82,340 research outputs found
A new query dependent feature fusion approach for medical image retrieval based on one-class SVM
With the development of the internet, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the medical images in the content-based ways through automatically extracting visual information of the medical images. Since a single feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Furthermore, a special feature is not equally important for different image queries since a special feature has different importance in reflecting the content of different images. However, most existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, based on multiply query samples provided by the user, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. The proposed query dependent feature fusion method for medical image retrieval can learn different feature fusion models for different image queries, and the learned feature fusion models can reflect the different importance of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /
Medical image retrieval with query-dependent feature fusion based on one-class SVM
Due to the huge growth of the World Wide Web, medical images are now available in large numbers in online repositories, and there exists the need to retrieval the images through automatically extracting visual information of the medical images, which is commonly known as content-based image retrieval (CBIR). Since each feature extracted from images just characterizes certain aspect of image content, multiple features are necessarily employed to improve the retrieval performance. Meanwhile, experiments demonstrate that a special feature is not equally important for different image queries. Most of existed feature fusion methods for image retrieval only utilize query independent feature fusion or rely on explicit user weighting. In this paper, we present a novel query dependent feature fusion method for medical image retrieval based on one class support vector machine. Having considered that a special feature is not equally important for different image queries, the proposed query dependent feature fusion method can learn different feature fusion models for different image queries only based on multiply image samples provided by the user, and the learned feature fusion models can reflect the different importances of a special feature for different image queries. The experimental results on the IRMA medical image collection demonstrate that the proposed method can improve the retrieval performance effectively and can outperform existed feature fusion methods for image retrieval.<br /
Multi-modal Medical Neurological Image Fusion using Wavelet Pooled Edge Preserving Autoencoder
Medical image fusion integrates the complementary diagnostic information of
the source image modalities for improved visualization and analysis of
underlying anomalies. Recently, deep learning-based models have excelled the
conventional fusion methods by executing feature extraction, feature selection,
and feature fusion tasks, simultaneously. However, most of the existing
convolutional neural network (CNN) architectures use conventional pooling or
strided convolutional strategies to downsample the feature maps. It causes the
blurring or loss of important diagnostic information and edge details available
in the source images and dilutes the efficacy of the feature extraction
process. Therefore, this paper presents an end-to-end unsupervised fusion model
for multimodal medical images based on an edge-preserving dense autoencoder
network. In the proposed model, feature extraction is improved by using wavelet
decomposition-based attention pooling of feature maps. This helps in preserving
the fine edge detail information present in both the source images and enhances
the visual perception of fused images. Further, the proposed model is trained
on a variety of medical image pairs which helps in capturing the intensity
distributions of the source images and preserves the diagnostic information
effectively. Substantial experiments are conducted which demonstrate that the
proposed method provides improved visual and quantitative results as compared
to the other state-of-the-art fusion methods.Comment: 8 pages, 5 figures, 6 table
Magnetic-Visual Sensor Fusion-based Dense 3D Reconstruction and Localization for Endoscopic Capsule Robots
Reliable and real-time 3D reconstruction and localization functionality is a
crucial prerequisite for the navigation of actively controlled capsule
endoscopic robots as an emerging, minimally invasive diagnostic and therapeutic
technology for use in the gastrointestinal (GI) tract. In this study, we
propose a fully dense, non-rigidly deformable, strictly real-time,
intraoperative map fusion approach for actively controlled endoscopic capsule
robot applications which combines magnetic and vision-based localization, with
non-rigid deformations based frame-to-model map fusion. The performance of the
proposed method is demonstrated using four different ex-vivo porcine stomach
models. Across different trajectories of varying speed and complexity, and four
different endoscopic cameras, the root mean square surface reconstruction
errors 1.58 to 2.17 cm.Comment: submitted to IROS 201
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