Isfahan University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share this content! On (X network) By
Level Set Method for Segmentation of Infrared Breast Thermograms



Golestani N1 ; Etehadtavakol M2 ; Ng EYK3
Authors
Show Affiliations
Authors Affiliations
  1. 1. Electrical and Computer Engineering Department, Isfahan University of Technology, Isfahan, 84154, Iran
  2. 2. Medical Image and Signal Processing Research Centre, Isfahan University of Medical Sciences, Isfahan 81745-319, Iran
  3. 3. School of Mechanical and Aerospace Engineering, College of Engineering, Nanyang Technological University, Singapore 639798, 50 Nanyang Avenue, Singapore

Source: EXCLI Journal Published:2014

Abstract

Breast thermography is a physiological test that provides information based on the tempera-ture changes in breast. It records the temperature distribution of a body using the infrared ra-diation emitted by the surface of that body. Precancerous tissue and the area around a cancer-ous tumor have higher temperature due to angiogenesis, and higher chemical and blood vessel activity than a normal breast; hence breast thermography has potential to detect early abnor-mal changes in breast tissues. It can detect the first sign of forming up cancer before mam-mography can detect. The thermal information can be shown in a pseudo colored image where each color represents a specific range of temperature. Various methods can be applied to extract hot regions for detecting suspected regions of interests in the breast infrared images and potentially suspicious tissues. Image segmentation techniques can play an important role to segment and extract these regions in the breast infrared images. Shape, size and borders of the hottest regions of the images can help to determine features which are used to detect ab-normalities. In this paper, three image segmentation methods: k-means, fuzzy c-means and level set are discussed and compared. These three methods are tested for different cases such as fibrocystic, inflammatory cancer cases. The hottest regions of thermal breast images in all cases are extracted and compared to the original images. According to the results, level set method is a more accurate approach and has potential to extract almost exact shape of tumors.
Related Docs
View other Related Docs
1. Improved Extraction of the Extreme Thermal Regions of Breast Ir Images, Advanced Methods in Biomedical Signal Processing and Analysis (2022)
2. Breast Cancer Detection From Thermal Images Using Bispectral Invariant Features, International Journal of Thermal Sciences (2013)
3. Automatic Color Segmentation of Breast Infrared Images Using a Gaussian Mixture Model, Optik (2015)
4. Separable and Non-Separable Discrete Wavelet Transform Based Texture Features and Image Classification of Breast Thermograms, Infrared Physics and Technology (2013)
5. Breast Thermography As a Potential Non-Contact Method in the Early Detection of Cancer: A Review, Journal of Mechanics in Medicine and Biology (2013)
6. Designing and Comparing Different Color Map Algorithms for Pseudo-Coloring Breast Thermograms, Journal of Medical Imaging and Health Informatics (2013)
7. Rapid Extraction of the Hottest or Coldest Regions of Medical Thermographic Images, Medical and Biological Engineering and Computing (2019)
8. Analysis of Breast Thermography Using Fractal Dimension to Establish Possible Difference Between Malignant and Benign Patterns, Journal of Healthcare Engineering (2010)
Experts (# of related papers)
View all Related Experts
Hossein Rabbani (2)
Alireza Mehri-Dehnavi (1)
Other Related Docs
9. Automatic Segmentation of Thermal Images of Diabetic-At-Risk Feet Using the Snakes Algorithm, Infrared Physics and Technology (2017)
10. Barrett′S Mucosa Segmentation in Endoscopic Images Using a Hybrid Method: Spatial Fuzzy C-Mean and Level Set, Journal of Medical Signals and Sensors (2016)
11. Nonlinear Analysis Using Lyapunov Exponents in Breast Thermograms to Identify Abnormal Lesions, Infrared Physics and Technology (2012)
12. Radiomics Feature Selection From Thyroid Thermal Images to Improve Thyroid Nodules Interpretations, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2023)
13. Survey of Numerical Bioheat Transfer Modelling for Accurate Skin Surface Measurements, Thermal Science and Engineering Progress (2020)
14. Ensemble Supervised Classification Method Using the Regions of Interest and Grey Level Co-Occurrence Matrices Features for Mammograms Data, Iranian Journal of Radiology (2015)
15. Enhanced Thyroid Nodule Segmentation Through U-Net and Vgg16 Fusion With Feature Engineering: A Comprehensive Study, Computer Methods and Programs in Biomedicine (2024)
16. Using Shape Contexts Method for Registration of Contra Lateral Breasts in Thermal Images, World Journal of Clinical Oncology (2014)
17. Estimating the Mutual Information Between Bilateral Breast in Thermograms Using Nonparametric Windows, Journal of Medical Systems (2011)
18. Diagnostic Accuracy of Two Methods of Breast Light and Thermography in Breast Cancer Screening: A Review Study, Iranian Journal of Obstetrics, Gynecology and Infertility (2021)