Research On Images Analysis Methods For Disease Diagnosis

Our dissertation focus on image analysis methods for disease diagnosis, the image processing techniques which we need to use in implementation of our algorithms is focus on classification and segmentation for three types of images; texture images, remote sens-ing images and medical images. The image processing techniques which used as:wavelet transform, image enhancement, fuzzy logic or neural network, feature extraction, Hough transform for line detection and mathematic methods like Euclidean distance.The aim of our dissertation is to make new algorithms for diseases diagnosis, but first we need to know the best image methods for classification and segmentation in our algorithms which made us to try to use many kinds of images including medical images, to make sure that our way is best way for diagnosis.We made three algorithms for image analysis to reach the disease diagnosis; one is for general image classification that means we use normal and remote sensing images and the two others is for disease diagnosis that means using medical images. The first al-gorithm is having two parts; the first part is for recognition of textures and the second one is for remote sensing images. So in the first part we try to mix between wavelet and neu-ral network to find suitable way for recognition the textures, via wavelet the texture will decompose into sub-images these sub-images will analyse and extract features, these fea-tures will be the key of the neural to recognition the different types of texture, via these features our neural network will recognition the type of the texture. In our work five dif-ferent types of texture have been used, each type has five pictures. The results from our way had more accuracy. In the second part we analysis textures of remote sensing images by taking two reference remote sensing images. By employ the wavelet transform and neural network for analysis and classification respectively. We use (symmlet5) and (ciofletl) mother functions for analyzing the two images that contains water, forest and earth. The images are gray level and (128×128) size. The processing is carried out to di-vide each image into (16) blocks with size (32×32). Each block will be entered to the wavelet mother function, after trying several mother functions, we found that the (Coifl, Sym5) are the best choice. The results are passed to the features extraction (mean, stan-dard deviation, and variance) and the output is then fed as input to the neural network (NN). Finally the result from NN with (Levenberg Marquardt (LM) algorithm) gives the type of texture (forest, earth, and water).The Second algorithm is for Constitutional Jaundice diagnosis, in this algorithm we have made an algorithm to diagnose the constitutional jaundice (Dubin-Johnson, Gil-bert and Rotor syndrome) the algorithm is decomposed into two parts:1) Using wavelet transform to analyse the image; via wavelet transform we collected three features for each kind of disease 2) Calculates the percentage of the gray scales (percentage of white and black colour) for each image via its histogram, it collects two features for each kind of disease. In total there will be five values; these five values will be the inputs for the fuzzy logic that will decide the kind of disease based on these values. We made experiments for 55 cases mostly for children who suffered from different kinds of constitutional jaundice. Our algorithm yields more accurate results compared to the diagnosis by a doctor's eyes only. We collected 55 cases mostly for children that suffered from different kinds of the Constitutional Jaundice.The third algorithm is to diagnose thin basement membrane. The idea of our algo-rithm is based on content based image retrieval. The diagnosis of this disease is depend on measure of the thickness of the membrane, the traditional way for diagnosis is to manually calculate the thickness, so we suggest an automatic algorithm to detect the membrane and calculate thickness, at first the detecting of membrane is decided by doc-tor diagnosis, then we used some pictures to built database, this database will detect the membrane automatically then calculate the thickness to know whether it is normal or ab-normal. Compared with manually ways our algorithm is easy to use and has more accu-racy.