Developing a Gaussian Model Based Histogram Equalization Technique for Enhancement of Breast Thermography Images
Abstract
Purpose: Breast cancer is one of the most prevalent diseases among women worldwide. One of the effective ways to reduce the risk of death from breast cancer is early detection by breast screening methods such as thermography. Thermography is non-invasive infrared imaging that detects early symptoms of breast angiogenesis based on the temperature difference and asymmetric patterns between left and right breasts. For better visual perception, it is essential to increase the medical image quality and contrast.
Materials and Methods: Histogram Equalization (HE) is a common and effective technique for contrast enhancement that uses the whole dynamic range of gray levels. In this paper, we propose to apply the equalization technique to the object part of the image rather than the background. One way is to use Otsu's method for automatic image thresholding. A more efficient approach to extract the body region is to fit a bimodal Gaussian distribution on the temperature information and restrict the equalization on gray level ranges corresponding to temperatures between the mean minus/plus three times of standard deviation.
Results: We compared the performance of the proposed approach with six conventional HE methods by using objective criteria, including Absolute Mean Brightness Error (AMBE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSI), and Entropy.
Conclusion: Based on objective measures, as well as subjective visual inspection of the results, the proposed Gaussian model-based HE has better performance in contrast enhancement and brightness preservation among other methods.
2- Anthony Muthu Arul Edwin Raj, Muniasamy Sundaram, and Thirassama Jaya, "Thermography based breast cancer detection using selfâadaptive gray level histogram equalization color enhancement method." International Journal of Imaging Systems and Technology, Vol. 31 (No. 2), pp. 854-73, (2021).
3- Lulu Wang, "Early diagnosis of breast cancer." Sensors, Vol. 17 (No. 7), p. 1572, (2017).
4- Siva Teja Kakileti, Geetha Manjunath, Himanshu Madhu, and Hadonahalli Venkataramanappa Ramprakash, "Advances in breast thermography." in IntechOpen, p. 91, (2017).
5- Deepika Singh and Ashutosh Kumar Singh, "Role of image thermography in early breast cancer detection-Past, present and future." Computer methods and programs in biomedicine, Vol. 183p. 105074, (2020).
6- Deborah A Kennedy, Tanya Lee, and Dugald Seely, "A comparative review of thermography as a breast cancer screening technique." Integrative cancer therapies, Vol. 8 (No. 1), pp. 9-16, (2009).
7- Monika Agarwal and Rashima Mahajan, "Medical image contrast enhancement using range limited weighted histogram equalization." Procedia Computer Science, Vol. 125pp. 149-56, (2018).
8- R Sumathi, M Venkatesulu, and Sridhar P Arjunan, "Segmenting and Classifying MRI Multimodal Images Using Cuckoo Search Optimization and KNN Classifier." IETE Journal of Research, pp. 1-8, (2021).
9- Haichao Cao, Shiliang Pu, Wenming Tan, and Junyan Tong, "Breast mass detection in digital mammography based on anchor-free architecture." Computer Methods and Programs in Biomedicine, Vol. 205p. 106033, (2021).
10- Jyoti Dabass and Manju Dabass, "Denoising, Edge Correction, and Enhancement of Breast Cancer Ultrasound Images." in Advances in Communication and Computational Technology: Springer, (2021), pp. 1153-72.
11- Saeed Kermani, Nasser Samadzadehaghdam, and Mahnaz EtehadTavakol, "Automatic color segmentation of breast infrared images using a Gaussian mixture model." Optik, Vol. 126 (No. 21), pp. 3288-94, (2015).
12- Nicholas A Diakides and Joseph D Bronzino, Medical infrared imaging. CRC press, (2007).
13- EFJ Ring and Kurt Ammer, "The technique of infrared imaging in medicine." Thermology international, Vol. 10 (No. 1), pp. 7-14, (2000).
14- Omprakash Patel, Yogendra PS Maravi, and Sanjeev Sharma, "A comparative study of histogram equalization based image enhancement techniques for brightness preservation and contrast enhancement." arXiv preprint arXiv:1311.4033, (2013).
15- Nicholas Sia Pik Kong, Haidi Ibrahim, and Seng Chun Hoo, "A literature review on histogram equalization and its variations for digital image enhancement." International Journal of Innovation, Management and Technology, Vol. 4 (No. 4), p. 386, (2013).
16- Yu Wang, Qian Chen, and Baeomin Zhang, "Image enhancement based on equal area dualistic sub-image histogram equalization method." IEEE Transactions on consumer Electronics, Vol. 45 (No. 1), pp. 68-75, (1999).
17- Yeong-Taeg Kim, "Contrast enhancement using brightness preserving bi-histogram equalization." IEEE Transactions on consumer Electronics, Vol. 43 (No. 1), pp. 1-8, (1997).
18- R Dale-Jones and Tardi Tjahjadi, "A study and modification of the local histogram equalization algorithm." Pattern Recognition, Vol. 26 (No. 9), pp. 1373-81, (1993).
19- Ali M Reza, "Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement." Journal of VLSI signal processing systems for signal, image and video technology, Vol. 38 (No. 1), pp. 35-44, (2004).
20- Shih-Chia Huang, Fan-Chieh Cheng, and Yi-Sheng Chiu, "Efficient contrast enhancement using adaptive gamma correction with weighting distribution." IEEE transactions on image processing, Vol. 22 (No. 3), pp. 1032-41, (2012).
21- Mary Kim and Min Gyo Chung, "Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement." IEEE Transactions on consumer Electronics, Vol. 54 (No. 3), pp. 1389-97, (2008).
22- Bruce G Lindsay, "Mixture models: theory, geometry and applications." in NSF-CBMS regional conference series in probability and statistics: JSTOR, pp. i-163, (1995).
| Files | ||
| Issue | Vol 9 No 3 (2022) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v9i3.9648 | |
| Keywords | ||
| Breast Thermography Histogram Equalization Gaussian Model Contrast Enhancement Objective Criteria | ||
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