Medical Image Classification Based On Local Feature

In the last ten years, there has been an explosion in the number of images acquired every day in modern hospital. How to retrieve the relevant information from the large amount of data is an urgent problem that need to solve. Medical image classification is a effective approach to improve the performance of image retrieval. Traditional manual medical image classification is time-consuming. Hence, automatic medical image classification become an important research problem.At present, local features based image classification has been widely used because of its good performance. Local features based medical image classification mainly consists of the follow steps:local features extraction, visual vocabulary construction, image encoding, and classifier training. At the beginning, this paper reviews the state of the art of medical image classification based on local feature, and introduce some basic theory of classification method. Then, we compare some different sampling methods of local features and assign method of visual words in medical classification. After that, for apply to lager dataset, several improved K-Means clustering algorithms are analyzed and compared, which can improve the efficiency of dictionary construction and query. Finally, sparse coding and one of its improved variants are analyzed and used for medical image classification.In this paper, the main contributions include:1.Different sampling methods for Patch and SIFT features are compared by experiments, and also are different assignment methods of visual words. The experimental results show that dense-grid sampled SIFT feature with soft assignment can get the best performance among the compared methods in medical image classification task.2. Several K-Means based clustering algorithms are analyzed, which are traditional K-Means, hierarchical K-Means and approximation K-Means. Their efficiency on dictionary construction and search are compared by experiments. The experimental results indicate that hierarchical K-Means and approximation K-Means can greatly improve the efficiency of dictionary construction and searching, thus can adapt to lager dataset. In addition, approximate K-Means can get higher search speed than hierarchical K-Means.3.Sparse coding and its variants methods--locality-constrained linear coding are analyzed and compared with traditional K-Means for image coding. The experimental results show that the two sparse coding methods can get better performance with linear SVM than K-Means with non-linear SVM.