Xin Liu - IEEE Xplore Author Profile

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In this paper, we introduce a novel receiver operating characteristic (ROC) analysis method that considers spatial correlation between pixels to evaluate classification algorithms. ROC analysis is one of the most important tools in the evaluation of medical images and computer aided diagnosis (CAD) systems. It provides a comprehensive description of the detection accuracy of the test image. To eva...Show More
In this paper, a new method that incorporates the spatial information to localize prostate cancer with magnetic resonance imaging (MRI) is proposed. Most automated methods for tumor localization require manual peripheral zone extraction from the prostate gland, and it is a tedious and time-consuming job with considerable inter-observer variability. In order to conquer this difficulty, we propose t...Show More
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 t...Show More
In this paper, we present a new statistical model for Maximum Likelihood Classification (MLC) algorithm to improve the image segmentation/classification performance. MLC has been widely used in many classification applications. For supervised MLC, the parameters of the statistical model are obtained from the training dataset at the learning step. However, in the previous studies, the feature value...Show More
Prostate cancer is the second leading cause of cancer death in American men. Current prostate MRI can benefit from automated tumor localization to help guide biopsy, radiotherapy and surgical planning. An important step of automated prostate cancer localization is the segmentation of the prostate. In this paper, we propose a fully automatic method for the segmentation of the prostate. We firstly a...Show More
Prostate cancer is one of the leading causes of death from cancer among men in the United States. Currently, high-resolution magnetic resonance imaging (MRI) has been shown to have higher accuracy than trans-rectal ultrasound (TRUS) when used to ascertain the presence of prostate cancer. As MRI can provide both morphological and functional images for a tissue of interest, some researchers are expl...Show More
Image segmentation is a crucial step in most computer vision tasks. We propose a new unsupervised fuzzy Bayesian image segmentation method using fuzzy Markov random fields (FMRFs). FRMF is known to provide improved segmentation results when compared to the “hard” MRF method. Typically, both hard and fuzzy MRF models have two groups of parameters to be estimated: the MRF parameters and class parame...Show More