Biomedical Research
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Localization of neurodegenerative brain MRI image for gene expression evaluation

Babu Gopal* and Sivakumar Rajagopal

R.M.K. Engineering College, Kavaraipettai-601206, Tamil Nadu, India

*Corresponding Author:
Babu Gopal
R.M.K Engineering College
Tamil Nadu, India

Accepted date: May 11, 2016

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Abstract

The soft computing techniques solve the major localization problems in optimization of biomedical images. We have developed an automatic method aimed first at segmentation of MRI brain images by denoising with Discrete Curvelet Transform. Then clustering of denoised images using Fuzzy C-means clustering localized the abnormality by simulating the anatomical structure. The statistical analysis confirmed the validity of the algorithm. The abnormality in localization compared with microarray gene expression evaluation also showed variations which will be helpful for the development of gene module based neuroimaging advancements.

Keywords

Neuro degeneration, Discrete curvelet decomposition, Spatial fuzzy clustering, Brain MRI image, Gene expression evaluation.

Introduction

Magnetic Resonance Imaging (MRI) is routinely used in the diagnosis, characterization and clinical management of neurodegenerative disorders of brain [1,2]. It is a dynamic noninvasive diagnostic imaging tool that allows global assessment of brain images and their interaction with their local environment [3]. Noise is an ingrained phenomenon in the medical images which may increase the root mean square error and reduce the peak signal to noise ratio [4-7].

Curvelet based approach is used for the denoising of Magnetic Resonance (MR) and Computer Tomography (CT) images [6]. Image fusion of MRI and CT images is also possible by this analysis for better interpretation [7]. Curvelet transform is a new multiscale representation suited for objects which are smooth away from discontinuities across curves, which was developed by Candies and Donoho [8] and this digital transforms is applied for the denoising of some standard MR and CT images embedded in random noise [9,10]. The Curvelet reconstructions exhibit higher perceptual quality than wavelet based reconstructions, offering visually sharper images and, in particular, higher quality recovery of edges and of faint linear and curvilinear features. Curvelets will be superior over wavelets in the following cases [11]

• Optimal sparse representation in object with edges

• Optimal image reconstruction of severely ill-posed problems

• Optimal sparse representation in wave propagators

Since the brain images have several objects and curved shapes, it is expected that Curvelet transform would be better in their denoising [12].

The proposed filtering technique is used along with the Curvelet transform and Wavelet transform and it is observed that the Curvelet transform produces better result when compared with the Wavelet transform [13]. The simulation results have proved that this method actively removes the noise and reduces the root mean square error while increasing the image enhancement factor and peak signal to noise ratio [14,15]. Major denoising methods include Gaussian filtering [16], Wiener filtering, and wavelet thresholding [17]. Many more methods make assumptions about the image that can lead to blurring.

A new method called the non-local means algorithm is presented that does not make the assumptions that lead to blurring [18]. Non local means filter uses all the possible selfpredictions and self-similarities that the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity [19,20]. Fuzzy clustering has a major role in solving problems in the areas of pattern recognition and fuzzy model identification. A variety of fuzzy clustering methods have been proposed and most of them are based upon distance criteria as described by Krinidis et al. [21].

It uses reciprocal distance to compute fuzzy weights. A more efficient algorithm is the new FCM. It computes the cluster centre using Gaussian weights as described by Yang et al. [22] and Despotovic et al. [23]. The FCM technique reduces the noise effect, because no similar cluster is present in the neighbourhood, the weight of the noisy cluster is greatly reduced with FCM which is not the case in K means as described [24]. Furthermore, the membership of the correct cluster is enhanced by the cluster distribution in the neighbouring pixels [25].

The level of cellular and molecular complexity of the nervous system creates unique problems for the neuroscientist in the design and implementation of functional genomics studies [26]. Reasons for major drawbacks of microarray data is its voluminous analysis [27,28]. Recently, several studies have attempted to correlate imaging findings with molecular markers, but no consistent associations have emerged and many of the imaging features that characterize neurodegeneration currently lack biological or molecular correlates [29].

Much of the information encoded within neuroimaging studies therefore remains unaccounted and incompletely characterized at the molecular level. We reasoned that the phenotypic neurodegeneration captured by neuroimaging reflects underlying could be uncovered by combining genome-scale gene expression and MRI [1].

