Content based image retrieval of ultrasound liver diseases based on hybrid approach

Citations of this article
Mendeley users who have this article in their library.


Problem statement: In the past few years, immense improvement was obtained in the field of Content-Based Image Retrieval (CBIR). Nevertheless, existing systems still fail when applied to medical image databases. Simple feature-extraction algorithms that operate on the entire image for characterization of color, texture, or shape cannot be related to the descriptive semantics of medical knowledge that is extracted from images by human experts. Approach: In this study, we present a hybrid approach called Support vector machine combined with relevance feedback for the retrieval of liver diseases from Ultrasound (US) images is introduced. SVM and RF are supervised active learning technique used to improve the effectiveness of the retrieval system. Three kinds of liver diseases are identified including cyst, alcoholic cirrhosis and carcinoma. The diagnosis scheme includes four steps: image registration, feature extraction, feature selection and image retrieval. First the ultrasound images are registered in the database based on the modality. Then the features, derived from first order statistics, gray level co-occurrence matrix and fractal geometry, are obtained from the Pathology Bearing Regions (PBRs) among the normal and abnormal ultrasound images. The Correlation Based Feature Selection (CFS) algorithm selects the certain features for the specific diseases and also reduces dimensionality space for classification. Finally, we implement our hybrid approach for retrieval of specific diseases from the database. Results: This hybrid approach can get the query from user and has retrieved both positive and negative samples from the database, by getting feedback in each round from the radiologist is help to improve the retrieval of correct images. Conclusion: The hybrid approach (SVM+RF) comprises several benefits when compared to existing CBIR for medical system by neural network algorithms. Fractal geometry in feature extraction plays crucial role in ultrasound liver image retrieval. CFS also reduce the dimensionality issue during storage. Image registration plays an important role in the retrieval. It reduces the redundancy of retrieval images and increases the response rate. Getting relevance feedback from physician helps to improve the accuracy of retrieval images from the database. © 2012 Science Publications.




Suganya, R., & Rajaram, S. (2012). Content based image retrieval of ultrasound liver diseases based on hybrid approach. American Journal of Applied Sciences, 9(6), 938–945.

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free