scholarly journals Gray level clustering and contrast enhancement (GLC–CE) of mammographic breast cancer images

2015 ◽  
Vol 2 (4) ◽  
pp. 279-286 ◽  
Author(s):  
Bhagwati Charan Patel ◽  
G. R. Sinha
Keyword(s):  
Breast Cancer ◽  
Contrast Enhancement ◽  
Gray Level

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

scholarly journals Predicting the Level of Tumor-Infiltrating Lymphocytes in Patients With Breast Cancer: Usefulness of Mammographic Radiomics Features

2021 ◽  
Vol 11 ◽  
Author(s):  
Hongwei Yu ◽  
Xianqi Meng ◽  
Huang Chen ◽  
Jian Liu ◽  
Wenwen Gao ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Feature Selection ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Training Dataset ◽  
Validation Dataset ◽  
Gray Level ◽  
Breast Cancer Patients ◽  
Short Run

ObjectivesThis study aimed to investigate whether radiomics classifiers from mammography can help predict tumor-infiltrating lymphocyte (TIL) levels in breast cancer.MethodsData from 121 consecutive patients with pathologically-proven breast cancer who underwent preoperative mammography from February 2018 to May 2019 were retrospectively analyzed. Patients were randomly divided into a training dataset (n = 85) and a validation dataset (n = 36). A total of 612 quantitative radiomics features were extracted from mammograms using the Pyradiomics software. Radiomics feature selection and radiomics classifier were generated through recursive feature elimination and logistic regression analysis model. The relationship between radiomics features and TIL levels in breast cancer patients was explored. The predictive capacity of the radiomics classifiers for the TIL levels was investigated through receiver operating characteristic curves in the training and validation groups. A radiomics score (Rad score) was generated using a logistic regression analysis method to compute the training and validation datasets, and combining the Mann–Whitney U test to evaluate the level of TILs in the low and high groups.ResultsAmong the 121 patients, 32 (26.44%) exhibited high TIL levels, and 89 (73.56%) showed low TIL levels. The ER negativity (p = 0.01) and the Ki-67 negative threshold level (p = 0.03) in the low TIL group was higher than that in the high TIL group. Through the radiomics feature selection, six top-class features [Wavelet GLDM low gray-level emphasis (mediolateral oblique, MLO), GLRLM short-run low gray-level emphasis (craniocaudal, CC), LBP2D GLRLM short-run high gray-level emphasis (CC), LBP2D GLDM dependence entropy (MLO), wavelet interquartile range (MLO), and LBP2D median (MLO)] were selected to constitute the radiomics classifiers. The radiomics classifier had an excellent predictive performance for TIL levels both in the training and validation sets [area under the curve (AUC): 0.83, 95% confidence interval (CI), 0.738–0.917, with positive predictive value (PPV) of 0.913; AUC: 0.79, 95% CI, 0.615–0.964, with PPV of 0.889, respectively]. Moreover, the Rad score in the training dataset was higher than that in the validation dataset (p = 0.007 and p = 0.001, respectively).ConclusionRadiomics from digital mammograms not only predicts the TIL levels in breast cancer patients, but can also serve as non-invasive biomarkers in precision medicine, allowing for the development of treatment plans.

scholarly journals DIFFERENTIAL DIAGNOSTICS OF BREAST PATHOLOGY USING CONTRAST ENHANCEMENT PATTERNS IN CONTRAST-ENHANCED SPECTRAL MAMMOGRAPHY

2020 ◽  
Vol 66 (3) ◽  
pp. 252-261
Author(s):  
Roksana Ulyanova ◽  
A. Chernaya ◽  
Petr Krivorotko ◽  
Sergey Novikov ◽  
Sergey Kanaev ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Contrast Enhancement ◽  
World Literature ◽  
Differential Diagnostics ◽  
Diagnostic Feature ◽  
Benign Lesions ◽  
Intravenous Contrast ◽  
Dynamic Contrast Enhancement ◽  
Contrast Enhanced ◽  
Detailed Assessment

