Medical image enhancement in F-shift transformation domain
- 65 Downloads
Image enhancement technology plays an important role in the diagnosis and treatment of medical diseases. In this paper, we propose a method to automatically enhance medical images. The proposed method could be used to support clinical medical diagnosis, adjuvant therapy and curative effect diagnosis. This scheme uses contrast limited adaptive histogram equalization (CLAHE) method in F-shift transformation domain. Firstly, we adjust the overall brightness of the underexposed or overexposed image. Secondly, we perform CLAHE to enhance the low-frequency components obtained by one-level two-dimensional F-shift transformation (TDFS) on the adjusted images. At this stage, most of the coefficients in the high-frequency component can be changed to zero through properly setting the error bound. We then use inverse transformation to reconstruct image which is further enhanced with CLAHE. Compared to previous work, this approach takes into account not only the image enhancement, but also the data compression. Experimental results and comparison with state-of-the-art methods show that our proposed method has a better enhancement performance. Moreover, it has a certain data compression ability.
KeywordsMedical image Image enhancement F-shift transformation CLAHE
This work was financially supported by the National Natural Science Foundation (No. 61572022), the Science and Technology Project of Hebei Academy of Sciences (Nos. 19607, 18607, 18605).
- 3.Wang Q, Chen L, Shen D. Fast histogram equalization for medical image enhancement. In: International conference of the IEEE Engineering in Medicine and Biology Society, 2008 (EMBS 2008); 2008. p. 2217–20.Google Scholar
- 4.Kaur H, Rani J. MRI brain image enhancement using histogram equalization techniques. In: International conference on wireless communications, signal processing and networking; 2016.Google Scholar
- 5.Senthilkumaran N, Thimmiaraja J. Histogram equalization for image enhancement using MRI brain images. In: Computing and communication technologies; 2014. p. 80–3.Google Scholar
- 7.Ahmad SAB, Taib MN, Khalid NEA, Taib H. Variations of adaptive histogram equalization (AHE) analysis on intra-oral dental radiograph. In: Control and system graduate research colloquium; 2016. p. 87–92.Google Scholar
- 9.Setiawan AW, Mengko TR, Santoso OS, et al. Color retinal image enhancement using CLAHE. In: IEEE International conference on ICT for smart society; 2013. p. 1–3.Google Scholar
- 12.Tsai DY, Lee Y. A method of medical image enhancement using wavelet-coefficient mapping functions. In: International conference on neural networks and signal processing, vol. 2; 2003. p. 1091–4.Google Scholar
- 14.Liu X, Sun Q, Tang J. An image enhancement technique in the DCT domain for cancer detection. In: 2008 Asilomar conference on signals, systems and computers; 2008. p. 1905–9.Google Scholar
- 15.He W, Wu Q, Li S. Medical X-ray image enhancement based on wavelet domain homomorphic filtering and CLAHE. In: International conference on robots & intelligent system; 2016. p. 249–54.Google Scholar
- 22.Guo X, Zhao H, Li X, Li T, Dai M. EEG signal analysis based on fixed-value shift compression algorithm. In: International conference on natural computation; 2016. p. 959–963.Google Scholar
- 23.Xu X, Wang H, Fan R, Li X. An application of lossy compression of the echolocation signals of toothed whale. In: International conference on wavelet analysis and pattern recognition; 2017. p. 7–12.Google Scholar