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We have developed a three-dimensional (3-D) computer-aided diagnosis scheme for automated detection of colonic polyps in computed tomography (CT) colonographic data sets, and assessed its performance based on colonoscopy as the gold standard. In this scheme, a thick region encompassing the entire colonic wall is extracted from an isotropic volume(More)
Computed tomographic colonography (CTC), also known as virtual colonoscopy, is an emerging alternative technique for screening of colon cancers. CTC uses CT to provide a series of cross-sectional images of the colon for detection of polyps and masses. Fecal tagging is a means of labeling of residual feces by an oral contrast agent for improving the accuracy(More)
PURPOSE A massive-training artificial neural network (MTANN) has been developed for the reduction of false positives (FPs) in computer-aided detection (CADe) of polyps in CT colonography (CTC). A major limitation of the MTANN is the long training time. To address this issue, the authors investigated the feasibility of two state-of-the-art regression models,(More)
This book is a detailed description of the basics of 3-dimensional (3D) digital image processing, in particular the processing of images from techniques such as CT, MRI, and nuclear emission tomography. audience is primarily graduate students and scientists in biomedical engineering and medical physics. The book is divided into 7 chapters. The first part of(More)
One of the limitations of the current computer-aided detection (CAD) of polyps in CT colonography (CTC) is a relatively large number of false-positive (FP) detections. Rectal tubes (RTs) are one of the typical sources of FPs because a portion of a RT, especially a portion of a bulbous tip, often exhibits a cap-like shape that closely mimics the appearance(More)
Colon cancer is one of the leading causes of cancer deaths in the United States. However, most colon cancers can be prevented if precursor colonic polyps are detected and removed. An advanced computer-aided diagnosis (CAD) scheme was developed for the automated detection of polyps at computed tomographic (CT) colonography. A region encompassing the colonic(More)
We have developed a new computer-aided diagnosis scheme for automated detection of lung nodules in digital chest radiographs based on a combination of morphological features and the wavelet snake. In our scheme, two processes were applied in parallel to reduce the false-positive detections after initial nodule candidates were selected. One process consisted(More)
A novel technique for optimizing the wavelet transform to enhance and detect microcalciications in mam-mograms was developed based on the supervised learning method. In the learning process, a cost function is formulated to represent the diierence between a desired output and the reconstructed image obtained from weighted wavelet coeecients for a given(More)