Materials and Methods

Subjects

Experiments were carried out with 50 normal and 100 abnormal subjects with neurodegenerative symptoms (Age group of 40-70 years) due to alcoholic history. The MRI Images were obtained from the radiology department, Stanley medical college. Reference MRI images of individuals used in this study received from Allen Brain Atlas (www.allenbrainatlas.com) (Table 1). Brain specimens of case no. 1015 showed histopathology as modest numbers of hemosiderin laden macrophages noted in Virchow-Robin spaces in cerebral lobes. Brain specimens of case no.s 1009 (age: 57 years) and 2001 (age: 24 years) had normal microneuropathology and were taken as controls for this study.

case Age Clinical history Neuromicropathology
1009 57 years atherosclerosis Normal
1015 49 years hypothyroidism Haemosiderosis
2001 24 years asthma Normal

Table.1. The description of MRI image references used in this study.

Experimental methods

Image independent noise can often be described by an additive noise model, where the recorded image f (i, j) is the sum of the true image s (i, j) and the noise n (i, j) and is denoted by [7],

f (i,j) = s (i,j) +n (i,j) → (1)

The noise n (i, j) is often zero mean and described by its variance. The impact of the noise on the image is often described by the Signal to Noise Ratio (SNR), which is as expressed as [7]

image (2)

Where σs and σf are the variances of the true image and the recorded image, respectively. Curvelets are designed to handle curves using only a small number of coefficients. Hence the Curvelet handles curve discontinuities well. The procedure to denoise an image using Curvelet transform can be expressed as [7]

Restored Image g (i, j) = DCT-1[DCT(f(i,j))]→(3)

The MRI brain images samples are denoised and restored by curvelet decomposing and compositions as restored image as shown in Figure 1. For performing curvelet transform, the curvelet transformation is applied to the noisy image instead of the wavelet transform [7].

biomedres-discret-curvelet-transform

Figure 1: Flow chart of discrete curvelet transform with spatial fuzzy clustering.

Dct (n) = C.T [f (i,j)] = {D1 (n) + D11 (n) + D12 (n) + D13 (n) + D14 (n) + D15 (n) + D16 (n) + D17 (n) + D18 (n) + D2 (n)}→(4)

The filter is applied to the decomposed components.

DF1 (n) = Fnlm (D1 (n))→ (5)

DF11 (n) = Ft (D11 (n)) → (6)

DF12 (n) = Ft (D12 (n)) → (7)

DF13 (n) = Ft (D13 (n)) → (8)

DF14 (n) = Ft (D14 (n)) → (9)

DF15 (n) = Ft (D15 (n)) → (10)

DF16 (n) = Ft (D16 (n)) → (11)

DF17 (n) = Ft (D17 (n)) → (12)

DF18 (n) = Ft (D18 (n)) → (13)

DF2 (n) = Ft (D2 (n)) → (14)

After applying the inverse transformation, the Restored Image is obtained as O (n) [7]

O (n) = I.C.T [DF1 (n) + DF11 (n) + DF12 (n) + DF13 (n) + DF14 (n) + DF15 (n) + DF16 (n) + DF17 (n) + DF18 (n) + D2 (n)]→(15)

The Performance Parameter, Peak Signal to Noise ratio (PSNR) can be derived as [7]

image (16)

Where the Performance Parameter RMSE can be obtained by [7]

image (17)

Spatial Fuzzy C Means method incorporates spatial information [30] and the membership weighting of each cluster is altered based on the neighbourhood (Figure 2). The first pass is the same as that in standard FCM to calculate the membership function in the spectral domain. In the second pass, the membership information of each pixel is mapped to the spatial domain and the spatial function is computed from that. The FCM iteration proceeds with the new membership that is incorporated with the spatial function and it has been described by Lui et al. [25] and Xiang et al. [31]. The idea of FCM is using the weights that minimize the total weighted mean square error as described by Ahmed et al. [32].

biomedres-spatial-fuzz-clustering

Figure 2: Flow chart of spatial fuzzy clustering.

