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Intensity Inhomogeneity Correction in Brain MR Images Based on Filtering Method

Intensity Inhomogeneity Correction in Brain MR Images Based on Filtering Method

C. Helen Sulochana, S. A. Praylin Selva Blessy
Copyright: © 2020 |Pages: 15
ISBN13: 9781522599029|ISBN10: 1522599029|EISBN13: 9781522599043
DOI: 10.4018/978-1-5225-9902-9.ch006
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MLA

Sulochana, C. Helen, and S. A. Praylin Selva Blessy. "Intensity Inhomogeneity Correction in Brain MR Images Based on Filtering Method." Handbook of Research on Applications and Implementations of Machine Learning Techniques, edited by Sathiyamoorthi Velayutham, IGI Global, 2020, pp. 96-110. https://doi.org/10.4018/978-1-5225-9902-9.ch006

APA

Sulochana, C. H. & Blessy, S. A. (2020). Intensity Inhomogeneity Correction in Brain MR Images Based on Filtering Method. In S. Velayutham (Ed.), Handbook of Research on Applications and Implementations of Machine Learning Techniques (pp. 96-110). IGI Global. https://doi.org/10.4018/978-1-5225-9902-9.ch006

Chicago

Sulochana, C. Helen, and S. A. Praylin Selva Blessy. "Intensity Inhomogeneity Correction in Brain MR Images Based on Filtering Method." In Handbook of Research on Applications and Implementations of Machine Learning Techniques, edited by Sathiyamoorthi Velayutham, 96-110. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9902-9.ch006

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Abstract

Brain tumor is a mass of abnormal growth of cells in the brain which disturbs the normal functioning of the brain. MRI is a powerful diagnostic tool providing excellent soft tissue contrast and high spatial resolution. However, imperfections arising in the radio frequency field and scanner-related intensity artifacts in MRI produce intensity inhomogeneity. These intensity variations pose major challenges for subsequent image processing and analysis techniques. To mitigate this effect in the intensity correction process, an enhanced homomorphic unsharp masking (EHUM) method is proposed in this chapter. The main idea of the proposed EHUM method is determination of region of interest, intensity correction based on homomorphic filtering, and linear gray scale mapping followed by cutoff frequency selection of low pass filter used in the filtering process. This method first determines the ROI to overcome the halo effect between foreground and background regions. Then the intensity correction is carried out using homomorphic filtering and linear gray scale mapping.

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