An Efficient Technique of Multimodality Medical Image Fusion using Improved Contourlet Transformation
Jaspreet Kaur1, Chirag Sharma2
1Jaspreet Kaur, Computer Science and Technology, Lovely Professional University, Phagwara, India.
2Chirag Sharma, Computer Science and Technology, Lovely Professional University, Phagwara, India.
Manuscript received on 12 December 2012 | Revised Manuscript received on 21 December 2012 | Manuscript Published on 30 December 2012 | PP: 28-30 | Volume-2 Issue-1, December 2012 | Retrieval Number: A0364112112 /2012©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In medical field to diagnosis the disease an advance technology is used that is multimodality images. To find best diagnosis for a particular disease we perform image fusion. Major issue in multimodality medical image fusion is how to fuse two or more images of different modalities, so that we get more accurate information. To perform efficient fusion contourlet transformation gives the up to mark results. So, In this paper, we propose an improved contourlet transformation, in which we are using multi scale decomposition and considering that DFBs can be modified with Log Gabor Filter in place of low pass and high pass filter. Log Gabor filter localizes an image more accurately and also minimizes the DC Component (noise in image) with which we are improving the quality of fused image. In this paper, we are considering Registered Medical Images. Performance of proposed method is evaluated by five qualities.
Keywords: CNT, DFBs, Log Gabor Filter Multimodalities Medical images fusion.
Scope of the Article: Aggregation, Integration, and Transformation