Introduction: The quality of CT images used for treatment planning of cancer patients is an important issue in accurate outlining of the tumor volume and organs at risk. Different kernels in CT scanner systems are available for improving the image quality. Applying these kernels  on   CT  images  will  change   the   CT  numbers  and  electron   density   of   tissues, conse...

The mixed convolution kernel alters his properties geographically according to the depicted organ structure, especially for the lung. Therefore, we compared the image quality of the mixed convolution kernel to standard soft and hard kernel reconstructions for different organ structures in thoracic computed tomography (CT) images.Our Ethics Committee approved this prospective study. In total, 31...

OBJECTIVE Conventional CT requires generation of separate images utilizing different convolution kernels to optimize lesion detection. Our goal was to develop and test a hybrid CT algorithm to simultaneously optimize bone and soft-tissue characterization, potentially halving the number of images that need to be stored, transmitted, and reviewed. MATERIALS AND METHODS CT images generated with ...

A fast kernel feature analysis is presented for 3-dimensional computer-aided detection of colonic polyps on CT colonographic images. The proposed algorithm, called Accelerated Kernel Feature Analysis (AKFA), extracts salient features from a sample of unclassified patterns by use of a kernel method. Unlike other kernel-based feature selection algorithms, AKFA iteratively constructs a linear subs...

This work presents a kernel method for clustering the nodes of a weighted, undirected, graph. The algorithm is based on a two-step procedure. First, the sigmoid commute-time kernel (KCT), providing a similarity measure between any couple of nodes by taking the indirect links into account, is computed from the adjacency matrix of the graph. Then, the nodes of the graph are clustered by performin...

The Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule (SPN) remains unclear. 240 patients with SPNs (malignant, n = 180; benign, n = 60) underwent non-contrast CT (NECT) and contrast-enhanced CT (CECT) which were reconstructed with different slice thickness and convolution kern...

BACKGROUND Computed tomography (CT) reconstruction parameters, such as slice thickness and convolution kernel, significantly affect the quantification of hyperaerated parenchyma (VHYPER%). The aim of this study was to investigate the mathematical relation between VHYPER% calculated at different reconstruction settings, in mechanically ventilated and spontaneously breathing patients with differe...

CT assessment of postoperative spine is challenging in the presence of metal streak artifacts that could deteriorate the quality of CT images. In this paper, we studied the influence of different acquisition parameters on the magnitude of metal streaking. A water-bath phantom was constructed with metal insertion similar with postoperative spine assessment. The phantom was scanned with different...

This project is an application of Medical Image Processing. The abdomen CT image was taken as input and anomalies in the liver was analyzed. The clustering algorithm groups the pixels based on the similarity of gray values. Here in this project an improved kernel fuzzy C-mean clustering algorithm with pixel intensity and location information (ILKFCM) is used which will segment the abdominal org...