scholarly journals Digital Mammogram Enhancement

10.5772/60988 ◽  
2015 ◽  
Author(s):  
Michal Haindl ◽  
Václav Remeš
Keyword(s):  
Digital Mammogram

ENHANCEMENT OF SCREEN FILM MAMMOGRAM UP TO A LEVEL OF DIGITAL MAMMOGRAM: EXPERIMENTAL ANALYSIS

2013 ◽  
Vol 13 (02) ◽  
pp. 1340004
Author(s):  
APARNA NARENDRA BHALE ◽  
MANISH RATNAKAR JOSHI
Keyword(s):  
Experimental Analysis ◽  
Breast Imaging ◽  
Point Of View ◽  
Quality Analysis ◽  
Digital Mammogram ◽  
Total Recovery ◽  
Clinical Observations ◽  
Opinion Score ◽  
Better Than

Breast cancer is one of the major causes of death among women. If a cancer can be detected early, the options of treatment and the chances of total recovery will increase. From a woman's point of view, the procedure practiced (compression of breasts to record an image) to obtain a digital mammogram (DM) is exactly the same that is used to obtain a screen film mammogram (SFM). The quality of DM is undoubtedly better than SFM. However, obtaining DM is costlier and very few institutions can afford DM machines. According to the National Cancer Institute 92% of breast imaging centers in India do not have digital mammography machines and they depend on the conventional SFM. Hence in this context, one should answer "Can SFM be enhanced up to a level of DM?" In this paper, we discuss our experimental analysis in this regard. We applied elementary image enhancement techniques to obtain enhanced SFM. We performed the quality analysis of DM and enhanced SFM using standard metrics like PSNR and RMSE on more than 350 mammograms. We also used mean opinion score (MOS) analysis to evaluate enhanced SFMs. The results showed that the clarity of processed SFM is as good as DM. Furthermore, we analyzed the extent of radiation exposed during SFM and DM. We presented our literally findings and clinical observations.

Empirical Evaluation of Generic Weighted Cubicle Pattern and LBP Derivatives for Abnormality Detection in Mammogram Images

2015 ◽  
Vol 15 (01) ◽  
pp. 1550001 ◽  
Author(s):  
A. Suruliandi ◽  
G. Murugeswari ◽  
P. Arockia Jansi Rani
Keyword(s):  
Image Segmentation ◽  
Performance Metrics ◽  
Empirical Evaluation ◽  
Rate Sensitivity ◽  
Digital Mammogram ◽  
Abnormality Detection ◽  
Texture Descriptor ◽  
Texture Pattern ◽  
Local Texture ◽  
Mammogram Images

Digital image processing techniques are very useful in abnormality detection in digital mammogram images. Nowadays, texture-based image segmentation of digital mammogram images is very popular due to its better accuracy and precision. Local binary pattern (LBP) descriptor has attracted many researchers working in the field of texture analysis of digital images. Because of its success, many texture descriptors have been introduced as variants of LBP. In this work, we propose a novel texture descriptor called generic weighted cubicle pattern (GWCP) and we analyzed the proposed operator for texture image classification. We also performed abnormality detection through mammogram image segmentation using k-Nearest Neighbors (KNN) algorithm and compared the performance of the proposed texture descriptor with LBP and other variants of LBP namely local ternary pattern (LTPT), extended local texture pattern (ELTP) and local texture pattern (LTPS). For evaluation, we used the performance metrics such as accuracy, error rate, sensitivity, specificity, under estimation fraction and over estimation fraction. The results prove that the proposed method outperforms other descriptors in terms of abnormality detection in mammogram images.

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.

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