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The bag-of-visual-words (BoVW) method with construction of a single dictionary of visual words has been used previously for a variety of classification tasks in medical imaging, including the diagnosis of liver lesions. In this paper, we describe a novel method for automated diagnosis of liver lesions in portal-phase computed tomography (CT) images that improves over single-dictionary BoVW methods...
Computed tomography is a popular imaging modality for detecting abnormalities associated with abdominal organs such as the liver, kidney and uterus. In this paper, we propose a novel weighted locality-constrained linear coding (LLC) method followed by a weighted max-pooling method to classify liver lesions into three classes: cysts, metastases, hemangiomas. We first divide the lesions into same-size...
Computer Aided Detection (CAD) in Computed Tomography Colonography (CTC) aims at detecting colonic polyps that are the precursors of cancer. We propose a polyp detection / identification algorithm with a built-in enhancement scheme. The underlying idea of the proposed method is to utilize the nonlinear heat diffusion process, which is closely related to the nonlinear diffusion filtering, to generate...
Image retrieval approaches can assist radiologists by finding similar images in databases as a means to providing decision support. In general, images are indexed using low-level imaging features, and a distance function is used to find the best matches in the feature space. However, using low-level features to capture the appearance of diseases in images is challenging and the semantic gap between...
In order to develop content based image retrieval (CBIR) systems, a robust reference standard of similarity between pairs of images is required, but challenging to create given the large number of pair-wise comparisons. We demonstrated a novel method of creating one for liver tumors seen in 19 portal venous CT scans by computing image similarity from subjective ratings of attributes on single images...
We aim to predict radiological observations using computationally-derived imaging features extracted from CT images. Our dataset consists of 79 portal venous phase liver CT images containing lesions identified and annotated by a radiologist using a controlled vocabulary of 76 semantic terms. Computationally-derived features were extracted describing intensity, texture, shape, and edge sharpness. Linear...
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