International Journal of Advanced Scientific and Technical Research Available online on http://www.rspublication.com/ijst/index.html

Issue 4 volume 5, Sep. – Oct. 2014 ISSN 2249-9954

An efficient data level fusion of multimodal medical images by cross scale fusion rule.

[1] AYUSH DOGRA(CORRESPONDING AUTHOR) PH.D STUDENT (DEPT OF ECE) MMU,MULLANA,AMBALA

[2]DR.PARVINDER BHALLA PROFESSOR(DEPT OF ECE) MMU, MULLANA,AMBALA

ABSTRACT Medical image fusion can help the physicians to extract the features that may not be normally visible in images by different modalities. In this paper, propose an efficient fusion method based on cross scale fusion rule. The performance of the propose method can be verified by objective evaluation metrics i.e. QAB/F .

INTRODUCTION & MOTIVATION Image fusion is the process of integrating two different modalities to form a single modality. Image fusion is playing a crucial role in medical imaging remote sensing, computer vision , robotics etc. image fusion can be categorized into three levels (1) data level (2) attribute level (3) symbol level. Though image fusion can R S. Publication, [email protected]

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Issue 4 volume 5, Sep. – Oct. 2014 ISSN 2249-9954

be used in many areas like remote sensing & astronomy. Applications like remote sensing & astronomy use multisensory fusion to achieve high spatial and high spectral resolutions by combining the two modalities , one having higher spatial resolutions and other having higher spectral resolution. There are numerous applications that have appeared in medical imaging like simultaneous evaluation of CT, MRI & PET images. In medical field pixel level is regarded as computationally efficient. Medical image fusion is hot topic of research now days. Lot of research has been conducted on medical image fusion in the last decade, but still there is scope in the coming years. Multisensory fusion have large applications in military security and surveillance areas. In multiview fusion a set of images of same scence taken by the same sensor but from different view points is fused to obtain an image with higher resolution . Beside multi sensor & multi view there are other types of fusion strategies that are well explained in [5].

LITERATURE SURVEY Since it is well known that lot of efficient & effective techniques have been proposed during the last ten years. But it is better to consider the most recent techniques during the last 4-5 years. In 2011, multi focus image fusion technique based on bilateral gradient sharpness criterion is proposed by Jing Tian & his co researchers [1]. In 2012, Jian Tian & Li chen proposed a multi focus image fusion method based on wavelet statistical sharpness measure [2]. In 2013,fusion techniques using cross bilateral filter is proposed by B.K. Shreyamsha kumar[3]. But a most revolutionary image fusion method based on cross scale coefficient selection is proposed by Rui shen and his co researchers in 2013 [4] & claims that his proposed method is efficient than the existing one.

RESEARCH GAPS &FUTURE SCOPE In paper [4], Dr. Rui shen and his fellow research workers claims that there is no formation of artifacts in the fused results. Formation of artifacts can not be fully avoided but it can be reduced. Amount of formation of artifacts can be calculated by objective evaluation metrics where as author does not done any such evaluation. Secondly the author does not evaluate the loss of information from source images to fused images. These are the mere drawbacks that I would like to investigate in R S. Publication, [email protected]

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Issue 4 volume 5, Sep. – Oct. 2014 ISSN 2249-9954

future research work and like to go for the future enhancement of the cross scale fusion rule .

PROBLEM FORMULATION

Figure 1-MSD based fusion[4] First the source images are decomposed to multi scale representations using multi resolution decomposition to various levels. MSR is low resolution pyramidal structure contains one approximation level and several detail levels. Approximation level stores low pass coefficients and DET stores high pass coefficients. Then a proposed cross scale fusion rule is applied to different coefficients and finally get the resultant synthesized image by applying inverse MSD.

OBJECTIVE OF THIS PAPER .(1) To re -implement the cross scale fusion rule proposed by Dr. Rui shen. and propose a novel techniques of image fusion.

WORK DONE We have taken one data set of PET & MRI images. Then decomposed these source images to 3 levels by MSD(DWT). Then imply the Gaussian & Butterworth band pass filtering of mid frequencies but prefer the Gaussian band pass filter as it avoid ringing effects that lead to formation of artifacts. Then activity level measurement is done by CBA (coefficient based activity) for DET of source images. Then compute the member ship by using zero gauss member ship function as input and compute the output the triangular membership function for coefficients selection. R S. Publication, [email protected]

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Issue 4 volume 5, Sep. – Oct. 2014 ISSN 2249-9954

If IX = 0,IY = 0 then select low pass coefficient i.e. APX. If Ix ≠ 0,Iy≠0 then select high pass coefficients i.e. DET. So as a conclusion we have to select both high pass & low pass coefficients. Firstly select the low pass coefficient for fusion. Fused the different levels of APX’S of first source images to another APX’S of second source images and get the final fused images.

