Application of Models based on Human Vision in Medical Image Processing: A Review Article

Full Text (PDF, 1008KB), PP.23-28

Views: 0 Downloads: 0


Farzaneh Nikroorezaei 1 Somayeh Saraf Esmaili 2,*

1. Department of Biomedical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2. Department of Biomedical Engineering, Garmsar Branch, Islamic Azad University, Garmsar, Iran

* Corresponding author.


Received: 13 Aug. 2019 / Revised: 18 Sep. 2019 / Accepted: 24 Oct. 2019 / Published: 8 Dec. 2019

Index Terms

Medical Image Processing, Region of Interest (ROI), Saliency Map, Visual Attention


Nowadays by growing the number of available medical ‎imaging data, there is a great demand towards ‎computational systems for image processing which can ‎help with the task of detection and diagnosis. Early detection of abnormalities using computational systems can help doctors to plan an effective treatment program for the patient. The main ‎challenge of medical image processing is the automatic ‎computerized detection of a region of interest. In recent years ‎in order to improve the detection speed and increase the ‎accuracy rate of ROI detection, different models based on the human vision ‎system, have been introduced. In this paper, we have provided a brief description of recent works which mostly used visual ‎models, in medical image processing and finally, ‎a conclusion is drawn about open challenges and required research in this field.‎

Cite This Paper

Farzaneh Nikroorezaei, Somayeh Saraf Esmaili, " Application of Models based on Human Vision in Medical Image Processing: A Review Article", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.12, pp. 23-28, 2019. DOI: 10.5815/ijigsp.2019.12.03


[1]L. G. Ungerleider and J. V. Haxby, “‘‘what’’ and ‘‘where’’ in the human brain,” Current Opinion in Neurobiology, vol.4, pp. 157-165, 1994.

[2]D. H. Hubel and T. N. Wiesel, "Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex," The Journal of Physiology, vol. 160, pp. 106–154, 1962.

[3]E. T. Rolls and T. Milward, “A model of invariant object recognition in the visual system: learning rules, activation functions, lateral inhibition, and information-based performance measures,” Neural Computation, vol. 12, pp. 2547-2572, 2000.

[4]M. Riesenhuber and T. Poggio, “Hierarchical Models of Object Recognition in Cortex,” Nature euroscience, vol. 2, pp. 1019-1025, 1999.

[5]L.Itti and C.Koch, “Computational modeling of visual attention,” Nature Reviews Neuroscience, vol.2 (3), pp.194–203, 2001.

[6]C. Koch and S. Ullman, “Shifts in selective visual attention: Towards the underlying neural circuitry,” Human Neurobiology, vol.4, pp.219-227, 1985.

[7]L. Itti, C. Koch and E. Niebur, “A model of saliency-based visual-attention for rapid scene analysis,” IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 20, pp.1254-1259, 1998.

[8]Harel, J., Koch, C., & Perona, P. (2007). Graph-based visual saliency. In NIPS.

[9]V.Jampani, J.Sivaswamy et al., “Assessment of computational visual attention models on medical images,” Proceeding of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing, 2012.

[10]P.Agrawal, M.Vatsa and R.Singh, “Saliency based mass detection from screening mammograms,” Signal processing 99, pp.29-47, 2014.

[11]G.Han, Y.Jiao and, “The research on lung cancer significant detection combined with shape feature of target,” MATEC Web of Conferences 77, 13001, 2016.

[12]E. Pesce, P.P. Ypsilantis et al., “learning to detect chest radiographs containing lung nodules using visual attention networks”, arXiv: 1712.00996v1, 2017.

[13]L.Lu, Y.Xiaoting and D.Bo, “A fast segmentation algorithm of PET images based on visual saliency model”, Procedia Computer Science 92, pp.361–370, 2016.

[14] S.Banerjee, S. Mitra et al., “A novel GBM saliency detection models using multi-channel MRI,”PLOS ONE | DOI:10.1371/journal.pone.0146388, 2016.

[15]O. Ben-Ahmed, F. lecellier et al. “Multi-view saliency-based MRI classification for Alzheimer’s disease diagnosis,”  Seventh International Conference on Image Processing Theory, Tools and Applications, 2017.

[16]M. Mozaffarilegha, A.Yaghobi joybari, A.Mostaar, “Medical image fusion using BEMD and an efficient Fusion scheme’”, 2018.

[17]S. Bagheri, S. Saraf Esmaili, “An automatic model combining descriptors of Gray-level Co-occurrence matrix and HMAX model for adaptive detection of liver disease in CT images,” Signal Processing and Renewable Energy, pp.1-21, March 2019.