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Retrieval of Brain Tumors by Adaptive Spatial Pooling and Fisher Vector Representation
- Source :
- PLoS ONE, PLoS ONE, Vol 11, Iss 6, p e0157112 (2016)
- Publication Year :
- 2016
- Publisher :
- Public Library of Science, 2016.
-
Abstract
- Content-based image retrieval (CBIR) techniques have currently gained increasing popularity in the medical field because they can use numerous and valuable archived images to support clinical decisions. In this paper, we concentrate on developing a CBIR system for retrieving brain tumors in T1-weighted contrast-enhanced MRI images. Specifically, when the user roughly outlines the tumor region of a query image, brain tumor images in the database of the same pathological type are expected to be returned. We propose a novel feature extraction framework to improve the retrieval performance. The proposed framework consists of three steps. First, we augment the tumor region and use the augmented tumor region as the region of interest to incorporate informative contextual information. Second, the augmented tumor region is split into subregions by an adaptive spatial division method based on intensity orders; within each subregion, we extract raw image patches as local features. Third, we apply the Fisher kernel framework to aggregate the local features of each subregion into a respective single vector representation and concatenate these per-subregion vector representations to obtain an image-level signature. After feature extraction, a closed-form metric learning algorithm is applied to measure the similarity between the query image and database images. Extensive experiments are conducted on a large dataset of 3604 images with three types of brain tumors, namely, meningiomas, gliomas, and pituitary tumors. The mean average precision can reach 94.68%. Experimental results demonstrate the power of the proposed algorithm against some related state-of-the-art methods on the same dataset.
- Subjects :
- Databases, Factual
Computer science
Kernel Functions
Fisher kernel
lcsh:Medicine
Information Storage and Retrieval
02 engineering and technology
Pituitary neoplasm
Field (computer science)
030218 nuclear medicine & medical imaging
Diagnostic Radiology
Pattern Recognition, Automated
0302 clinical medicine
0202 electrical engineering, electronic engineering, information engineering
Medicine and Health Sciences
Meningeal Neoplasms
lcsh:Science
Operator Theory
Neurological Tumors
Multidisciplinary
Brain Neoplasms
Radiology and Imaging
Glioma
Magnetic Resonance Imaging
Professions
Oncology
Neurology
Kernel (statistics)
Pattern recognition (psychology)
Metric (mathematics)
Physical Sciences
020201 artificial intelligence & image processing
Meningioma
Algorithms
Research Article
Imaging Techniques
Feature extraction
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Fisher vector
Neuroimaging
Research and Analysis Methods
03 medical and health sciences
Region of interest
Diagnostic Medicine
Artificial Intelligence
Radiologists
Image Interpretation, Computer-Assisted
Cancer Detection and Diagnosis
Humans
Pituitary Neoplasms
Image retrieval
business.industry
lcsh:R
Biology and Life Sciences
Cancers and Neoplasms
Pattern recognition
Models, Theoretical
ComputingMethodologies_PATTERNRECOGNITION
Subtraction Technique
People and Places
lcsh:Q
Population Groupings
Artificial intelligence
business
Mathematics
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....b56cb914ecacb5a7f7f2385270e7fa42