Initializing the Fuzzy Partition Matrix takes place initially. The weights are initialized using feature vectors or randomly [33]. The process of initializing the Fuzzy Partition Matrix (FPM) is done randomly and the size of it must be equal to the number of clusters and length of the image as rows and columns respectively [34].

Second the membership function M (i,j) is determined from the initialized Fuzzy Partition matrix as [34],

image (18)

Third the Centre Cij for each cluster is determined based on the input pixel intensity and the membership function determined using the expressed as [32],

image (19)

Then, the distance is calculated by finding the difference between the centre and the input image. The Distance is given by [33],

image (20)

Finally updated Membership matrix is to be determined. New membership matrix is inversely proportional to the square of distance matrix and updated during iterations.

Microarray analysis

Genes associated with neurogeneration in iron accumulation were identified as ATPase type 13A2 (ATP13A2), Phospholipase A2, group V1 (PLA2G6), Pantothenate kinase 2 (PANK2), DDB1 and CUL4 associated factor 17 (DCAF17), Fatty acid 2-hydroxylase (FA2H), Ferritin light polypeptide (FTL) and Ceruloplasmin (ferroxidase) (CP) [29]. Their microarray expression data were acquired from Allen brain atlas website and compared in Tables 4-6.

Coronal Plane %RD %Sen %Spe %Acc RMSE PSNR CC SSI
-97 2.9086 39.9335 99.9967 94.7571 5.6457 40.6136 0.9753 0.0576
-93 2.4408 74.4621 98.2430 96.1685 5.6556 40.6137 0.9762 0.0821
-86 1.9891 45.2125 99.9851 90.0955 5.6457 40.6136 0.9740 0.1185
-75 1.6420 28.7983 100 80.0430 5.6457 40.6136 0.9755 0.1659
-66 1.4926 41.1329 99.9651 79.7424 5.6457 40.6136 0.9736 0.2025
-55 1.3950 30.1337 99.9520 72.3053 5.6457 40.6136 0.9720 0.2475
-44 1.3759 37.7875 100 73.6649 5.6457 40.6136 0.9713 0.2660
-34 1.4155 43.8620 100 77.3849 5.6457 40.6136 0.9719 0.2620
-25 1.4572 63.8922 99.2335 85.4919 5.6457 40.6136 0.9750 0.2642
-19 1.4637 60.8328 99.4975 84.5505 5.6456 40.6137 0.9751 0.2621
-13 1.4784 59.8750 99.9065 85.3485 5.6457 40.6136 0.9744 0.2523
0 1.5272 58.8570 99.3294 85.5621 5.6457 40.6136 0.9747 0.2328
8 1.5824 42.1302 100 82.8720 5.6457 40.6136 0.9735 0.2132
18 1.6602 43.5137 100 84.9976 5.6457 40.6136 0.9740 0.1892
24 1.7647 42.0204 100 86.3907 5.6457 40.6136 0.9735 0.1639
35 1.9043 50.1957 99.8660 89.7964 5.6457 40.6136 0.9740 0.1381
43 1.9043 59.4738 99.8481 91.5115 5.6457 40.6136 0.9740 0.1377
50 2.1747 49.7313 99.9668 93.5486 5.6457 40.6136 0.9681 0.0988
58 2.4257 34.3820 99.9842 91.5527 5.6456 40.6137 0.9699 0.0776
64 2.8098 26.1394 100 95.3262 5.6457 40.6136 0.9688 0.0594

Table 2. Evaluation of performance parameters of coronal axes brain images.

