scholarly journals Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring

2019 ◽  
Vol 5 (2) ◽  
pp. 24 ◽  
Author(s):  
Minu George ◽  
Reyer Zwiggelaar
Keyword(s):  
Breast Density ◽  
Breast Tissue ◽  
Local Binary Pattern ◽  
Region Of Interest ◽  
Local Binary Patterns ◽  
Directional Pattern ◽  
Risk Scoring ◽  
Tissue Characteristics ◽  
Local Feature Extraction ◽  
Disk Region

Breast density is considered to be one of the major risk factors in developing breast cancer. High breast density can also affect the accuracy of mammographic abnormality detection due to the breast tissue characteristics and patterns. We reviewed variants of local binary pattern descriptors to classify breast tissue which are widely used as texture descriptors for local feature extraction. In our study, we compared the classification results for the variants of local binary patterns such as classic LBP (Local Binary Pattern), ELBP (Elliptical Local Binary Pattern), Uniform ELBP, LDP (Local Directional Pattern) and M-ELBP (Mean-ELBP). A wider comparison with alternative texture analysis techniques was studied to investigate the potential of LBP variants in density classification. In addition, we investigated the effect on classification when using descriptors for the fibroglandular disk region and the whole breast region. We also studied the effect of the Region-of-Interest (ROI) size and location, the descriptor size, and the choice of classifier. The classification results were evaluated based on the MIAS database using a ten-run ten-fold cross validation approach. The experimental results showed that the Elliptical Local Binary Pattern descriptors and Local Directional Patterns extracted most relevant features for mammographic tissue classification indicating the relevance of directional filters. Similarly, the study showed that classification of features from ROIs of the fibroglandular disk region performed better than classification based on the whole breast region.

scholarly journals Correlation analysis of resected breast tissue and implant volume after mastectomy and its association with breast density

2021 ◽  
Author(s):  
Wolfram Malter ◽  
Bo Jan Bachmann ◽  
Barbara Krug ◽  
Martin Hellmich ◽  
Max Zinser ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Breast Density ◽  
Breast Tissue ◽  
Mathematical Function ◽  
Breast Cancer Patients ◽  
Tissue Weight ◽  
Implant Selection ◽  
Volume Measurements ◽  
The Individual ◽  
The Ideal

Abstract Background The current methods for calculating the ideal implant volume for breast reconstruction are based on pre- or intraoperative volume measurements of the existing breast volume and do not take into account the individual breast density of the woman. This study aims is to identify objective parameters that can help to improve the optimal implant selection. Materials and methods This retrospective analysis includes 198 breast cancer patients who underwent mastectomy. Breast densities (ACR) measured in mammography and MRI were compared with the removed breast tissue weight and volume of the implants used. In addition, the resected weight was compared directly with the implant volume to calculate a mathematical function. Results There was no significant correlation between the ACR values and the resected weights [correlation coefficient: mammography:− 0.117 (p = 0.176), MRI − 0.033 (p = 0.756)]. A negative correlation between the implant volumes and both imaging methods could be demonstrated [correlation coefficient: mammography − 0.268; p = 0.002; MRI was − 0.200 (p = 0.055)]. A highly significant correlation between the resected weights and the implant volumes (correlation coefficient 0.744; p < 0.001) was observed. This correlation corresponds to a power function (y = 34.71 x0.39), in which any resected weight can be used for the variable x to calculate the implant volume. Conclusion We were able to show that there is a significant correlation between the resected breast tissue and the implant volume. With our novel potency function, the appropriate implant volume can be calculated for any resected weight making it easier for the surgeon to choose a fitting implant in a simple and more objective manner.

scholarly journals Postmenopausal Dense Breasts Maintain Premenopausal Levels of GH and Insulin-like Growth Factor Binding Proteins in Vivo

2020 ◽  
Vol 105 (5) ◽  
pp. 1617-1628 ◽  
Author(s):  
Nina Dabrosin ◽  
Charlotta Dabrosin
Keyword(s):  
Postmenopausal Women ◽  
Breast Density ◽  
Breast Tissue ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Dense Breast ◽  
Breast Cancers ◽  
Dense Breast Tissue ◽  
Dense Breasts

Abstract Context Dense breast tissue is associated with 4 to 6 times higher risk of breast cancer by poorly understood mechanisms. No preventive therapy for this high-risk group is available. After menopause, breast density decreases due to involution of the mammary gland. In dense breast tissue, this process is haltered by undetermined biological actions. Growth hormone (GH) and insulin-like binding proteins (IGFBPs) play major roles in normal mammary gland development, but their roles in maintaining breast density are unknown. Objective To reveal in vivo levels of GH, IGFBPs, and other pro-tumorigenic proteins in the extracellular microenvironment in breast cancer, in normal breast tissue with various breast density in postmenopausal women, and premenopausal breasts. We also sought to determine possible correlations between these determinants. Setting and Design Microdialysis was used to collect extracellular in vivo proteins intratumorally from breast cancers before surgery and from normal human breast tissue from premenopausal women and postmenopausal women with mammographic dense or nondense breasts. Results Estrogen receptor positive breast cancers exhibited increased extracellular GH (P &lt; .01). Dense breasts of postmenopausal women exhibited similar levels of GH as premenopausal breasts and significantly higher levels than in nondense breasts (P &lt; .001). Similar results were found for IGFBP-1, -2, -3, and -7 (P &lt; .01) and for IGFBP-6 (P &lt;.05). Strong positive correlations were revealed between GH and IGFBPs and pro-tumorigenic matrix metalloproteinases, urokinase-type plasminogen activator, Interleukin 6, Interleukin 8, and vascular endothelial growth factor in normal breast tissue. Conclusions GH pathways may be targetable for cancer prevention therapeutics in postmenopausal women with dense breast tissue.

