**4. Implementation of SGLCM, globus for hepatic lesions detection using region of interest**

In constructing the sparse coding for SGLCM, the reduction of the number of intensity levels by quantizing the image to fewer levels of intensity [13] helps increase the speed of computation, with some loss of textural information. An interactive graphical user interface (GUI) region drawing tool was developed for image block size flexibility. Inter-sample distance of *d* = 1, image block size of 12 x 12 pixels and direction *θ*= 0°, 45°, 90° and 135°, were used in the experiment. Fig. 2 shows an ROI drawn on healthy liver texture for NN training. Fig. 3 and Fig. 4 show the ROI image block of 12 x 12 pixels drawn on suspected texture areas of cyst and liver tumor, respectively.

Fig. 2. 12 x 12 ROI block drawn on healthy liver in a MR image of the abdomen.

Fig. 3. 12 x 12 ROI block drawn on suspected liver tumor in a MR image of the abdomen. Liver tumor has irregular shape and has multiple growths tissue.

Co-occurrence matrices for the *θ* = 0° and *θ* = 90° are calculated as illustrated in Fig. 5 and Fig. 6, respectively. A test image of 4 x 4 pixels was used as the input to illustrate the sparse matrix construction. As observed in Fig. 4, each pixel within the test image window becomes

Characterization of Hepatic Lesions

element (2,3).

and so forth.

**the image block** 

tissue characterization is shown in Fig. 7.

**4.2 Implementation and anlaysis** 

coarse textures, the change in the distribution is rapid [16].

Using Grid Computing (Globus) and Neural Networks 275

Similar calculations using SGLCM are evaluated with *θ*=45°, 135°, 180°, 225°, 270°, and 315° as the direction of the reference pixel. If the test image is smaller (e.g 3 x 3 image block), the

Fig. 6. Constructing SGLCM spatial matrix based on *θ* =90°, *d*=1, using 4 x 4 ROI block. A reference pixel of 0 and its neighbour of 0 at the direction of 90° will contribute one count to the matrix element (0,0). Similar to Fig. 4, a reference pixel of 3 and its vertical neighbour of 2 would contribute one count to the matrix element (3,2) and one count to the matrix

It is, in theory, possible to choose three or more pixels in a given direction [15]. However, this becomes extremely unwieldy for calculations and is not an operational procedure. Calculation involving three pixels would be third order, four pixels would be forth order

**4.1 Implementation of SGLCM for hepatic lesions using automated segmentation of** 

An automated segmentation scheme using a flexible image block size for automated liver

Square image blocks of widths of 5, 8 and 10 pixels were used within the liver boundary. The purpose of automated segmentation with these various block sizes was for preliminary diagnosis of the entire liver, without requiring intervention by the user in identifying an ROI.

By using SGLCM, approximately two dozen co-occurrence features can be obtained [16]. Consideration of the number of distance angle relations also will lead to a potentially large number of dependent features. In this study, we restrict the representation to four features, which we hypothesize from Table 1 would provide useful information for texture characterization. These are entropy, correlation, contrast and homogeneity. For soft textures, the second order measurement distributions change very slightly with distance, while for

sum of all the entries in the SGLCM spatial matrix generated would be smaller.

the reference pixel in the position of the matrix, starting from the upper left corner and proceeding to the lower right. The pixels along the right edge of the image have no right hand neighbour, so they are not used in this count.

Fig. 4. 12 x 12 ROI block drawn on cyst in a MR image of the abdomen. cyst is a recently recognized genetic disorder characterized by the appearance of numerous cysts spread throughout the liver. A cyst may be identified as an abnormal fluid-filled sac-like structure.

Fig. 5. Constructing SGLCM spatial matrix based on *θ* =0°, d=1, using 4 x 4 ROI block. Each pixel within the test image becomes the reference pixel of the position in the matrix of the direction of 0°. A reference pixel of 3 and its horizontal neighbour of 2 would contribute one count to the matrix element (3,2) and one count to the matrix element (2,3).

The spatial matrix, 1 0*, P* is constructed by filling in the probability of the combinations of pixels coordinate occurring in the window test image at the direction, denoted as angle, *θ*. The top cell of 1 0*, P* will be filled with the number of times the combination of (0,0) occurs (i.e. amount of times within the image area a pixel with grey level 0 neighboring pixels) falls to the left and right side of another pixel with grey level 0 as the reference pixel. The number of combination of (0,0) that occurs are 4 at the angle direction of 0° with the distance, *d*=1. As such, the sparse matrix constructed corresponds to the size of the test image.

274 Grid Computing – Technology and Applications, Widespread Coverage and New Horizons

the reference pixel in the position of the matrix, starting from the upper left corner and proceeding to the lower right. The pixels along the right edge of the image have no right

Fig. 4. 12 x 12 ROI block drawn on cyst in a MR image of the abdomen. cyst is a recently recognized genetic disorder characterized by the appearance of numerous cysts spread throughout the liver. A cyst may be identified as an abnormal fluid-filled sac-like structure.

Fig. 5. Constructing SGLCM spatial matrix based on *θ* =0°, d=1, using 4 x 4 ROI block. Each pixel within the test image becomes the reference pixel of the position in the matrix of the direction of 0°. A reference pixel of 3 and its horizontal neighbour of 2 would contribute one

The spatial matrix, 1 0*, P* is constructed by filling in the probability of the combinations of pixels coordinate occurring in the window test image at the direction, denoted as angle, *θ*. The top cell of 1 0*, P* will be filled with the number of times the combination of (0,0) occurs (i.e. amount of times within the image area a pixel with grey level 0 neighboring pixels) falls to the left and right side of another pixel with grey level 0 as the reference pixel. The number of combination of (0,0) that occurs are 4 at the angle direction of 0° with the distance, *d*=1. As

count to the matrix element (3,2) and one count to the matrix element (2,3).

such, the sparse matrix constructed corresponds to the size of the test image.

hand neighbour, so they are not used in this count.

Similar calculations using SGLCM are evaluated with *θ*=45°, 135°, 180°, 225°, 270°, and 315° as the direction of the reference pixel. If the test image is smaller (e.g 3 x 3 image block), the sum of all the entries in the SGLCM spatial matrix generated would be smaller.

Fig. 6. Constructing SGLCM spatial matrix based on *θ* =90°, *d*=1, using 4 x 4 ROI block. A reference pixel of 0 and its neighbour of 0 at the direction of 90° will contribute one count to the matrix element (0,0). Similar to Fig. 4, a reference pixel of 3 and its vertical neighbour of 2 would contribute one count to the matrix element (3,2) and one count to the matrix element (2,3).

It is, in theory, possible to choose three or more pixels in a given direction [15]. However, this becomes extremely unwieldy for calculations and is not an operational procedure. Calculation involving three pixels would be third order, four pixels would be forth order and so forth.