Axial Plane %RD %Sen %Spe %Acc RMSE PSNR CC SSI
-45 2.3143 43.0611 100 93.0008 5.6457 40.6137 0.9719 0.0999
-35 1.8256 42.3746 99.6355 87.6846 5.6457 40.6136 0.9731 0.1599
-29 1.6333 25.9270 99.8060 80.5618 5.6457 40.6136 0.9720 0.1897
-23 1.5119 18.9072 100 74.4110 5.6458 40.6136 0.9728 0.2062
-17 1.4217 20.9665 99.9121 71.6446 5.6457 40.6136 0.9693 0.2223
-11 1.3892 24.7301 99.7721 69.4397 5.6457 40.6136 0.9702 0.2379
-4 1.3706 49.3072 100 79.8462 5.6457 40.6136 0.9653 0.2686
0 1.3676 49.3802 99.9687 78.9841 5.6457 40.6136 0.9667 0.2753
7 1.3680 65.0304 95.7717 82.3364 5.6457 40.6136 0.9695 0.2742
9 1.3698 62.4724 96.2724 81.7871 5.6458 40.6136 0.9692 0.2745
11 1.3711 62.2585 96.4885 81.5948 5.6457 40.6136 0.9699 0.2716
21 1.4019 57.1018 99.0230 81.6528 5.6457 40.6136 0.9722 0.2664
28 1.4360 60.9955 99.8524 84.2957 5.6457 40.6136 0.9739 0.2641
31 1.4557 59.1488 99.9281 84.2545 5.6457 40.6136 0.9730 0.2526
37 1.5096 53.3040 99.7876 83.3649 5.6457 40.6136 0.9731 0.2171
45 1.6093 51.4073 99.5319 84.5047 5.6457 40.6136 0.9733 0.1630
52 1.7381 43.7377 99.9544 85.196 5.6457 40.6137 0.9740 0.1311
59 1.9320 42.9188 99.9477 87.8555 5.6456 40.6137 0.9741 0.1058
66 2.2419 46.2605 99.9596 90.8554 5.6457 40.6136 0.9744 0.0140
74 2.9112 35.3337 100 94.4260 5.6456 40.6137 0.9703 0.0472

Table 3. Evaluation of performance parameters of axial axes brain images.

Gene-symbol Gene-name Temporal lobe (1009)
FuG HG ITG MTG PLP STG TP TG
ATP13A2 ATPase type 13A2 0.7348 0.4904 0.436 0.7994 0.7385 0.9103 0.394 0.7095
ATP13A2 ATPase type 13A2 0.1763 0.0362 0.2209 0.4997 0.1901 0.2735 -0.3926 -0.4575
ATP13A2 ATPase type 13A2 0.5915 0.1901 0.393 0.6296 0.5416 0.6172 -0.1019 0.6599
PLA2G6 phospholipase A2, group VI 0.5474 0.3094 0.7145 0.8728 -0.01 0.3704 0.1645 1.45
PLA2G6 phospholipase A2, group VI 0.6242 0.8049 0.8018 1.037 0.792 0.8634 0.3632 0.7755
PANK2 pantothenate kinase 2 0.7498 0.3183 0.8637 1.0426 0.8359 1.138 0.3497 1.1843
PANK2 pantothenate kinase 2 0.0383 -0.039 0.3525 0.4041 0.1649 0.3654 -0.2511 0.9139
DCAF17 DDB1 and CUL4 associated factor 17 -1.111 -0.685 -0.4477 -0.459 -0.7618 -0.7368 -1.671 -1.5145
DCAF17 DDB1 and CUL4 associated factor 17 -0.857 1.1656 -0.0664 0.1936 0.3913 -0.1311 -0.7693 -1.5939
FA2H fatty acid 2-hydroxylase 0.4312 0.646 0.491 0.7335 0.2482 0.3291 -0.5366 1.0275
FTL ferritin, light polypeptide 0.0957 -0.394 0.4807 0.8951 -0.1633 -0.14 0.9766 0.5527
FTL ferritin, light polypeptide -0.5385 -0.268 -0.3583 -0.463 -0.9754 -0.8111 -0.355 -1.3685
FTL ferritin, light polypeptide 0.1397 -0.139 0.4189 0.7713 0.3844 -0.0619 1.0032 0.1818
FTL ferritin, light polypeptide 0.49 0.1431 0.7961 1.2327 0.7149 0.4299 1.0536 -0.0534
FTL ferritin, light polypeptide 0.7504 0.0252 0.8003 1.2034 0.841 0.5291 1.3541 1.3565
CP ceruloplasmin (ferroxidase) -0.3291 -0.333 -0.0529 0.2578 -0.5072 -0.5076 -0.6631 1.4698
CP ceruloplasmin (ferroxidase) 0.0816 0.3987 0.3147 0.6051 -0.0262 0.1535 -0.3762 0.4853
PANK2 pantothenate kinase 2 -0.3028 -0.43 -0.3236 -0.26 -0.6733 -0.2603 -0.4188 -1.2405

Table 4. Microarray expression data of genes associated with neurogeneration in iron accumulation for case no. 1009.

