Breast cancer detection from thermal images using bispectral invariant features

Tavakol, Mahnaz, Chandran, Vinod, Ng, E. Yin Kwee, & Kafieh, Raheleh (2013) Breast cancer detection from thermal images using bispectral invariant features. International Journal of Thermal Sciences, 69, pp. 21-36.

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Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images.

In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

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16 citations in Scopus
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12 citations in Web of Science®

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ID Code: 59275
Item Type: Journal Article
Refereed: Yes
Keywords: Adaboost, Breast cancer, Higher order spectra, Invariant, Thermal images
DOI: 10.1016/j.ijthermalsci.2013.03.001
ISSN: 1290-0729
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 Elsevier Masson SAS
Deposited On: 25 Apr 2013 22:21
Last Modified: 07 May 2013 04:01

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