A method to measure slice sensitivity profiles of CT images under low-contrast and high-noise conditions

2019 ◽  
Vol 60 ◽  
pp. 100-110
Author(s):  
Mitsunori Goto ◽  
Chiaki Tominaga ◽  
Masaaki Taura ◽  
Hiroki Azumi ◽  
Kazuhiro Sato ◽  
...  
Keyword(s):  
Ct Images ◽  
High Noise ◽  
Low Contrast

Tilted-wire method for measuring resolution properties of CT images under extremely low-contrast and high-noise conditions

2018 ◽  
Vol 11 (2) ◽  
pp. 125-137 ◽  
Author(s):  
Chiaki Tominaga ◽  
Hiroki Azumi ◽  
Mitsunori Goto ◽  
Masaaki Taura ◽  
Noriyasu Homma ◽  
...  
Keyword(s):  
Ct Images ◽  
High Noise ◽  
Low Contrast ◽  
Wire Method

scholarly journals Contrast Enhancement Method Based on Gray and Its Distance Double-Weighting Histogram Equalization for 3D CT Images of PCBs

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Lei Zeng ◽  
Bin Yan ◽  
Weidong Wang
Keyword(s):  
Printed Circuit Boards ◽  
Histogram Equalization ◽  
Ct Images ◽  
3D Ct ◽  
Circuit Boards ◽  
Beam Hardening ◽  
Metal Artifacts ◽  
Low Contrast ◽  
Image Histogram ◽  
Enhancement Method

Cone beam computed tomography (CBCT) is a new detection method for 3D nondestructive testing of printed circuit boards (PCBs). However, the obtained 3D image of PCBs exhibits low contrast because of several factors, such as the occurrence of metal artifacts and beam hardening, during the process of CBCT imaging. Histogram equalization (HE) algorithms cannot effectively extend the gray difference between a substrate and a metal in 3D CT images of PCBs, and the reinforcing effects are insignificant. To address this shortcoming, this study proposes an image enhancement algorithm based on gray and its distance double-weighting HE. Considering the characteristics of 3D CT images of PCBs, the proposed algorithm uses gray and its distance double-weighting strategy to change the form of the original image histogram distribution, suppresses the grayscale of a nonmetallic substrate, and expands the grayscale of wires and other metals. The proposed algorithm also enhances the gray difference between a substrate and a metal and highlights metallic materials. The proposed algorithm can enhance the gray value of wires and other metals in 3D CT images of PCBs. It applies enhancement strategies of changing gray and its distance double-weighting mechanism to adapt to this particular purpose. The flexibility and advantages of the proposed algorithm are confirmed by analyses and experimental results.

A Novel Method for Low-Contrast and High-Noise Vessel Segmentation and Location in Venipuncture

2017 ◽  
Vol 36 (11) ◽  
pp. 2216-2227 ◽  
Author(s):  
Yuhe Li ◽  
Zhendong Qiao ◽  
Shaoqin Zhang ◽  
Zhenhuan Wu ◽  
Xueqin Mao ◽  
...  
Keyword(s):  
Vessel Segmentation ◽  
High Noise ◽  
Low Contrast ◽  
Novel Method

Prediction of major torso organs in low-contrast micro-CT images of mice using a two-stage deeply supervised fully convolutional network

2019 ◽  
Vol 64 (24) ◽  
pp. 245014 ◽  
Author(s):  
Hongkai Wang ◽  
Ye Han ◽  
Zhonghua Chen ◽  
Ruxue Hu ◽  
Arion F Chatziioannou ◽  
...  
Keyword(s):  
Ct Images ◽  
Micro Ct ◽  
Two Stage ◽  
Convolutional Network ◽  
Low Contrast ◽  
Fully Convolutional Network

scholarly journals Statistical Analysis of Haralick Texture Features to Discriminate Lung Abnormalities

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Nourhan Zayed ◽  
Heba A. Elnemr
Keyword(s):  
Mathematical Method ◽  
Statistical Analysis ◽  
Pulmonary Edema ◽  
Statistical Evaluation ◽  
Lung Tumor ◽  
Texture Features ◽  
Tissue Type ◽  
High Noise ◽  
Low Contrast ◽  
Lung Abnormalities

