Hyper Spectral Multi Spectral Images Fusion Using Matlab

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Description

Abstract:

Multimodal medical image fusion is effectuated to minimize the redundancy while augmenting the necessary information from the input images acquired using different medical imaging sensors. The sole aim is to yield a single fused image, which could be more informative for efficient clinical analysis. This paper presents? multimodal fusion framework using the non? sub-sampled Contourlet transform (NSCT) domains for images acquired using two distinct medical imaging sensor modalities (i.e., magnetic resonance imaging and computed tomography scan). The major advantage of using NSCT is to improve upon the shift variance, directionality, and phase information in the final fused image. Does the first stage employ an NSCT domain for fusion? and then the second stage to enhance the contrast of the diagnostic features by using the Guided filter. A quantitative analysis of fused images is carried out using dedicated fusion metrics. The fusion responses of the proposed approach are also compared with other state-of-the-art fusion approaches; depicting the superiority of the obtained fusion results. Finally, segment the tumor part by applying Fuzzy C-Means Clustering.


OVERVIEW AND SCOPE

The main? the purpose is to scan the medical image as a fusion by using image processing technique and it’s one of the techniques which we used . and also we used deep learning which comes under image processing. It is used for diagnoses like CT SCAN, MRI SCAN, etc?


Existing method:

  • Image averaging and maximization method
  • Principal component analysis
  • Discrete Cosine Transform

Drawbacks:-

  • Contrast information loss due to averaging method
  • The maximizing approach is sensitive to sensor noise
  • Spatial distortion is high
  • Limited performance in terms of edge and texture representation

PROPOSED SYSTEM

  • PREPROCESSING?
  • STATIONARY WAVELET TRANSFORM
  • CONVOLUTIONAL NEURAL NETWORK
  • NSCT

ADVANTAGE

  • Efficient compression ratio
  • Accuracy is high
  • Visual quality is high
  • Security is high
  • NSCT provides better edges and texture regions than other transforms

Block diagram

Hyper Spectral Multi Spectral Images Fusion Using Matlab


 

CLASSIFICATION OF IMAGES:

There are 3 types of images used in Digital Image Processing. They are

  1. Binary Image
  2. Gray Scale Image
  3. Color Image

CONCLUSION

In this research, we proposed the wavelet-based fusion approach for PET and MRI image diagnosis. The experiment has tested on three dieses datasets named for normal axial, normal coronal, and Alzheimer’s disease brain images. The wavelet decomposition of the dataset has been done four-level with low and high activity regions. The quality of the fused image is tested using the MSE and PSNR approaches. This proposed method gives 90-95% accuracy for the fusion. The experiment is tested over the haar wavelet approach. This experiment can be extended towards the haar and db1 wavelet for the three-dimensional medical multi-model databases with for fusion. Medical image fusion plays a dynamic role in medical imaging applications by helping the radiologists for spotting the abnormality, especially tumors in MRI brain images. The proposed image fusion algorithm has been analyzed for different types of MRI and CT images From the obtained results it is noted that the proposed method NSCT is giving better results than other methods.


REFERENCE:

[1] R Yuqian Li, Xin Liu,Feng Wei, ?An Advanced MRI and MRSI data fusion scheme for enhancing unsupervised brain tumor differentiation?, Elsevier, computers in biology and medicine 81, pg.no.121-129, 2017.

[2] Tian Lan, Zhe Xiao, Yi Li, Yi Ding, Zhiguang Qin, ?Multimodal Medical Image Fusion using wavelet transform and human vision system?, ICALIP,978-1-4799-3903-9/4, IEEE 2014.

[3] K.P.Indira, Dr.R.Hemamalini,?Impact of co-efficient selection rules on the performance of DWT-based fusion on medical images? International Conference on Robotics, Automation, Control and Embedded Systems, ISBN 978-81-925974-3-0, 2015.

[4] Sonia kuruvilla, J.Anitha,?Comparison of registered multimodal medical image fusion techniques? International Conference on Electronics and Communication Systems,2014.

[5] Ramandeep Kaur, Sukhpreet Kaur,?An approach for image fusion using PCA and Genetic Algorithm?, International journal of computer applications (0975-8887), volume 145, no.6, July 2016.

[6] Arati Kushwaha, Ashish Khare, Om Prakash, Jong-In Song, Moong Jeon ?3D Medical Image Fusion using Dual tree complex wavelet transform?, International conference on control, information and automation sciences, 978-1-4799-9892-0/15/, IEEE 2015.

[7] Tannaz akbarpour, Mousa Shamsi, Sabalan Daneshvar,?Structural medical image fusion by means of dual-tree complex wavelet? IEEE Iranian conference on electrical engineering, 978-1-4799-4409-5/14, 2014.

[8] Richa Srivastava1, Om Prakash, Ashish Khare, ?Local energy-based multimodal medical image fusion in curvelet domain?, IET computer vision, volume 10, issue 6, pp.513-527, 2016.

[9] S. Wuerger, G. Meyer, M. Hofbauer, C. Zetzsche, K. Schill, Motion extrapolation of auditory? visual targets, Information Fusion 11 (1) (2010) 45? 50.?

[10] T. D. Dixon, S. G. Nikolov, J. J. Lewis, J. Li, E. F. Canga, J. M. Noyes, T. Troscianko, D. R. Bull, C. N. Canagarajah, Task-based scan path assessment of multi-sensor video fusion in complex scenarios, Information Fusion 11 (1) (2010) 51? 65.

[11] J.-B. Lei, J.-B. Yin, H.-B. Shen, Feature fusion and selection for recognizing cancer-related mutations from common polymorphisms, in Pattern Recognition (CCPR), 2010 Chinese Conference on, IEEE, 2010, pp. 1? 5.

?[12] S. Tsevas, D. Iakovidis, Dynamic time warping fusion for the retrieval of similar patient cases represented by multimodal time-series medical data, in Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on, IEEE, 2010, pp. 1? 4.?

[13] H. M?ller, J. K.-Cramer, The Image CLEF Medical Retrieval Task at ICPR 2010? Information Fusion to Combine Visual and Textual Information, in Recognizing Patterns in Signals, Speech, Images and Videos, Springer, 2010, pp. 99? 108.?

[14] Z. R. Mnatsakanyan, H. S. Burkom, M. R. Hashemian, M. A. Coletta, Distributed information fusion models for regional public health surveillance, Information Fusion 13 (2) (2012) 129? 136.?

[15] S. Marshall, G. Matsopoulos, Morphological data fusion in medical imaging, in Nonlinear Digital Signal Processing, 1993. IEEE Winter Workshop on, IEEE, 1993, pp. 6? 1.


 

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