Johne’s disease is one of the most widespread bacterial diseases of domestic animals. It induces emaciation, decrease in milk and meat production, diarrhea and death. It causes yearly losses which exceed $1.5 billion in the United States, and its impact on the world economy is enormous. In this thesis an automatic intelligent computer-aided system is proposed for diagnosis of Johne's disease, the system uses image analysis and computer vision techniques to extract features from two different microscopic images and irrelevant features are eliminated. Neural Networks and k-nearest neighbor are used as a classification technique for the extracted features to diagnose the Johne's disease.

The proposed system performs two tests; histopathological examination and the acid fast stain test. Histopathological examination depends on extracting 192 different texture features and then the features are minimized into only 8 features and classified using Artificial Neural Networks. While in the second test, color segmentation is used to detect the bacteria, k-nearest neighbor was used for classification.

The construction and testing of both models are carried out using a total of 294 microscopic images, 194 images for the histopathological examination test which produces an overall accuracy of 98.33%. The other 100 images are used for the acid fast stain test, and its accuracy is 96.97%.