Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Prostate cancer localization with multispectral MRI based on relevance vector machines
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Prostate cancer segmentation with multispectral MRI using cost-sensitive conditional random fields
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
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In this paper, a new method that uses relative contrast is proposed for medical image segmentation problems. Generally, the absolute intensity values of different features are mapped into a comparable range with a normalization method, but the differences across patients are not considered. In order to utilize the patient-specific information from medical images, we use relative contrast between the normal and malignant tissues to perform training. The proposed relative contrast based method mimics the image segmentation procedure performed by human readers based on relative intensity values rather than absolute intensity values. The proposed method requires the knowledge of normal and malignant tissues since it is based on their relative intensities. This is known at the training stage, but unknown for the test data. Therefore, we present an iterative algorithm to estimate the relative contrast based on the current estimate of the class membership for the test data. Our experimental results show that the suggested algorithm outperforms the classical z-score normalization for prostate cancer localization with multispectral MR images.