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Prostate cancer spectral multifeature analysis using TRUS images
- Source :
- IEEE transactions on medical imaging. 27(4)
- Publication Year :
- 2008
-
Abstract
- This paper focuses on extracting and analyzing different spectral features from transrectal ultrasound (TRUS) images for prostate cancer recognition. First, the information about the images' frequency domain features and spatial domain features are combined using a Gabor filter and then integrated with the expert radiologist's information to identify the highly suspicious regions of interest (ROIs). The next stage of the proposed algorithm is to scan each identified region in order to generate the corresponding 1-D signal that represents each region. For each ROI, possible spectral feature sets are constructed using different new geometrical features extracted from the power spectrum density (PSD) of each region's signal. Next, a classifier-based algorithm for feature selection using particle swarm optimization (PSO) is adopted and used to select the optimal feature subset from the constructed feature sets. A new spectral feature set for the TRUS images using estimation of signal parameters via rotational invariance technique (ESPRIT) is also constructed, and its ability to represent tissue texture is compared to the PSD-based spectral feature sets using the support vector machines (SVMs) classifier. The accuracy obtained ranges from 72.2% to 94.4%, with the best accuracy achieved by the ESPRIT feature set.
- Subjects :
- Male
Computer science
Feature extraction
Feature selection
Sensitivity and Specificity
Pattern Recognition, Automated
Prostate cancer
Gabor filter
Image texture
Artificial Intelligence
Image Interpretation, Computer-Assisted
medicine
Humans
Computer vision
Electrical and Electronic Engineering
Ultrasonography
Radiological and Ultrasound Technology
Contextual image classification
business.industry
Rectum
Prostatic Neoplasms
Reproducibility of Results
Pattern recognition
medicine.disease
Image Enhancement
Computer Science Applications
Support vector machine
Feature (computer vision)
Computer Science::Computer Vision and Pattern Recognition
Frequency domain
Artificial intelligence
business
Software
Algorithms
Subjects
Details
- ISSN :
- 1558254X
- Volume :
- 27
- Issue :
- 4
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging
- Accession number :
- edsair.doi.dedup.....0f6ddadc96d6bf776544ee5fa3fc3572