PET IMAGE

MRI IMAGE

MULTIRESOLUTION DECOMPOSITION BY DWT FOR MRI (1ST ,2ND & 3RD LEVEL)

MULTIRESOLUTION DECOMPOSITION BY DWT FOR PET (1ST,2ND ,& 3RD LEVEL)

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Issue 4 volume 5, Sep. – Oct. 2014 ISSN 2249-9954

GAUSSIAN BAND PASS FILTERING FOR MRI APX( 1ST ,2ND & 3RD LEVEL )

BUTTERWORTH BAND PASS FILETRING FOR MRI APX(1ST,2ND&3RD LEVEL)

GAUSSIAN BAND PASS FILTERING FOR PET APX( 1ST ,2ND & 3RD LEVEL )

BUTTERWORTH BAND PASS FILETRING FOR PET APX(1ST,2ND&3RD LEVEL) R S. Publication, [email protected]

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ACTIVITY LEVEL MEASURMENT BY CBA FOR DET (PET)

ACTIVITY LEVEL MEASURMENT BY CBA FOR DET (MRI)

CALCULATION OF CORRELATION COFFICIENT OF IMAGE WITH MEDIAN FILTER

CALCULATION OF CORRELATION COFFICIENT OF IMAGE WITH MEDIAN FILTER

PET 1ST LEVEL

PET 2ND LEVEL

PET 3RD LEVEL

MRI 1ST LEVEL

MRI 2ND LEVEL

MRI 3RD LEVEL

HORIZONTAL

DIAGONAL

VERTICAL

HORIZONTAL

DIAGONAL

VERTICAL

- 0.2741

0.8759

- 0.0865

- 0.2688

0.8794

- 0.0571

0.6603

0.6656

0.6672

0.7400

0.8132

0.7205

0.9584

0.9636

0.5978

0.2122

0.7765

0.8278

MEMBERSHIP FUNCTION FOR MRI ,PET (APX-1ST,2ND AND 3RD LEVEL)

MEMBERSHIP FUNCTION FOR MRI ,PET (DET-1ST,2ND AND 3RD LEVEL)

• IF Ix =0, Iy= 0 then select low coefficient i.e. APX • IF Ix not equal to 0 ,Iy not equal to 0, then select high coefficient i.e. DET

• IF Ix =0, Iy= 0 then select low coefficient i.e. APX • IF Ix not equal to 0 ,Iy not equal to 0, then select high coefficient i.e. DET

FUSING APX 1ST LEVEL MRI TO APX 1ST LEVEL PET

MRI APX 1ST LEVEL

PET APX 1ST LEVEL

FUSED IMAGE

FUSING APX 2ND LEVEL MRI TO APX 2ND LEVEL PET

• MRI APX 2NDLEVEL

PET APX 2ND LEVEL

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FUSED IMAGE

FUSING APX 3RD LEVEL MRI TO APX 3RD LEVEL PET

• MRI APX 3RD LEVEL

PET APX 3 RD LEVEL

FUSED IMAGE

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Issue 4 volume 5, Sep. – Oct. 2014 ISSN 2249-9954

OBJECTIVE PERFORMANCE EVALUATION LEVELS

QAB/F

MRI PET (1ST LEVEL)

0.7261

MRI PET (2ND LEVEL)

0.6342

MRI PET (3RD LEVEL)

0.7702

CONCLUSION QAB/F value is almost close to base paper[4]. But there is no control over the formation of artifacts. Formation of artifacts induces distortions and noise in the fused images. It is serious matter to avoid for the upcoming steps.

REFERENCES [1]. Jing Tian, Li Chen, Lihong Ma, Weiyu Yu,"multi focus image fusion using a bilateral gradient based sharpness criterion" elsevier,optics communications vol.264,issue 1,jan 2011. [2]. Jing Tian , Li Chen,"adaptive multifocus image using a wavelet based statistical sharpness measure"Elsevier,signal processing,vol. 92,issue 9,September 2012. [3]. B.K. Shreyamsha Kumar,"image fusion based on pixel significance using cross bilateral filter "signal,image & video processing,springer, october 2013. [4] Rui Shen, Irene Cheng, Anup Basu," cross scale coefficient slection for volumetric medical image fusion,”IEEE,TBE,vol.60,no.4,april 2013. [5] http://staff.utia.cas.cz/sroubekf/papers/EUSIPCO_07_fusion_tut.pdf

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