Abstract
Modeling low level features to high level semantics in medical imaging is an important aspect in filtering anatomy objects. Bag of Visual Words (BOVW) representations have been proven effective to model these low level features to mid level representations. Convolutional neural nets are learning systems that can automatically extract high-quality representations from raw images. However, their deployment in the medical field is still a bit challenging due to the lack of training data. In this paper, learned features that are obtained by training convolutional neural networks are compared with our proposed hand-crafted HSIFT features. The HSIFT feature is a symmetric fusion of a Harris corner detector and the Scale Invariance Transform process (SIFT) with BOVW representation. The SIFT process is enhanced as well as the classification technique by adopting bagging with a surrogate split method. Quantitative evaluation shows that our proposed hand-crafted HSIFT feature outperforms the learned features from convolutional neural networks in discriminating anatomy image classes.
Highlights
Medical images acquired from various imaging sources play an extremely important role in a healthcare facility’s diagnostic process
The proposed feature representation technique is robust because of its abilities to perform the recognition tasks for different domains such as modality and anatomy. The novelty of this feature representation, compared to existing representations are several fold: (1) The modification of Scale Invariant Feature Transform (SIFT) descriptor by embedding the Harris corner as keypoint detection technique has led to the generalized feature representation that can be applied for various medical image classification tasks as carried out in this research for multiple modalities and multiple anatomical images
The use of Harris corner to replace the first two steps of SIFT process has the advantage of assigning orientation to each keypoint that provides rotation invariance while at the same time helps to deal with viewpoint changes
Summary
Classification of Anatomy Objects. BESE, King Abdullah University of Science and Technology, Jeddah 23955, Saudi Arabia Department of Management Information Systems, College of Business Administration, King Faisal Department of Computer Science & Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
