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Prostate cancer characterization on MR images using fractal features. | LitMetric

Prostate cancer characterization on MR images using fractal features.

Med Phys

Inserm, U703, Université Nord de France, 152 rue du Docteur Yersin, 59120 Loos, CHRU Lille, France.

Published: January 2011

AI Article Synopsis

  • The study focuses on using computerized techniques to detect prostate cancer in T2-weighted MRI scans.
  • Researchers combined fractal and multifractal analysis to enhance the textural evaluation of the images, employing Support Vector Machine (SVM) and AdaBoost for classification.
  • Results showed high detection accuracy with SVM (83% sensitivity, 91% specificity) and AdaBoost (85% sensitivity, 93% specificity), outperforming traditional texture analysis methods and demonstrating robustness to signal variations.

Article Abstract

Purpose: Computerized detection of prostate cancer on T2-weighted MR images.

Methods: The authors combined fractal and multifractal features to perform textural analysis of the images. The fractal dimension was computed using the Variance method; the multifractal spectrum was estimated by an adaptation of a multifractional Brownian motion model. Voxels were labeled as tumor/nontumor via nonlinear supervised classification. Two classification algorithms were tested: Support vector machine (SVM) and AdaBoost.

Results: Experiments were performed on images from 17 patients. Ground truth was available from histological images. Detection and classification results (sensitivity, specificity) were (83%, 91%) and (85%, 93%) for SVM and AdaBoost, respectively.

Conclusions: Classification using the authors' model combining fractal and multifractal features was more accurate than classification using classical texture features (such as Haralick, wavelet, and Gabor filters). Moreover, the method was more robust against signal intensity variations. Although the method was only applied to T2 images, it could be extended to multispectral MR.

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Source
http://dx.doi.org/10.1118/1.3521470DOI Listing

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