Purpose: To effectively perform quantification of brain normal tissues and pathologies simultaneously, independent component analysis (ICA) coupled with support vector machine (SVM) is investigated and evaluated for effective volumetric measurements of normal and lesion tissues using multispectral MR images. Materials and Methods:Synthetic and real MR data of normal brain and white matter lesion (WML) data were used to evaluate the accuracy and reproducibility of gray matter (GM), white matter (WM), and WML volume measurements by using the proposed ICAþSVM method to analyze three sets of MR images, T1-weighted, T2-weighted, and proton density/fluid-attenuated inversion recovery images.Results: The Tanimoto indexes of GM/WM classification in the normal synthetic data calculated by the ICAþSVM method were 0.82/0.89 for data with 0% noise level. As for clinical MR data experiments, the ICAþSVM method clearly extracted the normal tissues and white matter hyperintensity lesions from the MR images, with low intra-and inter-operator coefficient of variations. Conclusion:The experiments conducted provide evidence that the ICAþSVM method has shown promise and potential in applications to classification of normal and pathological tissues in brain MRI.

From the outcome review of this national mammography screening, there is still room to ameliorate our performance through comprehensive and continued education, to improve the competence of cancer detection and decrease false negative (FN) cases.

Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources to be separated, a dilemma encountered in MR image analysis is that the number of MR images is usually less than the number of signal sources to be blindly separated. As a result, at least two or more brain tissue substances are forced into a single independent component (IC) in which none of these brain tissue substances can be discriminated from another. In addition, since the ICA is generally initialized by random initial conditions, the final generated ICs are different. In order to resolve this issue, this paper presents an approach which implements the over-complete ICA in conjunction with spatial domain-based classification so as to achieve better classification in each of ICA-demixed ICs. In order to demonstrate the proposed over-complete ICA, (OC-ICA) experiments are conducted for performance analysis and evaluation. Results show that the OC-ICA implemented with classification can be very effective, provided the training samples are judiciously selected.

Doppler ultrasound is an adjunct to other imaging modalities in differentiating benign from malignant breast tumors. Two groups of patients with breast nodules were examined using a 10/4.5 MHz (imaging frequency/pulsed Doppler frequency) image-directed Doppler probe and a 7.0/5.0 MHz color Doppler imaging probe, separately. Whenever flow signals were detected within or at the margin of the breast nodule, the lesion was considered to be malignant. In detecting malignant breast tumors, the sensitivity was 77.3% and 94.5%, specificity 83.3% and 40.1%, accuracy 81% and 63.4% for image directed Doppler and color Doppler imaging, respectively. We found color Doppler to be easier and more efficient in detecting the flow signals of neovascularity in breast tumor. Color Doppler exhibits a higher sensitivity in detecting the malignant breast tumors. However, more false-positive diagnoses were made. Color Doppler ultrasound also expedited the examination, and the whole procedure could be shortened from 35 minutes to 8 minutes compared with our previous examination performed by image-directed Doppler ultrasound. Due to its higher sensitivity and saving in examination time, we use color Doppler imaging as a routine procedure when solid lesions are observed in x-ray mammography or sonography, as a supplement to the diagnosis of breast tumors.

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