department, IIITA for funding, and Computer Vision and Biometrics Lab, IIITA, Prayagraj for giving access to Thermal camera E40_NR1.2 for the current study.

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Authors and Affiliations

  1. Electronics and Communication Engineering Department, Indian Institute of Information Technology Allahabad, Prayagraj, Uttar Pradesh, India

    Deepika Singh & Ashutosh Kumar Singh

  2. Radiation Oncology Department, Kamla Nehru Memorial Hospital and Regional Cancer Centre, Prayagraj, Uttar Pradesh, India

    Sonia Tiwari

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  1. Deepika Singh
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  2. Ashutosh Kumar Singh
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  3. Sonia Tiwari
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Correspondence to Deepika Singh .

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Editors and Affiliations

  1. Niramai Health Analytix Pvt. Ltd., Bengaluru, India

    Siva Teja Kakileti

  2. IBM Research - Zurich, Rüschlikon, Switzerland

    Maria Gabrani

  3. Niramai Health Analytix Pvt. Ltd., Bengaluru, India

    Geetha Manjunath

  4. University of Haifa, Haifa, Israel

    Michal Rosen-Zvi

  5. Picture Health, Cleveland, OH, USA

    Nathaniel Braman

  6. Piedmont Physical Medicine and Rehabilitation, Greenville, SC, USA

    Robert G. Schwartz

  7. University of Leeds, Leeds, UK

    Alejandro F. Frangi

  8. National Cheng Kung University, Tainan, Taiwan

    Pau-Choo Chung

  9. Cleveland Clinic, Cleveland, OH, USA

    Christopher Weight

  10. Tempus Labs, Menlo Park, CA, USA

    Vekataraman Jagadish

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Singh, D., Singh, A.K., Tiwari, S. (2022). Early Thermographic Screening of Breast Abnormality in Women with Dense Breast by Thermal, Fractal, and Statistical Analysis. In: Kakileti, S.T., et al. Artificial Intelligence over Infrared Images for Medical Applications and Medical Image Assisted Biomarker Discovery. MIABID AIIIMA 2022 2022. Lecture Notes in Computer Science, vol 13602. Springer, Cham. https://doi.org/10.1007/978-3-031-19660-7_3

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  • DOI: https://doi.org/10.1007/978-3-031-19660-7_3

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-19660-7

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