The image segmentation method based on appearance-model has high computational complexity in iterative positioning feature points, and it is difficult to optimize the nonlinear local feature. To solve these above problems and locate feature points of left ventricular endocardium and epicardium, a gradient decent algorithm based on supervised learning was proposed, a multi-resolution pyramid model of 4 levels was built, and a new feature extraction function based on Bhattacharyya coefficient, namely B-SIFT, was used to replace the Scale Invariant Feature Transform (SIFT) feature in the original method. Firstly, the training set images were normalized to unify the size of each TransEsophageal Echocardiography (TEE). Then the supervised descent model based on B-SIFT and multi-resolution pyramid was built to get a gradient descent direction sequence that approaches the actual values. Finally, the learned direction sequence was applied to the test set to obtain the segmentation results of left ventricular. The experimental results show that compared with the traditional gradient decent method based on supervised learning, the average segmentation error of the proposed method is reduced by 47%, and the iteration results are more closer to the actual values compared with the single-scale method.

Feature extraction is a key step of image retrieval and image registration, but the single feature can not express the information of medical images efficiently. To overcome this shortcoming, a new algorithm for medical image retrieval combining global features with local features was proposed based on the characteristics of medical images. First, after studying the medical image retrieving techniques with single feature, a new retrieval method was proposed by considering global feature and relevance feedback. Then to optimize the Scale-Invariant Feature Transform (SIFT) features, an improved SIFT features extraction and matching algorithm was proposed. Finally, in order to ensure the accuracy of the results and improve the retrieval result, local features were used for stepwise refinement. The experimental results on general Digital Radiography (DR) images prove the effectiveness of the proposed algorithm.