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Problem statement: In this study a skin disease diagnosis system was developed and tested. The system was used for diagnosis of psoriases skin disease. Approach: Present study relied on both skin color and texture features (features derives from the GLCM) to give a better and more efficient recognition accuracy of skin diseases. We used feed forward neural networks to classify input images to be psoriases infected or non psoriasis infected. Results: The system gave very encouraging results during the neural network training and generalization face. Conclusion: The aim of this worked to evaluate the ability of the proposed skin texture recognition algorithm to discriminate between healthy and infected skins and we took the psoriasis disease as example. Key words: Skin recognition, skin texture, computer aided disease diagnosis, texture analysis, neural networks, psoriasis, INTRODUCTION With advance of medical imaging technologies (including instrumentation, computer and algorithm), the acquired data information is getting so rich toward beyond the human's capability of visual recognition and efficient [...]

Problem Statement: A novel method for dominant skeleton extraction of textures using different nonlinear wavelet transforms is proposed in this study. In the present study 3x3 masks are used for extraction of skeleton primitives. For a 3x3 skeleton primitive there will be [2.sup.9] skeleton primitive combinations. But the present study considered a skeleton primitive for the skeletonization purpose if and only if its center pixel is one and skeleton primitives are represented by the corresponding skeleton primitive weight. By this, there will be 28 combinations of skeleton primitives. Approach: The skeleton primitive are used for evaluating skeleton points. The skeleton of an object has the property that it is reduced to one point when the skeleton primitive used for the skeletonization is exactly homothetic to the object. The dominant skeleton subset is evaluated by counting the skeleton points. The skeleton subset that leads to the least skeleton points will be the resultant skeleton subset. The present study classified the textures based on two methods. In the first method textures are classified based on skeleton primitive weight, which is nothing but based on skeleton primitive combination. In the second method classification is made based on distance function of skeleton points. Results: The proposed method is applied on 24 Brodatz textures using the three nonlinear wavelet transformed textures. By this the dominant skeleton primitive weight is obtained for each texture. Based on the number of skeleton points distance measures are calculated based on which texture classification is obtained. Conclusions: The two methods are applied on Brodatz textures using different nonlinear wavelet transforms which classified the textures. The first method is appropriate if one need to classify based on skeleton subsets. The second method is appropriate if the classification is to be done based on least number of skeleton points. Keywords: Dominant skeleton subset, combinations of skeleton primitives, homothetic, skeleton points, nonlinear wavelets, INTRODUCTION Texture is the term used to characterize the surface of a given phenomenon in an image and it is undoubtedly one of the principal features used in image processing. [...]

Breast cancer accounts for the second most cancer diagnoses among women and the second most cancer deaths in the world. In fact, more than 11000 women die each year, all over the world, because this disease. The automatic breast cancer diagnosis is a very important purpose of medical informatics researches. Some researches has been oriented to make automatic the diagnosis at the step of mammographic diagnosis, some others treated the problem at the step of cytological diagnosis. In this work, we describes the current state of the ongoing the BC automated diagnosis research program. It is a software system that provides expert diagnosis of breast cancer based on three step of cytological image analysis. The first step is based on segmentation using an active contour for cell tracking and isolating of the nucleus in the studied image. Then from this nucleus, have been extracted some textural features using the wavelet transforms to characterize image using its texture, so that malign texture can be differentiated from benign on the assumption that tumoral texture is different from the texture of other kinds of tissues. Finally, the obtained features will be introduced as the input vector of a Multi-Layer Perceptron (MLP), to classify the images into malign and benign ones. Key words: Breast Cancer, GVF-Snake, Segmentation, Wavelet feature extraction, Neural Network, MLP classification., INTRODUCTION Medical automatic diagnosis is still considered as a hard task. In fact, medical diagnosis requires an expert able to cope with the uncertain cases only by eyeing the visible [...]