Gene-symbol Gene-name Temperoal lobe (1015)
FuG HG ITG MTG PLP STG TG
ATP13A2 ATPase type 13A2 0.5445 -0.357 0.0875 0.4397 0.1961 0.1982 0.2435
ATP13A2 ATPase type 13A2 0.0618 -0.872 -0.1538 -0.039 -0.2016 -0.1477 -0.2584
ATP13A2 ATPase type 13A2 0.3145 -0.626 -0.0904 0.1857 -0.0088 -0.0123 -0.095
PLA2G6 phospholipase A2, group VI 0.5189 0.3871 0.1641 0.2536 -0.0653 -0.2302 0.1514
PLA2G6 phospholipase A2, group VI 1.2103 0.9949 0.5537 0.9645 0.7989 0.6287 1.003
PANK2 pantothenate kinase 2 1.6748 -0.238 0.5908 0.9414 0.4263 0.9184 0.4067
PANK2 pantothenate kinase 2 1.5868 -0.552 0.5726 0.9894 0.5368 0.9837 0.757
DCAF17 DDB1 and CUL4 -0.6786 -0.307 -0.897 -0.839 -0.6765 -1.027 -1.1944
DCAF17 DDB1 and CUL4 -0.0529 0.6431 -0.4059 0.224 -0.0494 -0.4334 -0.4133
FA2H fatty acid 2-hydroxylase 0.3648 0.8213 0.1307 0.5309 0.3536 0.1045 0.3888
FA2H fatty acid 2-hydroxylase 0.3977 0.6857 0.1061 0.5587 0.3349 0.0303 0.2702
FTL ferritin, light polypeptide 0.6052 0.2921 0.1319 0.4078 0.5027 0.3071 0.2215
FTL ferritin, light polypeptide -0.4181 -0.675 -0.4477 -0.352 -0.5527 -0.3973 -0.4697
FTL ferritin, light polypeptide 0.2633 -0.416 -0.1057 0.1049 0.218 0.03 -0.1148
FTL ferritin, light polypeptide 0.5806 -0.301 0.1932 0.4578 0.4752 0.3117 0.191
FTL ferritin, light polypeptide 0.7636 -0.093 0.3041 0.5845 0.6223 0.4706 0.3029
CP ceruloplasmin (ferroxidase) -0.4257 -0.536 -0.5864 -0.497 -0.2763 -0.3394 -0.3451
CP ceruloplasmin (ferroxidase) 0.0435 -1.051 -0.55 -0.344 -0.3693 -0.5069 -0.6194
PANK2 pantothenate kinase 2 0.2862 -0.194 0.1842 0.0878 0.2759 0.0815 -0.8658

Table 5. Microarray expression data of Genes associated with neurogeneration in iron accumulation for case no. 1015.

