Automatic Identification of Breast Cancer Using Texture Features from Infrared Thermograms

2012 ◽  
pp. 399-418
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Automatic Identification

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector

scholarly journals Segmentation and Classification of Breast Cancer Tumour

2018 ◽  
Vol 2 (1) ◽  
pp. 14-18
Author(s):  
Gokalp Cinarer ◽  
Bulent Gursel Emiroglu ◽  
Ahmet Hasim Yurttakal
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Computer Aided Diagnosis ◽  
Wavelet Approximation ◽  
Proposed Model ◽  
Svm Algorithm ◽  
Computer Aided ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

Breast cancer is cancer that forms in the cells of the breasts. Breast cancer is the most common cancer diagnosed in women in the world. Breast cancer can occur in both men and women, but it's far more common in women. Early detection of breast cancer tumours is crucial in the treatment. In this study, we presented a computer aided diagnosis expectation maximization segmentation and co-occurrence texture features from wavelet approximation tumour image of each slice and evaluated the performance of SVM Algorithm. We tested the model on 50 patients, among them, 25 are benign and 25 malign. The 80% of the images are allocated for training and 20% of images reserved for testing. The proposed model classified 2 patients correctly with success rate of 80% in case of 5 Fold Cross-Validation  Keywords: Breast Cancer, Computer-Aided Diagnosis (CAD), Magnetic Resonance Imaging (MRI);

Analysis of Microcalcifications Using Block Wise Local Binary Pattern and Independent component analysis in Mammograms

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Spandana Paramkusham ◽  
Dr. Kunda M.M. Rao ◽  
Dr. BVVSN Prabhakar Rao
Keyword(s):  
Breast Cancer ◽  
Independent Component Analysis ◽  
Local Binary Pattern ◽  
Texture Features ◽  
Component Analysis ◽  
Independent Component ◽  
Significant Shift ◽  
Statistical Features ◽  
Calcium Deposits

In India, the average age of developing a breast cancer has undergone a significant shift over last few decades. Most prominent features that indicate breast cancer are microcalcifications. Microcalcifications are tiny calcium deposits deposited on skin and non-palpable. Automatic analysis of microcalcification helps specialist in having more precise decision. The paper presents an approach that involves classification of microcalcifications into benign/malignant in mammograms. Texture features such LBP and statistical features are extracted from ROIs with microcalcification and independent component analysis is applied to reduce the feature set. These feature set is fed to artificial neural networks to classify the ROIs into malignant and benign calcifications.

Prognostic value of pretreatment MRI texture features in breast cancer brain metastasis treated with Gamma Knife radiosurgery

2020 ◽  
pp. 028418512095629
Author(s):  
Yingyan Zheng ◽  
Daoying Geng ◽  
Tonggang Yu ◽  
Wei Xia ◽  
Dejun She ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Brain Metastasis ◽  
Gamma Knife ◽  
Prognostic Value ◽  
Gamma Knife Radiosurgery ◽  
Texture Features ◽  
Progression Free Survival ◽  
Target Lesion ◽  
Prognostic Models ◽  
Breast Cancer Brain Metastasis

Background Gamma Knife radiosurgery (GKS) was recommended for treating patients with breast cancer brain metastasis (BCBM), but predictions of the existing prognostic models for therapeutic responsiveness vary substantially. Purpose To investigate the prognostic value of pretreatment clinical, MRI radiologic, and texture features in patients with BCBM undergoing GKS. Material and Methods The data of 81 BCBMs in 44 patients were retrospectively reviewed. Progressive disease was defined as an increase of at least 20% in the longest diameter of the target lesion or the presence of new intracranial lesions on contrast-enhanced T1-weighted (CE-T1W) imaging. Radiomic features were extracted from pretreatment CE-T1W images, T2-weighted (T2W) images, and ADC maps. Cox proportional hazard analyses were performed to identify independent predictors associated with BCBM-specific progression-free survival (PFS). A nomogram was constructed and its calibration ability was assessed. Results The cumulative BCBM-specific PFS was 52.27% at six months and 11.36% at one year, respectively. Age (hazard ratio [HR] 1.04; 95% confidence interval [CI] 1.01–1.06; P = 0.004) and CE-T1W-based kurtosis (HR 0.72; 95% CI 0.57–0.92; P = 0.008) were the independent predictors. The combination of CE-T1W-based kurtosis and age displayed a higher C-index (C-index 0.70; 95% CI 0.63–0.77) than did CE-T1W-based kurtosis (C-index 0.65; 95% CI 0.57–0.73) or age (C-index 0.63; 95% CI 0.56–0.70) alone. The nomogram based on the combinative model provided a better performance over age ( P < 0.05). The calibration curves elucidated good agreement between prediction and observation for the probability of 7- and 12-month BCBM-specific PFS. Conclusion Pretreatment CE-T1W-based kurtosis combined with age could improve prognostic ability in patients with BCBM undergoing GKS.

