Content-Based Medical Image Retrieval

A content-based image retrieval system in medical applications has always been the most vivid research areas in recent ten years, but no general breakthrough has been achieved. This thesis aims to bring some highlight in this field. First, it presents a brief introduction of CBIR system and its key techniques, reviews the current research development, the main research methods and the existed CBIR system, and analyzes the features of medical image. And then, centered on the medical application of CBIR, it mainly does some research about the following issues:(1) Segmentation of brain MR images based on GAUSS-MARKOV random field model, (see chapter 2). Image segmentation lays foundation for selecting and measuring the key region of content information, which is advantageous for image retrieval to use key information. Gauss-Markov random field model is the often used image segmentation model, taking advantage of both image intensity and spatial information imposed by Gibbs prior. It can be used to effectively segment the images with high levels of noise. However it is always difficult to confirm the Gibbs penalty factor. As usual, it requires a tedious trial-and-error process. So to solve this problem, this paper defines a class-adaptive penalty factor. It is automatically estimated from the posterior probability and is anisotropic for each class. Furthermore the model iteratively gets their parameters estimation in the EM-MAP algorithm. Finally, by application of this algorithm to medical image segmentation, it is proved effective.(2) Locating region of interest of images based on elastic registration, (see chapter 3). Diagnosing diseases is usually on the basis of the form and size of pathological regions and its anatomic parts. Therefore, the pathological region is also defined as region of interest. To delimit the region of interest is beneficial to CBIR for the purpose of narrowing down information area and getting key retrieval information. By this way, the image information characters for retrieval are tremendously decreased. At present, many CBIRs are capable of offering the method of confirming ROI, but all are realized by hand. For instance, in Blobworld system, firstly, the image to be retrieved is segmented into several areas according to image's various features, such as veins, colors and polarity, and then by the way of interaction, the ROI is determined by hand. This thesis, however, tries to adopt the method based on electric registration to automatically determine it.(3) Features extraction of images based on DT-CWT (see chapter 4). Feature extraction is critical and fundamental to CBIR. The most used methods for feature extraction are: fourier descriptors, moment invariants used to express the shape features, the wavelet and Gabor wavelet used to express the texture feature. But the traditional wavelet bears two deficiencies: shift variance and lack of directionality. Though Gabor wavelet has the excellent directionality, a typical Gabor image analysis is not only expensive to compute and noninvertible, but also yields a great deal of redundant information because of its nonorthogonality. DT-CWT can overcome these deficiencies. It is a good way to abstract image features. Therefore,this thesis analyzes its theory and will adopt it in abstracting image features.(4) Brain MR images retrieval based on DT-CWT and Kullback-leibler distance similarity measure (see chapter 5). After two stages DT-CWT, an image can get 6 sub-band coefficients in 6 directions for every stage, and then each sub-band coefficient histogram is fitted with Generalized Gaussian Density function. Based on matching moment, this thesis tries to estimate parameters under the maximized likelihood rule. By means of it, 12 pairs of parameters which are image characters are abstracted from each image. After getting the features, measuring the similarity of these features is a critical problem. Grounded on the maximized likelihood selective rule, this thesis applies the Kullback-leibler distance using the chain rule to fulfilling it.