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22 May 2001 New features for detecting cervical precancer using hyperspectral diagnostic imaging
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Proceedings Volume 4255, Clinical Diagnostic Systems; (2001)
Event: BiOS 2001 The International Symposium on Biomedical Optics, 2001, San Jose, CA, United States
Principal component analysis (PCA) in the wavelet domain provides powerful new features for the non-invasive detection of cervical intraepithelial neoplasia (CIN) using fluorescence imaging spectroscopy. These features are known as principal wavelet components (PWCs). The multiscale structure of the fluorescence spectrum for each pixel of the hyperspectral data cube is extracted using the continuous wavelet transform. PCA is then used to compress and denoise the wavelet representation for presentation to a feed- forward neural network for tissue classification. Using PWC features as inputs to a 5-class NN resulted in average correct classification rates of 95% over five cervical tissue classes corresponding to low-grade dysplasia, squamous, columnar, metaplasia plus a fifth class for other unspecified tissue types, blood and mucus. A 2-class NN was also trained to discriminate between CIN1 and normal tissue with sensitivity and specificity of 98% and 99%, respectively. All performance assessments were based on test data from a set of patients not seen during NN training. Trained neural classifiers were used to `compress' and transform 3D hyperspectral data cubes into 2D color-coded images that accurately mapped the spatial distribution of both normal and dysplastic tissue over the surface of the entire cervix.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gordon S. Okimoto, Mary F. Parker, Gregory C. Mooradian, Steven J. Saggese, Ames A. Grisanti, Dennis M. O'Connor, and Kunio Miyazawa "New features for detecting cervical precancer using hyperspectral diagnostic imaging", Proc. SPIE 4255, Clinical Diagnostic Systems, (22 May 2001);

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