APLICACIONES DE AUDIO

As m entioned earlier, the Kohonen netw ork is a general-purpose adaptive pattern classification engine which requires unsupervised training. The latter characteristic in conjunction w ith its straightforw ard architecture and robust clustering potential as well as its ability to resolve pattern recognition problems of non-linearly separable classes, constituted some of the main reasons for choosing it for this segmentation task. In the present classification problem the netw ork structure w as the same as that show n in Fig 5.3, but the num ber of inputs was 3 for the set of input vectors: = (R,G,B), 6 w hen the

vectors had the form: Xg = (R,G,B,Mr,Mg,Mb), and 9 w hen x^ = (R,G,B,Mr,Mg,Mb,Or,Cg,Oy). The num ber of outputs was fixed as 3, which is essentially the num ber of classes being classified: BG, BV and TCN. The training was initiated by setting the weights to small, normally-distributed, random values w ith zero mean. As soon as a full-histological image was appeared on the screen, the user was able to select m anually a sub-image, which w as used for training the Kohonen network. After the RGB, MrMgMb and GrOgOy values were calculated for each pixel in the selected sub-image, their values were stored in three different 2D arrays of size Q x N (Q = num ber of pixels in the sample image and N = 3, 6 or 9), and also normalised

so that they fitted on the interval [-1, 1]. This is essentially a common pre­

processing step before training and often very useful if different variables have typical values which differ significantly. In our case the intensity (RGB) or neighbourhood average intensity (MrMgMb) values differed to standard deviation (a^OgOb) values by several orders of m agnitude, and thus it w as necessary for all of them to lie w ithin symmetric bounds, usually taken to be [- 1, 1].

After the training set was built, the netw ork worked in two modes: training mode and application mode. During the training m ode the netw ork adapted itself by running over a stored array of vectors, separately for each of the 3 sets of 2D arrays. Its weights w ere updated according to a m anner similar to

Chapter 5_______________________________________________________ Vascular Image Analysis

neuron was reinforced, while all others retained their values. At this point it should be stressed that w hen the m inim um error limit was reached (usually set to 0.1), the learning process was halted, otherwise the process continued

until it reached a m axim um num ber of iterations (i.e. epochs). If the desired stopping criterion was not reached w ithin the originally determ ined limit of epochs (usually set to 150), the whole process started again in the same m anner but w ith a new set of random w eight values. The latter w as useful in some occasions w here no convergence w as found for the particular group of w eight values set at the beginning of the process, and it provided an alternative of escape from an infinite searching for a global error minimum. After the training stage was complete, the netw ork ran in the application mode. During that step, for every single pixel in the entire histological image the appropriate colour features were extracted, depending on the initial num ber of vector inputs specified by the user, and scaled in the interval [-1, 1].

Each input vector stored in this 2D array was then presented to the netw ork for being classified as one of the three histological patterns according to its closest distance to the output neurons.

5.6

N N Segmentation Results

The Kohonen netw ork segmentation algorithm w as developed in order to exploit the potential to extract vessel sections from full-scale histological images w ith m inim um user interaction and w ithin reasonable time limit. The average time required for training the netw ork in the RGB space w as 6 sec for

the user selected sub-image (average size 109 x 199 pixels), while the application m ode took 3 sec. W hen the num ber of vector inputs w as increased to 6, the overall time for training was 21 sec while the application m ode took 4

sec. For 9 inputs the same procedures required 55 and 32 sec respectively, w hereas the average time required for classification w as 14 sec for 768 x 576 size image data.

In the developed NN system, the training procedure could be taken either offline or on-line. Offline training means that the Kohonen netw ork was

Chapter 5_______________________________________________________ Vascular Image Analysis

trained using the training data saved in a file before segmentation. For online training, a region in the displayed full-histological image was selected as the training set of the N N directly by the m ouse of the computer. The online training set could also be saved for future use.

5.6.1

RGB and HSI Colour Spaces

Initially, it was attem pted to select the most appropriate set of colour features in-between the RGB and HSI colour space. Figure 5.8 displays segmentation results for two large-scale histological images using the tissue section regions show n in Fig. 5.6(a,d) as a training set, for the RGB and HSI colour systems. Histological background is labelled w ith blue, regions of tum our cell nuclei w ith red and blood vessels w ith green. W hen the RGB intensity values were used, pixels belonging to the BV class w ere detected adequately, in contrast to the HSI space w here m any background regions w ere misclassihed as blood vessels (see Fig. 5.8(f)). This is more apparent for the image illustrated in Fig. 5.8(b) which exhibits staining of lower quality com pared to that show n in Fig. 5.8(a). The same phenom enon occurs also at a greater degree, in both colour spaces, for the TCN class, b ut this is not of great importance since our aim was exclusively the correct segmentation of blood vessels. In addition, it is clear that the cancerous cells in Fig. 5.8(b) exhibit very w eak staining so that they can be hardly detected as a separate class, even by eye (see for example the magnified cell-nucleus show n in the rectangular). This could also be concluded from the RGB colour distribution of the sample image, where the BG and TCN clusters are scarcely separable (see Fig. 5.7(d)). Similar results w ere obtained for many other histological images w ith various qualities of staining, where classification was worse w hen pixel values were expressed in HSI. Thus, the RGB space w as selected for investigating further the NN segmentation process.

Chapter 5 Vascular Image Analysis