Gene-symbol Gene-name Temporal lobe (2001)
FuG HG ITG MTG PLP PLT STG TP TG
ATP13A2 ATPase type 13A2 0.4312 -0.193 0.1939 0.4136 -0.0757 -0.5313 0.1666 1.4838 -0.3189
ATP13A2 ATPase type 13A2 0.4754 0.1328 0.1887 0.2792 -0.3373 -0.1249 0.1649 0.6876 -0.1354
ATP13A2 ATPase type 13A2 0.4523 -0.007 0.2082 0.3749 -0.2643 -0.3199 0.2253 0.9642 -0.2922
PLA2G6 phospholipase A2, group VI -0.3846 -0.423 -0.5133 -0.28 0.0998 -0.9799 -0.3964 -0.3111 -0.8777
PLA2G6 phospholipase A2, group VI 0.3276 -0.245 0.1529 0.409 0.113 -0.077 0.1013 1.2151 0.0264
PANK2 pantothenate kinase 2 0.8085 0.1588 0.4921 0.6789 0.5108 -0.1138 0.2951 1.8381 -0.2699
PANK2 pantothenate kinase 2 0.0704 0.239 0.0691 0.1224 0.3284 0.1294 0.1837 0.8949 -0.4588
DCAF17 DDB1 and CUL4 associated factor 17 -0.8383 0.0647 -0.7526 -0.698 -1.2485 -0.4934 -0.4171 -1.4248 -0.0066
DCAF17 DDB1 and CUL4 associated factor 17 -1.134 0.1027 0.1722 -0.392 0.4119 1.0846 0.7262 -0.4074 -0.7103
FA2H fatty acid 2-hydroxylase -0.6818 -0.098 -0.5762 -0.48 -0.1964 -0.5395 -0.4046 -0.6577 0.329
FA2H fatty acid 2-hydroxylase -0.8646 0.1281 -0.8381 -0.698 -0.6267 -0.6973 -0.7474 -0.8164 -0.2664
FTL ferritin, light polypeptide 0.2459 -0.052 0.0545 0.2693 1.3181 0.1233 0.2861 0.0608 -0.248
FTL ferritin, light polypeptide -0.3841 -0.643 -0.4763 -0.378 -0.0175 -1.0062 -0.4945 -0.0522 -1.0955
FTL ferritin, light polypeptide 0.2257 0.0631 0.1845 0.2094 0.7076 -0.0336 0.3506 0.2684 -0.5253
FTL ferritin, light polypeptide 0.456 0.186 0.2243 0.4459 0.9101 -0.2046 0.2801 1.1545 -0.476
FTL ferritin, light polypeptide 0.6271 0.2876 0.3862 0.6438 1.1675 -0.0769 0.3875 1.354 -0.5252
CP ceruloplasmin (ferroxidase) -0.5654 -0.829 -0.6971 -0.843 -0.976 -0.9208 -0.8784 1.2616 -0.5649
CP ceruloplasmin (ferroxidase) -0.2464 -0.238 -0.0513 0.1402 0.0332 -0.2134 -0.0372 0.8696 -0.5955
PANK2 pantothenate kinase 2 0.0413 -0.378 -0.176 -0.213 -0.6247 -0.0137 -0.2085 -0.0399 -0.2607

Table 6. Microarray expression data of genes associated with neurogeneration in iron accumulation for case no. 2001.

Results

Image segmentation using discrete curvelet transform with minimum RMSE score (Tables 2 and 3) was done for all MRI images including reference images (case no. 1015, 2001 and 1009). As per shown in Figure 3, DCT is advantageous over DWT based on PSNR and RMSE values. Fuzzy C-means Clustering of images standardized with different clusters. 82% of abnormal images showed temporal lobe localizations (Figures 4 and 6).

biomedres-PSNR-comparison-values

Figure 3: RMSE and PSNR comparison values between DWT and DCT.

biomedres-neurodegeneration-localization

Figure 4: Results for abnormality of patient brain sample on temporal lobe due to neurodegeneration localization in coronal slices of brain images using DCT with spatial fuzzy c means clustering (patient history : aged 42 years with severe alcoholic degeneration).

biomedres-neurdegeneration-brain-images

Figure 5: Evaluation graph of performance parameters of coronal plane neurdegeneration brain images.

biomedres-alcoholic-degeneration

Figure 6: Results for abnormality of patient brain sample on temporal lobe due to neurodegeneration localization in axial slices of brain images using DCT with spatial fuzzy c means clustering (patient history : aged 42 years with severe alcoholic degeneration).

Reference case 1015 with history of neurodegeneration also showed clustering localization as abnormal cells with temporal lobe in both axial and coronal planes. Hence, the main objective is to reduce Root Mean Square Error (RMSE) and also to increase Peak Signal to Noise Ratio (PSNR) and Correlation Coefficient (Tables 2 and 3).