Dual-energy contrast-enhanced spectral mammography (CESM) is a new promising method for visualizing pathological changes in breast, which combines digital mammography and a functional assessment of vascularization using intravenous contrast ehnancement. According to accumulated experience CESM is well tolerated by patients and is similar to magnetic resonance imaging with dynamic contrast enhancement (MRI with DCE), but at the same time, CESM is more affordable and can be performed in patients with contraindications for MRI. However, few studies have been conducted to evaluate the role of CESM. In the world literature, interpretation of contrast images is based only on the degree of accumulation of the contrast agent, but we propose a more detailed assessment of the structure of the hypervascular lesions by highlighting the contrast enhancement patterns. Objective: to determine the diagnostic effectiveness of CESM using the contrast enhancement patterns in malignant and benign lesions. Materials and methods. 239 women with suspicious for breast cancer lesions were examined from August 2018 to December 2019. The mean age of the women was 51 years. 322 lesions were revealed, 149 (46.3%) were malignant, 173 (53.7%) were benign. All lesions were histologically confirmed. As a result of the analysis of our data, 9 types of contrast enhancement patterns were distinguished: reticulate, granular, annular, diffuse-spherical, lacunar, cloud-like, heterogeneous-annular, point, cotton-like. Results. Using an additional diagnostic feature - contrast enhancement patterns in lesions, increased the sensitivity of CESM from 91.3% to 98.0% (p=0.26), specificity from 80.3% to 93, 6% (p=0.013), accuracy from 85.4 to 95.7% (p=0.004) in comparison with using of only one feature of contrast enhancement intensity in the differential diagnosis of malignant and benign lesions. Conclusion: thus, this approach of interpreting subtraction images allows to increase the efficiency of CESM in diagnosis of breast cancer.

Recognition of Heterogeneous Edges in Multiwavelength Transmission Images Based on the Weighted Constraint Decision Method

2020 ◽  
Vol 74 (8) ◽  
pp. 883-893
Author(s):  
Fulong Liu ◽  
Gang Li ◽  
Shuqiang Yang ◽  
Wenjuan Yan ◽  
Guoquan He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Image Enhancement ◽  
Light Transmission ◽  
Image Data ◽  
Experimental System ◽  
Probability Density Distribution ◽  
High Signal ◽  
Gray Level ◽  
Weighted Constraint

Multiwavelength light transmission imaging provides a possibility for early detection of breast cancer. However, due to strong scattering during the transmission process of breast tissue analysis, the transmitted image signal is weak and the image is blurred and this makes heterogeneous edge detection difficult. This paper proposes a method based on the weighted constraint decision (WCD) method to eliminate the erosion and checkerboard effects in image histogram equalization (HE) enhancement and to improve the recognition of heterogeneous edge. Multiwavelength transmission images of phantom are acquired on the designed experimental system and the mask image with high signal-to-noise ratio (SNR) is obtained by frame accumulation and an Otsu thresholding model. Then, during image enhancement the image is divided into low-gray-level (LGL) and high-gray-level (HGL) regions according to the distribution of light intensity in image. And the probability density distribution of gray level in the LGL and HGL regions are redefined respectively according to the WCD method. Finally, the reconstructed image is obtained based on the modified HE. The experimental results show that compared with traditional image enhancement methods, the WCD method proposed in this paper can greatly improve the contrast between heterogeneous region and normal region. Moreover, the correlation between the original image data is maintained to the greatest extent, so that the edge of the heterogeneity can be detected more accurately. In conclusion, the WCD method not only accurately identifies the edge of heterogeneity in multiwavelength transmission images, but it also could improve the clinical application of multiwavelength transmission images in the early detection of breast cancer.