The Haralick texture features are a well-known mathematical method to detect the lung abnormalities and give the opportunity to the physician to localize the abnormality tissue type, either lung tumor or pulmonary edema. In this paper, statistical evaluation of the different features will represent the reported performance of the proposed method. Thirty-seven patients CT datasets with either lung tumor or pulmonary edema were included in this study. The CT images are first preprocessed for noise reduction and image enhancement, followed by segmentation techniques to segment the lungs, and finally Haralick texture features to detect the type of the abnormality within the lungs. In spite of the presence of low contrast and high noise in images, the proposed algorithms introduce promising results in detecting the abnormality of lungs in most of the patients in comparison with the normal and suggest that some of the features are significantly recommended than others.

Automated liver segmentation for whole-body low-contrast CT images from PET-CT scanners

2009 ◽  
Author(s):  
Xiuying Wang ◽  
Changyang Li ◽  
S. Eberl ◽  
M. Fulham ◽  
Dagan Feng
Keyword(s):  
Ct Images ◽  
Whole Body ◽  
Liver Segmentation ◽  
Low Contrast ◽  
Pet Ct ◽  
Ct Scanners ◽  
Contrast Ct

scholarly journals Joint Segmentation Methods of Tumor Delineation in PET – CT Images: A Review

2018 ◽  
Vol 7 (3.32) ◽  
pp. 137
Author(s):  
Farli Rossi ◽  
Ashrani Aizzuddin Abd Rahni
Keyword(s):  
Image Segmentation ◽  
Ct Images ◽  
Basic Knowledge ◽  
Low Contrast ◽  
Tumor Delineation ◽  
Innovative Methods ◽  
Pet Ct ◽  
Segmentation Methods ◽  
Disease Staging ◽  
Puzzle Task

Segmentation is one of the crucial steps in applications of medical diagnosis. The accurate image segmentation method plays an important role in proper detection of disease, staging, diagnosis, radiotherapy treatment planning and monitoring. In the advances of image segmentation techniques, joint segmentation of PET-CT images has increasingly received much attention in the field of both clinic and image processing. PET - CT images have become a standard method for tumor delineation and cancer assessment. Due to low spatial resolution in PET and low contrast in CT images, automated segmentation of tumor in PET - CT images is a well-known puzzle task. This paper attempted to describe and review four innovative methods used in the joint segmentation of functional and anatomical PET - CT images for tumor delineation. For the basic knowledge, the state of the art image segmentation methods were briefly reviewed and fundamental of PET and CT images were briefly explained. Further, the specific characteristics and limitations of four joint segmentation methods were critically discussed.  

AUTOMATIC MULTI-SCALE SEGMENTATION OF INTRAHEPATIC VESSEL IN CT IMAGES FOR LIVER SURGERY PLANNING

2013 ◽  
Vol 27 (01) ◽  
pp. 1357001 ◽  
Author(s):  
YI WANG ◽  
BIN FANG ◽  
JINGRUI PI ◽  
LEI WU ◽  
PATRICK S. P. WANG ◽  
...  
Keyword(s):  
Distribution Function ◽  
Blood Vessels ◽  
Medical Image ◽  
Automatic Segmentation ◽  
Hessian Matrix ◽  
Ct Images ◽  
Vascular Pattern ◽  
Low Contrast ◽  
Multi Scale ◽  
Normal Scale

The processing of blood vessels is an indispensable part in complicated surgeries of livers and hearts as the development of medical image technologies, which requires an automatic segmentation system over CT images of organs. However, the vascular pattern of livers in CT images suffers from low contrast to background so that the existing segmentation technologies are not able to extract the blood vessels completely. In the paper, we propose a new algorithm to extract the blood vessels of livers based on the adaptive multi-scale segmentation. First, we prove that the background histogram of normal scale blood vessels obeys the Gaussian distribution in CT images, and obtain the vascular distribution function from the vascular signal segmented from the background with a local optimal threshold. Second, Hessian matrix is employed to enhance the thin blood vessels before the extraction, and a complete and clear segmentation system for blood vessels is constructed by combining the major and thin blood vessels via filtering. Experimental results show the effectiveness of the proposed method, which is able to extract more complete blood vessels for 3D system, and assist the clinical liver surgeries efficiently.

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