MULTI-VIEW GENDER CLASSIFICATION USING MULTI-RESOLUTION LOCAL BINARY PATTERNS AND SUPPORT VECTOR MACHINES

2007 ◽  
Vol 17 (06) ◽  
pp. 479-487 ◽  
Author(s):  
HUI-CHENG LIAN ◽  
BAO-LIANG LU
Keyword(s):  
Feature Extraction ◽  
Support Vector Machines ◽  
Local Binary Pattern ◽  
Spatial Information ◽  
Local Binary Patterns ◽  
Support Vector ◽  
Gender Classification ◽  
Classification Rate ◽  
Vector Machines ◽  
Facial Images

In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs). The experiments clearly show the superiority of the proposed method over both support gray faces and support Gabor faces on the CAS-PEAL face database. A higher correct classification rate of 96.56% and a higher cross validation average accuracy of 95.78% have been obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and fine-to-coarse description of facial images allow for multi-view gender classification.

scholarly journals Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review

2020 ◽  
Vol 170 ◽  
pp. 03007
Author(s):  
Aparna Goyal ◽  
Reena Gunjan
Keyword(s):  
Local Binary Pattern ◽  
Texture Classification ◽  
Local Binary Patterns ◽  
Image Texture ◽  
Discriminative Power ◽  
Computer Image Analysis ◽  
Bleeding Detection ◽  
Segmentation Of Images ◽  
Effective Description ◽  
Lacunarity Analysis

Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern

2015 ◽  
Vol 42 (24) ◽  
pp. 9499-9511 ◽  
Author(s):  
Mohamed Abdel-Nasser ◽  
Hatem A. Rashwan ◽  
Domenec Puig ◽  
Antonio Moreno
Keyword(s):  
Breast Density ◽  
Directional Pattern ◽  
Mammographic Images ◽  
Local Directional Pattern

scholarly journals Passive Forensics for Region Duplication Image Forgery Based on Harris Feature Points and Local Binary Patterns

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Jie Zhao ◽  
Weifeng Zhao
Keyword(s):  
Efficient Method ◽  
Local Binary Pattern ◽  
Local Binary Patterns ◽  
Image Editing ◽  
Feature Points ◽  
Feature Vectors ◽  
Region Duplication ◽  
Software Packages ◽  
Passive Forensics ◽  
Ransac Algorithm

Nowadays the demand for identifying the authenticity of an image is much increased since advanced image editing software packages are widely used. Region duplication forgery is one of the most common and immediate tampering attacks which are frequently used. Several methods to expose this forgery have been developed to detect and locate the tampered region, while most methods do fail when the duplicated region undergoes rotation or flipping before being pasted. In this paper, an efficient method based on Harris feature points and local binary patterns is proposed. First, the image is filtered with a pixelwise adaptive Wiener method, and then dense Harris feature points are employed in order to obtain a sufficient number of feature points with approximately uniform distribution. Feature vectors for a circle patch around each feature point are extracted using local binary pattern operators, and the similar Harris points are matched based on their representation feature vectors using the BBF algorithm. Finally, RANSAC algorithm is employed to eliminate the possible erroneous matches. Experiment results demonstrate that the proposed method can effectively detect region duplication forgery, even when an image was distorted by rotation, flipping, blurring, AWGN, JPEG compression, and their mixed operations, especially resistant to the forgery with the flat area of little visual structures.

scholarly journals Effects of breast density and compression on normal breast tissue hemodynamics through breast tomosynthesis guided near-infrared spectral tomography

2016 ◽  
Vol 21 (9) ◽  
pp. 091316 ◽  
Author(s):  
Kelly E. Michaelsen ◽  
Venkataramanan Krishnaswamy ◽  
Linxi Shi ◽  
Srinivasan Vedantham ◽  
Andrew Karellas ◽  
...  
Keyword(s):  
Breast Density ◽  
Breast Tissue ◽  
Near Infrared ◽  
Normal Breast Tissue ◽  
Normal Breast ◽  
Breast Tomosynthesis ◽  
Infrared Spectral ◽  
Spectral Tomography

scholarly journals Classification the Mammograms Based on Hybrid Features Extraction Techniques Using Multilayer Perceptron Classifier

2020 ◽  
Vol 31 (4) ◽  
pp. 72
Author(s):  
Hayder Adnan AlSudani ◽  
Enaas M. Hussain ◽  
Enam A. Khalil
Keyword(s):  
Multilayer Perceptron ◽  
Region Of Interest ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Features Selection ◽  
Hybrid Features ◽  
Reliable Assessment ◽  
Occurrence Matrix ◽  
Two Stages

Cancer of the breast is one of the world's most prevalent causes of death for women. Early and efficient identification is important for can care choices and reducing mortality. Mammography is the most effective early breast cancer detection process. Radiologists cannot however make a detailed and reliable assessment of mammograms due to fatigue or poor image quality. The main aim of this work is to establish a new approach to help radiologists identify anomalies and improve diagnostic precision. The proposed method has been applied through the implementation of preprocessing then segmentation of the images to get the region of interest that was used to find a texture features that were calculated based on first Order (statistical features), Gray-Level Co-Occurrence Matrix (GLCM), and Local Binary Patterns LBP (LBP). In the features selection phase mutual information (MI) algorithm is applied to choose from the extracted features collection suitable features. Finally, Multilayer Perceptron has been applied in two stages to classify the mammography images first to normal or abnormal, and secondly, classification of abnormal images into benign or malignant images. This method was implemented and gave an accuracy of 92.91 % for the first level and 93.15% for the second level classification.

Sign in / Sign up

Export Citation Format

Share Document