An efficient algorithm is proposed for interactive ultrasound image retrieval using magnitude frequency spectrum (MFS). The interactive retrieval is especially intended to be useful for training an intern to diagnose with ultrasound images. In the retrieval process, information on which are relevant to a query image among object images retrieved in the previous iteration is fed back by user interaction. In order to improve discrimination between a query image and each of object images in a database (DB) by using the MFS, which is powerful for ultrasound image retrieval, we incorporate feature vector normalization and root filtering in feature extraction. To effectively integrate the feedback information, we use a feedback scheme based on Rocchio equation, where the feature of a query image is replaced with the weighted average of the feature of a query image and those of object images. Experimental results for real ultrasound images show that while yielding a precision of about 75% at a recall of about 8% in the initial retrieval, the interactive procedure yields a great performance improvement, that is, a precision of about 95% in the third iteration.
In this paper, we propose an efficient algorithm for organ recognition in ultrasound images using log power spectrum. The main procedure of the algorithm consists of feature extraction and feature classification. In the feature extraction, as a translation invariant feature, log power spectrum is used for extracting the information on the echo of organ tissues from a preprocessed input image. In the feature classification, Mahalanobis distance is used as a measure of the similarity between the feature of an input image and the representative feature of each class. Experimental results for real ultrasound images show that the proposed algorithm yields the maximum 30% improvement of recognition rate over the recognition algorithm using power spectrum and Euclidean distance, and results in 10-40% improvement of recognition rate over the recognition algorithm using weighted quefrency complex cepstrum.
We propose an efficient method for content-based ultrasound image retrieval using magnitude frequency spectrum and implement an ultrasound image retrieval system based on the proposed method. The target images are ultrasound images of adult organs. A trained staff often acquires such images so that images of the same kind of organs are very similar, although their locations may not exactly coincide. Therefore, the magnitude frequency spectrum, which has a translation-invariant property, is used as a feature for content-based retrieval. A test image database is composed of real ultrasound images. As a retrieval result, a specified number of highly similar target images are retrieved from all the target images. If all the target images in the database are pre-classified into organs of the same kind, the retrieved images are selected among the images whose class is the same as that of the highest similarity image. Experimental results of the proposed method is superior to other methods. The proposed method especially yields further performance improvement by using the pre-classification. Moreover, it is found from the experimental results that the magnitude frequency spectrum method is robust to the speckle noise that usually exists in ultrasound images.