Tumor CT imaging using targeted nanoparticle delivery for contrast enhancement and tumor inhibition using targeted release of carboplatin nanoparticles via radiotherapy

2014 ◽  
Vol 24 (03n04) ◽  
pp. 137-149
Author(s):  
S. Harada ◽  
S. Ehara ◽  
K. Ishii ◽  
T. Sato ◽  
M. Koka ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Hyaluronic Acid ◽  
Contrast Enhancement ◽  
Tumor Cells ◽  
Antitumor Effect ◽  
X Ray ◽  
Nanoparticle Delivery ◽  
Targeted Nanoparticles ◽  
Targeted Release

In this paper, we used microcapsules releasing liposome-protamine-hyaluronic acid nanoparticles (LPH-NP) with/without carboplatin in response to radiation to image and treat MM48 breast cancer in C3He/N mice in two radiation sessions. The micro-particle-induced X-ray emission (PIXE) camera and quantitative PIXE were used to image and measure the release of nanoparticles from the microcapsules. In session one, iopamiron and computed tomography (CT)-detectable microcapsules containing P-selectin and LPH-NP were mixed with a solution of alginate, hyaluronate, ascorbate, and P-selectin. This solution was sprayed into an FeCl2 solution containing VEGFR-1/2 antibodies (Abs). The microcapsules obtained were injected intravenously into mice, and after 9 h, the mice were exposed to 10 or 20 Gy (140 keV) of X-ray radiation. Anti-VEGFR-1/VEGFR-2 microcapsules accumulated around tumors and released P-selectin and the iopamiron-labeled LPH-NP in response to the first radiation. The iopamiron-containing nanoparticles were detected by CT, allowing detection of MM48 tumors by CT. In the second session, the microcapsules released LPH-NH that delivered carboplatin into the tumor cells. This treatment had a significant antitumor effect [Formula: see text]. The micro-PIXE camera and quantitative PIXE successfully imaged and measured the release of contents from microcapsules. Our results indicate that targeted nanoparticles allow for accurate detection and treatment of tumors.

DIGITAL MAMMOGRAM ENHANCEMENT USING KAPUR MEASURE OF ENTROPY AND MATHEMATICAL MORPHOLOGY

2013 ◽  
Vol 25 (03) ◽  
pp. 1350029 ◽  
Author(s):  
Baljit Singh Khehra ◽  
Amar Partap Singh Pharwaha
Keyword(s):  
Breast Cancer ◽  
Image Analysis ◽  
Early Detection ◽  
Contrast Enhancement ◽  
Mathematical Morphology ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Signal To Noise ◽  
Mammogram Image ◽  
Background Contrast

Mammography is the most reliable, effective, low cost and highly sensitive method for early detection of breast cancer. Mammogram analysis usually refers to the processing of mammograms with the goal of finding abnormality presented in the mammogram. Mammogram enhancement is one of the most critical tasks in automatic mammogram image analysis. Main purpose of mammogram enhancement is to enhance the contrast of details and subtle features while suppressing the background heavily. In this paper, a hybrid approach is proposed to enhance the contrast of microcalcifications while suppressing the background heavily, using fuzzy logic and mathematical morphology. First, mammogram is fuzzified using Gaussian fuzzy membership function whose bandwidth is computed using Kapur measure of entropy. After this, mathematical morphology is applied on fuzzified mammogram. Mathematical morphology provides tools for the extraction of microcalcifications even if the microcalcifications are located on a nonuniform background. Main advantage of Kapur measure of entropy over Shannon entropy is that Kapur measure of entropy has α and β parameters that can be used as adjustable values. These parameters can play an important role as tuning parameters in the image processing chain for the same class of images. Experiments have been conducted on images of mini-Mammogram Image Analysis Society (MIAS) database (UK). Experiment results of the proposed approach are compared with histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE) and fuzzy histogram hyperbolization (FHH) which are well-established image enhancement techniques. In order to validate the results, several different kinds of standard test images (fatty, fatty-glandular and dense-glandular) of mini-MIAS database are considered. Objective image quality assessment parameters: Target-to-background contrast enhancement measurement based on standard deviation (TBCSD), target-to-background contrast enhancement measurement based on entropy (TBCE), contrast improvement index (CII), peak signal-to-noise ratio (PSNR) and average signal-to-noise ratio (ASNR) are used to evaluate the performance of proposed approach. The experimental results show that the proposed approach performs well. This study can be a part of developing a computer-aided diagnosis (CAD) system for early detection of breast cancer.

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