1. For each infected erythrocyte sub-image, generate its RGB and HSI colour histograms and compute the first five statistical moments for each histogram.

2. Use RGB and HSI colour components of infected erythrocyte sub-images to compute four statistical texture measures namely;

i. R-measure ii. 3^rd moment iii. Uniformity iv. Entropy 3. Threshold the infected erythrocyte sub-image using the first threshold value, T₁ obtained from Zack’s algorithm to produce a binary image of the infected erythrocyte and use this image to compute the following features;

i. Infected erythrocyte relative size, S_if. This is obtained as follows;

whereI_area = the number of foreground pixels in an infected erythrocyte nI_area = the number of foreground pixels in a non-infected erythrocyte ii. First five statistical moments of the infected erythrocyte shape signature iii. Eccentricity of the erythrocyte iv. Compactness v. Roundness vi. Aspect ratio vii. Form factor viii. Solidity ix. Convexity x. Extent xi. Erythrocyte centroid 4. Threshold the infected erythrocyte sub-image using the second threshold value, T₂ obtained by zack’s algorithm to produce a binary image of the potential Plasmodium parasite. Use this binary image to compute the following features;

i. Relative size of the parasite. This is given by the following expression;

where Ap is the area of the parasitized region and A.I.Eis the total area of the infected erythrocytes.

ii. Eccentricity of the parasite iii. Compactness iv. Solidity v. Convexity vi. Aspect ratio vii. Form factor viii. Extent ix. Roundness x. Number of nucleated objects xi. Separation distances of the nucleated objects xii. Distances of the nucleated object from the centroid of the infected erythrocyte 5. Form a feature vector of each infected erythrocyte sub-image using features obtained from steps 1, 2, 3, and 4 above.

6. Group these feature vectors in four categories based on the Plasmodium species infecting the erythrocyte 7. Train a multilayer ANN using the features of step six above as the training set.

8. Vary the number of neurons in the hidden layer of the ANN and record the performance of the resulting classifier.

9. Determine the best performance obtained in step 8 above. This is the classification accuracy of the ANN.