scholarly journals Texture Analysis in the Diagnosis of Primary Breast Cancer: Comparison of High-Resolution Dedicated Breast Positron Emission Tomography (dbPET) and Whole-Body PET/CT

2020 ◽  
Vol 7 ◽  
Author(s):  
Yoko Satoh ◽  
Kenji Hirata ◽  
Daiki Tamada ◽  
Satoshi Funayama ◽  
Hiroshi Onishi
Keyword(s):  
Breast Cancer ◽  
Positron Emission Tomography ◽  
High Resolution ◽  
Texture Analysis ◽  
Texture Features ◽  
Emission Tomography ◽  
Whole Body ◽  
Tumor Characteristics ◽  
Positron Emission ◽  
Pet Ct

Objective: This retrospective study aimed to compare the ability to classify tumor characteristics of breast cancer (BC) of positron emission tomography (PET)-derived texture features between dedicated breast PET (dbPET) and whole-body PET/computed tomography (CT).Methods: Forty-four BCs scanned by both high-resolution ring-shaped dbPET and whole-body PET/CT were analyzed. The primary BC was extracted with a standardized uptake value (SUV) threshold segmentation method. On both dbPET and PET/CT images, 38 texture features were computed; their ability to classify tumor characteristics such as tumor (T)-category, lymph node (N)-category, molecular subtype, and Ki67 levels was compared. The texture features were evaluated using univariate and multivariate analyses following principal component analysis (PCA). AUC values were used to evaluate the diagnostic power of the computed texture features to classify BC characteristics.Results: Some texture features of dbPET and PET/CT were different between Tis-1 and T2-4 and between Luminal A and other groups, respectively. No association with texture features was found in the N-category or Ki67 level. In contrast, receiver-operating characteristic analysis using texture features' principal components showed that the AUC for classification of any BC characteristics were equally good for both dbPET and whole-body PET/CT.Conclusions: PET-based texture analysis of dbPET and whole-body PET/CT may have equally good classification power for BC.

Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection

2020 ◽  
Vol 9 (2) ◽  
pp. 25-44
Author(s):  
Usha N. ◽  
Sriraam N. ◽  
Kavya N. ◽  
Bharathi Hiremath ◽  
Anupama K Pujar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Classification Accuracy ◽  
Breast Imaging ◽  
Texture Features ◽  
Support Vector ◽  
Risk Of Death ◽  
Automated Method ◽  
Occurrence Matrix ◽  
Aided Diagnosis ◽  
Mammogram Images

Breast cancer is one among the most common cancers in women. The early detection of breast cancer reduces the risk of death. Mammograms are an efficient breast imaging technique for breast cancer screening. Computer aided diagnosis (CAD) systems reduce manual errors and helps radiologists to analyze the mammogram images. The mammogram images are typically in two views, cranial-caudal (CC) and medio lateral oblique (MLO) views. MLO contains pectoral muscles (chest muscles) at the upper right or left corner of the image. In this study, it was removed by using a semi-automated method. All the normal and abnormal images were filtered and enhanced to improve the quality. GLCM (Gray Level Co-occurrence Matrix) texture features were extracted and analyzed by changing the number of features in a feature set. Linear Support Vector Machine (LSVM) was used as classifier. The classification accuracy was improved as the number of features in GLCM feature set increases. Simulation results show an overall classification accuracy of 96.7% with 19 GLCM features using SVM classifiers.

scholarly journals Semi-automatic identification of punching areas for tissue microarray building: the tubular breast cancer pilot study

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
Federica Viti ◽  
Ivan Merelli ◽  
Mieke Timmermans ◽  
Michael den Bakker ◽  
Francesco Beltrame ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Pilot Study ◽  
Tissue Microarray ◽  
Automatic Identification ◽  
Tubular Breast

scholarly journals EP-1854: Mammographic texture features for determination breast cancer molecular subtype

2016 ◽  
Vol 119 ◽  
pp. S873
Author(s):  
M. Arenas Prat ◽  
L. Díez-Presa ◽  
J. Torrents-Barrena ◽  
M. Arquez ◽  
C. Pallas ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Molecular Subtype ◽  
Texture Features
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