It leads to the accurate measurement of Cerebral Blood Volume, Cerebral Blood Flow and Mean Transmit Time and hence the analysis of brain becomes more accurate. The restored image is segmented into various clusters by using Spatial Fuzzy Clustering. The clusters are formed based on the intensity of the pixels present in the image. The intensity of grey matter, white matter, CSF and neurodegeneration area in the brain will be different and based on which the segmentation is done and grouped into various clusters. Evaluation of performance parameters of Coronal and axial Brain Images were tabulated in Tables 2 and 3. Percentage accuracy, sensitivity, specificity and Residual Difference (RD) were graphed in Figures 5 and 7. Pank2 gene expression of temporal lobe of reference case: 1015 for both axial and coronal MRI images was shown in Figure 8. Microarray gene expression data of all three cases with temporal lobe for particular genes of iron accumulation based neurodegeneration clearly showed variation in expression (Figure 9). The up regulated genes are PANK2, FTL, PLA2G6 and the down regulated genes are CP, ATP13A2. Similar expression patterns are also functionally correlated.

biomedres-performance-parameters

Figure 7: Evaluation graph of performance parameters of axial plane neurodegeneration brain images.

biomedres-coronal-MRI-images

Figure 8: Pank2 gene expression of temporal lobe of reference case: 1015 for both axial and coronal MRI images.

biomedres-Microarray-gene-expression

Figure 9: Microarray gene expression data of all three cases with temporal lobe for particular genes of iron accumulation based neurodegeneration.

Discussion and Conclusion

In this work, we have presented an algorithm aimed at automatically localizing the abnormal cells in MRI images. The first phase roughly segments the brain structure using Discrete Curvelet transform by denoising under consideration with images. In the second phase, the structure is more precisely localized abnormalities using a fuzzy C-means clustering based deformable model that adapts its shape to match the anatomical structure of interest. The method is able to deal with imprecise and incomplete images, and our tests on actual images have been successful with statistically valid up to 90%. Image quality is often affected by various artefacts, such as noise which could make it difficult to analyse or to extract useful information. Basically, the goal of image denoising is to reduce the noise as much as possible, while retaining important features such as edges and fine details. Sensitivity measures the proportion of actual positives which are correctly identified as present in the image. Specificity measures the proportion of actual negatives as present in the image. Accuracy is the measurement of the degree to which the result of measurement is equal to the correct or standard value. From the Tables 2 and 3, it has been observed that the Sensitivity, Specificity and Accuracy are more for the proposed work. Microarray gene expression analysis of localized area also confirms the variations in expression comparatively. It will be helpful in the development of gene modules which will be very needful for molecular studies in the present day neuroimaging advancements. Over the past years, there has been exponential growth in the power and clinical utility of imaging modalities such as MRI to diagnose and characterize disease and to guide clinical management. In parallel, the development of functional genomics tools such as DNA microarrays has provided powerful methods for explaining the molecular basis of disease on a genome-wide level.

Discrete Curvelet transform (DCT) can overcome the disadvantages i.e. isotropic and less coefficients are needed to account for edge and reach better approximation rates. But it is not a mature technology and will be improved in future for reducing complexity and better thresholding function [35]. Similarly the improvements of fuzzy C means are important tool in segmentation of brain images. Various diagnostic studies like mammogram analysis, MRI brain analysis, bone and retinal analysis etc., using neural network approach result in use of back propagation network, probabilistic neural network, and extreme learning machine recurrently [36]. The major limitations of many microarray-based studies continue to be the difficulty of translating molecular findings into clinically useful assays or interventions [2]. As shown in this study, the fusion of imaging and functional genomic datasets offers the potential for a more rapid clinical translation [37]. This non-invasive prognostic biomarker may be useful in clinical management; individuals are shown to differ in their susceptibility to therapies [38]. Although these findings will need to be further characterized and validated the power of the combined radiologic and genomic approach provide a paradigm for rapidly identifying test in the clinical setting. Future work includes automating the configuration step which sets the algorithm parameters. The automatic segmentation of the structure and clustering the segmented areas will lead, to identifying sets of genes whose expression generates similar textural patterns in corresponding regions, since it can be argued that genes with similar expression patterns are also functionally correlated.

Acknowledgement

We gratefully acknowledge Dr. S Balaji, MD in Radiology and Dr. G Suresh, MBBS, DCH, Stanley Medical College, Chennai, Tamilnadu, India for their contribution in providing MRI Images for subjects and also necessary inputs for this work. We also acknowledge Dr. G Sathish, MVSc (Animal Biotechnology), Veterinary Department, Govt. Of Tamilnadu, India for providing inputs for gene expression analysis to complete the work successfully.

References