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Vol.2 , No. 5, Publication Date: Sep. 14, 2017, Page: 30-39
| [1] | Reza Takht Kinai, Department of Computer Sciences, Khoy Branch, Islamic Azad University, Khoy, Iran. |
| [2] | Yousef Farhang, Department of Computer Sciences, Khoy Branch, Islamic Azad University, Khoy, Iran. |
Multimodality merging of medical images has developed as a powerful tool for clinical applications by the rise of different modalities of medical images. The main incentive was to obtain relevant information from different sources as a single output which plays a key role in medical diagnosis. A good image fusion algorithm should preserve all significant features of the source image and provide as few contradictions in the results as possible. Contourlet can provide less heterogeneity in multi-resolution and directional and positional properties of 2D signals compared to other image opening methods. In this work it was tried to develop an algorithm for medical image fusion by combining contourlet transformation and multi-fractal spectrum in which the fused image can provide more information for each of the input sources of the merged image resulting in more suitable images for human vision and comprehension and clinical applications. The efficiency is shown using different tests on different medical images. In addition, improved performance of the proposed framework compared to other methods was observed.
Keywords
Merging of Medical Images, Counterlet Transform, Multi-modality, Multi-fractal Spectrum, Human Vision and Comprehension
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