MRI Image Enhancement: Optimized Filtering Mechanism for Achieving High Accuracy in Diagnose Process


  • Abhinav Singh Andotra Department of Computer Science, Guru Nanak Dev University, Amritsar, Punjab, India
  • Sandeep Sharma Department of Computer Science, Guru Nanak Dev University, Amritsar, Punjab, India



Noise handling, MSE, PSNR, Histogram equivalence, Adaptive median filter


Segmentation plays an important role in separating data from medicinal images and also helps in clinical findings. Segmentation is the way toward apportioning the image into different regions. MRI is utilized to extract images of delicate tissues of human body. It is utilized in analyzing the human organs without the requirement of surgery. For the most part MRI images contain a lot of noise caused by operator performance, equipment and the environment, which prompts genuine errors. MRI is a productive way in giving data in regards to the area of tumors and even the volume. The noise present in the MRI image can be evacuated by utilizing different de-noising procedures whichever is most appropriate method depending on the type of image obtained and afterward can be handled by any of the segmentation techniques. The noise in MRI images might be because of field strength, RF pulses, RF coil, voxel volume, or receiver bandwidth. In our proposed paper a review of different noise handling and filtering mechanism is conducted in order to enhance the quality of image. In this paper we modify the adaptive median filter by applying redundancy handling mechanism and enhance the contrast of image by applying histogram equivalence method.


G. Deng and L. W. Cahill, “Guassian filter for noise reduction,” in 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, pp. 1615–1619.

M. Kaur and R. Kaur, “Review of various filters for noise reduction,” pp. 238–242, 2016.

Y. Ma, D. Lin, B. Zhang, Q. Liu, and J. Gu, “ A PCNN Time matrix used for noise removal,” in 2007 IEEE International Conference on Signal Processing and Communications, pp. 1499–1502, 2007.

N. Vyas, A. Jain, and C. P. Singh, “A Review of high density function for noise removal,” pp. 1613–1616, 2016.

P. Singh, “Review of image restoration techniques,” IEEE ACCESS, Vol. 149, No. 1, pp. 18–27, 2016.

L. Liang, J. Wang, J. Song, and A. S. Model, “ an algorithm for noise removal using CNGSS,” no. 5, pp. 1–3.

A. Kandpal and V. Ramola, “Image segmentation using various filters,” Vol. 3, No. 3, pp. 201–206, 2015.

G. Wang, D. Li, W. Pan, and Z. Zang, “Noise removal using Modified switching median filter,” Signal Processing, Vol. 90, No. 12, pp. 3213–3218, 2010.

M. P. Mahajan, E. Engineering, and E. Engineering, “ Review of various noise removal filter utilized non linear methodology,” pp. 6820–6828.

A. June, “An algorithm for noise reduction based on Fuzzy system,” Vol. 2, No. 2, 2014.

A. Singh, “ Survey of various noise removal techniques for removing impulse noise,” IEEE ACCESS, Vol. 3, No. 6, pp. 659–665, 2014.

E. E. Kerre, D. Van De Ville, M. Nachtegael, D. Van Der Weken, and E. E. Kerre, “ By Using fuzzy image filtering noise removal . IEEE,” Vol. 125050, No. January 2013, 2003.

R. Pandey, A. Awasthi, and V. Srivastava, “ the comparative study of Bit error rate and SNR in OFDM,” Vol. 2013, No. Cac2s, pp. 463-466, 2013.

A. Agrawal, A. Choubey, and K. K. Nagwanshi, “Noise Reduction in medical image using adaptive fuzzy based Image Filtering techniques,” Vol. 2, No. 4, pp. 1457–1461, 2011.

S. H. Teoh and H. Ibrahim, “Analysis of Median Filtering Frameworks : A Literature Survey,” Vol. 1, No. 4, pp. 4–7, 2012.

U. R and P. K, “Hybrid Approach for Noise Removal and Image Enhancement of Brain Tumors in Magnetic Resonance Images,” Adv. Comput. An Int. J., Vol. 7, No. 1/2, pp. 67–77, 2016.

A. S. Y. Bin-habtoor and S. S. Al-amri, “Removal Speckle Noise from Medical Image Using Image Processing Techniques,” Int. J. Comput. Sci. Inf. Technol., Vol. 7, No. 1, pp. 375–377, 2016.

S. Renukalatha, “Brain Tumor Analysis of Rician Noise Affected MRI Images,” vol. 141, no. 14, pp. 26–33, 2016.

S. Aja-Fernández, C. Alberola-López, and C. F. Westin, “Noise and signal estimation in magnitude MRI and Rician distributed images: A LMMSE approach,” IEEE Trans. Image Process., Vol. 17, No. 8, pp. 1383–1398, 2008.

A. Srivastava, A. Asati, and M. Bhattacharya, “A Fast and Noise-Adaptive Rough-Fuzzy Hybrid Algorith for Medical Image Segmentation,” IEEE Int. Onference Bioinforma. Biomed., pp. 416–421, 2010.

P. Borrelli, E. Tedeschi, S. Cocozza, C. Russo, M. Salvatore, G. Palma, M. Comerci, B. Alfano, and E. M. Haacke, “ NLM -based noise removal technique for Improving SNR,” IST 2014 – 2014 IEEE Int. Conf. Imaging Syst. Tech. Proc., Vol. 2, No. 3, pp. 346–350, 2014.




How to Cite

Andotra, A. S., & Sharma, S. (2018). MRI Image Enhancement: Optimized Filtering Mechanism for Achieving High Accuracy in Diagnose Process. Asian Journal of Computer Science and Technology, 7(1), 66–70.