Controlling gray-level variation in contrast-enhanced digital mammography: design of a calibration procedure

2003 ◽  
Author(s):  
Fanny Jeunehomme ◽  
Razvan Iordache ◽  
Serge L. Muller ◽  
Gordon E. Mawdsley ◽  
Martin J. Yaffe
Keyword(s):  
Digital Mammography ◽  
Calibration Procedure ◽  
Gray Level ◽  
Level Variation ◽  
Contrast Enhanced

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

Cancer Conspicuity on Low-energy Images of Contrast-enhanced Mammography Compared With 2D Mammography

2021 ◽  
Author(s):  
Christina Konstantopoulos ◽  
Tejas S Mehta ◽  
Alexander Brook ◽  
Vandana Dialani ◽  
Rashmi Mehta ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Analysis Of Variance ◽  
Digital Mammography ◽  
Regions Of Interest ◽  
Low Energy ◽  
Wilcoxon Test ◽  
Digital Mammogram ◽  
Contrast Enhanced ◽  
Improved Performance ◽  
2D Images

Abstract Objective Low-energy (LE) images of contrast-enhanced mammography (CEM) have been shown to be noninferior to digital mammography. However, our experience is that LE images are superior to 2D mammography. Our purpose was to compare cancer appearance on LE to 2D images. Methods In this IRB-approved retrospective study, seven breast radiologists evaluated 40 biopsy-proven cancer cases on craniocaudal (CC) and mediolateral oblique (MLO) LE images and recent 2D images for cancer visibility, confidence in margins, and conspicuity of findings using a Likert scale. Objective measurements were performed using contrast-to-noise ratio (CNR) estimated from regions of interest placed on tumor and background parenchyma. Reader agreement was evaluated using Fleiss kappa. Per-reader comparisons were performed using Wilcoxon test and overall comparisons used three-way analysis of variance. Results Low-energy images showed improved performance for visibility (CC LE 4.0 vs 2D 3.5, P < 0.001 and MLO LE 3.7 vs 2D 3.5, P = 0.01), confidence in margins (CC LE 3.2 vs 2D 2.8, P < 0.001 and MLO LE 3.1 vs 2D 2.9, P < 0.008), and conspicuity compared to tissue density compared to 2D mammography (CC LE 3.6 vs 2D 3.2, P < 0.001 and MLO LE 3.5 vs 2D 3.2, P < 0.001). The average CNR was significantly higher for LE than for digital mammography (CC 2.1 vs 3.2, P < 0.001 and MLO 2.1 vs 3.4, P < 0.001). Conclusion Our results suggest that cancers may be better visualized on the LE CEM images compared with the 2D digital mammogram.

A Robust Line Filter for Automatic X-Ray/CT Image Segmentation

2014 ◽  
Vol 721 ◽  
pp. 783-787
Author(s):  
Shao Hu Peng ◽  
Hyun Do Nam ◽  
Yan Fen Gan ◽  
Xiao Hu
Keyword(s):  
Multiscale Analysis ◽  
Automatic Segmentation ◽  
Local Variation ◽  
Recognition System ◽  
Ct Images ◽  
Gray Level ◽  
Ct Image ◽  
Image Pyramid ◽  
Level Variation ◽  
X Ray

Automatic segmentation of the line-like regions plays a very important role in the automatic recognition system, such as automatic cracks recognition in X-ray images, automatic vessels segmentation in CT images. In order to automatically segment line-like regions in the X-ray/CT images, this paper presents a robust line filter based on the local gray level variation and multiscale analysis. The proposed line filter makes usage of the local gray level and its local variation to enhance line-like regions in the X-ray/CT image, which can well overcome the problems of the image noises and non-uniform intensity of the images. For detecting various sizes of line-like regions, an image pyramid is constructed based on different neighboring distances, which enables the proposed filter to analyze different sizes of regions independently. Experimental results showed that the proposed line filter can well segment various sizes of line-like regions in the X-ray/CT images, which are with image noises and non-uniform intensity problems.

scholarly journals New technologies in breast cancer detection – contrast enhanced dual-energy spectral mammography

2019 ◽  
pp. 49-61
Author(s):  
A. V. Chernaya ◽  
S. N. Novikov ◽  
P. V. Krivorotko ◽  
R. Kh. Ulyanova ◽  
V. V. Danilov
Keyword(s):  
Breast Cancer ◽  
Digital Mammography ◽  
Predictive Value ◽  
New Technologies ◽  
Malignant Tumors ◽  
Dual Energy ◽  
Post Contrast ◽  
Morphological Studies ◽  
Contrast Enhanced ◽  
Sensitivity Specificity

Purpose: to study the possibilities of contrast enhanced dual-energy spectral mammography (CESM) in the diagnostics of malignant tumors in the breast.Material and methods. Forty-seven patients with suspicious for breast cancer (BC) lesions underwent CESM. Digital mammography (MMG) and post-contrast images were correlated with the results of path morphological studies after surgery or puncture biopsy was performed.Results. Sensitivity, specificity and overall accuracy in the diagnostics of breast cancer were 83.3%, 85.7%, 85.1% for digital mammography and 91.6%, 91.4%, 91.4% for CESM, respectively. The positive predictive value was 66.6% for digital MMG and 78.5% for CESM. The negative predictive value (NPV) was 96.9% for the CESM and exceeded NPV of the digital MMG, which was 93.7%.Conclusion. Thus, these findings suggest that CESM is an effective method for the diagnostics of malignant tumors in the breast.

Contrast Optimization in Clinical Contrast-Enhanced Digital Mammography Images

2012 ◽  
pp. 17-23 ◽  
Author(s):  
Juan-Pablo Cruz-Bastida ◽  
Iván Rosado-Méndez ◽  
Héctor Pérez-Ponce ◽  
Yolanda Villaseñor ◽  
Héctor A. Galván ◽  
...  
Keyword(s):  
Digital Mammography ◽  
Contrast Enhanced

Contrast Enhanced Digital Mammography (CEDM) Helps to Safely Reduce Benign Breast Biopsies for Low to Moderately Suspicious Soft Tissue Lesions

2020 ◽  
Vol 27 (7) ◽  
pp. 969-976
Author(s):  
Margarita L. Zuley ◽  
Andriy I. Bandos ◽  
Gordon S. Abrams ◽  
Marie A. Ganott ◽  
Terri-Ann Gizienski ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Digital Mammography ◽  
Soft Tissue Lesions ◽  
Contrast Enhanced
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