A novel method for breast cancer prognosis using wavelet packet based neural network

Hybrid Discrete Wavelet Transform and Gabor Filter Banks Processing for Features Extraction from Biomedical Images

A new methodology for automatic feature extraction from biomedical images and subsequent classification is presented. The approach exploits the spatial orientation of high-frequency textural features of the processed image as determined by a two-step process. First, the two-dimensional discrete wavelet transform (DWT) is applied to obtain the HH high-frequency subband image. Then, a Gabor filter bank is applied to the latter at different frequencies and spatial orientations to obtain new Gabor-filtered image whose entropy and uniformity are computed. Finally, the obtained statistics are fed to a support vector machine (SVM) binary classifier. The approach was validated on mammograms, retina, and brain magnetic resonance (MR) images. The obtained classification accuracies show better performance in comparison to common approaches that use only the DWT or Gabor filter banks for feature extraction.

Enhancement of visual perception with use of dynamic cues

Institutional review board approval and signed informed consent were not needed, as medical images included in public databases were used in this study. The purpose of this study was to improve the detection of microcalcifications on mammograms and lung nodules on chest radiographs by using the dynamic cues algorithm and the motion and flickering sensitivity of the human visual system (HVS). Different sets of mammograms from the Mammographic Image Analysis Society database and chest radiographs from the Japanese Society of Radiological Technology database were presented statically, as is standard, and in a video sequence generated with the dynamic cues algorithm. Nine observers were asked to rate the presence of abnormalities with a five-point scale (1, definitely not present; 5, definitely present). The data were analyzed with receiver operating characteristic (ROC) techniques and the Dorfman-Berbaum-Metz method. The video sequence generated with the dynamic cues algorithm increased the rate of detection of microcalcifications by 10.2% (P = .002) compared with that obtained with the standard static method, as measured by the area under the ROC curve. Similar results were obtained for lung nodules, with an increase of 12.3% (P = .0054). The increase in the rate of correct detection did not come just from the image contrast change produced by the algorithm but also from the fact that image frames generated with the dynamic cues algorithm were put together in a video sequence so that the motion sensitivity of the HVS could be used to facilitate the detection of low-contrast objects.

SUPPLEMENTAL MATERIAL

http://radiology.rsnajnls.org/cgi/content/full/250/2/551/DC1.