scholarly journals A New Method for Detecting Architectural Distortion in Mammograms by NonSubsampled Contourlet Transform and Improved PCNN

2019 ◽  
Vol 9 (22) ◽  
pp. 4916
Author(s):  
Guangming Du ◽  
Min Dong ◽  
Yi Sun ◽  
Shuyi Li ◽  
Xiaomin Mu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Complex Structure ◽  
Clinical Manifestations ◽  
Screening Mammography ◽  
New Method ◽  
Contourlet Transform ◽  
Detection Accuracy ◽  
Architectural Distortion ◽  
Exponential Transformation ◽  
Digital Database

Breast cancer is the leading cause of cancer death in women, and early detection can reduce mortality. Architectural distortion (AD) is a feature of clinical manifestations for breast cancer, however, due to its complex structure and low detection accuracy, which cause a high mortality of breast cancer. In order to improve the accuracy of AD detection and reduce the mortality of breast cancer, this paper proposes a new method by combining the non-subsampled contourlet transform (NSCT) with the improved pulse coupled neural network (PCNN). Firstly, the top–bottom hat transformation and the exponential transformation are employed to enhance the image. Secondly, the NSCT is employed to expand the overall contrast of the mammograms and filter out the noise. Finally, the improved PCNN by the maximum inter-class variance threshold selection method is employed to complete the AD detection. This proposed approach is tested on the public and authoritative database—Digital Database for Screening Mammography (DDSM). The specificity of the method is 98.73%, the accuracy is 93.16%, and the F1-score is 79.80%, and the area under curve (AUC) of the receiver operating characteristic (ROC) curve is 0.93, these results clearly demonstrate that the proposed method is comparable with those methods in recent literatures. This proposed method is simple, furthermore it can achieve high accuracy and help doctors to perform computer-aided detection of AD effectively.

A Computer-Assisted Diagnostic (CAD) of Screening Mammography to Detect Breast Cancer Without a Surgical Biopsy

2019 ◽  
Vol 11 (4) ◽  
pp. 31-49 ◽  
Author(s):  
Hadj Ahmed Bouarara
Keyword(s):  
Breast Cancer ◽  
Screening Mammography ◽  
Computer Assisted ◽  
Surgical Biopsy ◽  
Major Health Problem ◽  
Major Health ◽  
Cad System ◽  
Digital Database ◽  
The Social ◽  
N Gram

Breast cancer has become a major health problem in the world over the past 50 years and its incidence has increased in recent years. It accounts for 33% of all cancer cases, and 60% of new cases of breast cancer occur in women aged 50 to 74 years. In this work we have proposed a computer-assisted diagnostic (CAD) system that can predict whether a woman has cancer or not by analyzing her mammogram automatically without passing through a biopsy stage. The screening mammogram will be vectorized using the n-gram pixel representation. After the vectors obtained will be classified into one of the classes—with cancer or without cancer—using the social elephant algorithm. The experimentation using the digital database for screening mammography (DDSM) and validation measures—f-measure entropy recall, accuracy, specificity, RCT, ROC, AUC—show clearly the effectiveness and the superiority of our proposed bioinspired technique compared to others techniques existed in the literature such as naïve bayes, Knearest neighbours, and decision tree c4.5. The goal is to help radiologists with early detection to reduce the mortality rate among women with breast cancer.

scholarly journals Breast Cancer Detection using Image Processing Techniques

2017 ◽  
Vol 10 (2) ◽  
pp. 391-399 ◽  
Author(s):  
Prannoy Giri ◽  
K. Saravanakumar
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Back Propagation ◽  
Texture Features ◽  
Screening Mammography ◽  
Breast Cancers ◽  
Back Propagation Algorithm ◽  
Digital Database ◽  
Processing Techniques ◽  
Aided Diagnosis

Breast Cancer is one of the significant reasons for death among ladies. Many research has been done on the diagnosis and detection of breast cancer using various image processing and classification techniques. Nonetheless, the disease remains as one of the deadliest disease. Having conceive one out of six women in her lifetime. Since the cause of breast cancer stays obscure, prevention becomes impossible. Thus, early detection of tumour in breast is the only way to cure breast cancer. Using CAD (Computer Aided Diagnosis) on mammographic image is the most efficient and easiest way to diagnosis for breast cancer. Accurate discovery can effectively reduce the mortality rate brought about by using mamma cancer. Masses and microcalcifications clusters are an important early symptoms of possible breast cancers. They can help predict breast cancer at it’s infant state. The image for this work is being used from the DDSM Database (Digital Database for Screening Mammography) which contains approximately 3000 cases and is being used worldwide for cancer research. This paper quantitatively depicts the analysis methods used for texture features for detection of cancer. These texture featuresare extracted from the ROI of the mammogram to characterize the microcalcifications into harmless, ordinary or threatening. These features are further decreased using Principle Component Analysis(PCA) for better identification of Masses. These features are further compared and passed through Back Propagation algorithm (Neural Network) for better understanding of the cancer pattern in the mammography image.

scholarly journals Mammogram Diagnostics via 2-D Complex Wavelet-based Self-similarity Measures

2014 ◽  
Vol 8 (2) ◽  
pp. 265 ◽  
Author(s):  
Seonghye Jeon ◽  
Orietta Nicolis ◽  
Brani Vidakovic
Keyword(s):  
Breast Cancer ◽  
Similarity Measures ◽  
The United States ◽  
Screening Mammography ◽  
South Florida ◽  
Self Similarity ◽  
Complex Wavelets ◽  
Digital Database ◽  
Complex Wavelet ◽  
The University

Breast cancer is the second leading cause of death in women in the United States. Mammography is currently the most eective method for detecting breast cancer early; however, radiological inter- pretation of mammogram images is a challenging task. Many medical images demonstrate a certain degree of self-similarity over a range of scales. This scaling can help us to describe and classify mammograms. In this work, we generalize the scale-mixing wavelet spectra to the complex wavelet domain. In this domain, we estimate Hurst parameter and image phase and use them as discriminatory descriptors to clas- sify mammographic images to benign and malignant. The proposed methodology is tested on a set of images from the University of South Florida Digital Database for Screening Mammography (DDSM). Keywords: Scaling; Complex Wavelets; Self-similarity; 2-D Wavelet Scale-Mixing Spectra.

CLASSIFICATION OF MAMMOGRAMS HAVING ARCHITECTURAL DISTORTION AND SPICULATED MASSES USING TEXTURE FEATURES

2021 ◽  
pp. 3-5
Author(s):  
D.B. Aghor ◽  
M.R. Banwaskar
Keyword(s):  
Breast Cancer ◽  
Texture Features ◽  
Screening Mammography ◽  
Support Vector ◽  
Architectural Distortion ◽  
Nonpalpable Breast Cancer ◽  
Current Systems ◽  
Aided Diagnosis ◽  
Mammogram Images

Architectural distortion is the third most common mammographic appearance of nonpalpable breast cancer, representing nearly 6% of abnormalities detected on screening mammography. Although its prevalence on mammography is small compared with calcication or visible mass, architectural distortion is also more difcult to diagnose because it can be subtle and variable in presentation. Early detection of breast cancer is possible by nding architectural distortion in monographic images. Spiculated masses account for about 14% of biopsied lesions and about 81% of these are malignant. Current CAD systems are dramatically better at detecting microcalcications than masses. The sensitivity is considerably lower for Spiculated Masses that are rated as "subtle" by radiologists Moreover, since current systems were devised with masses and calcications in mind, they don’t perform as well on other, less prevalent but still clinically signicant lesion types. In this paper, we propose a computer aided diagnosis system for distinguishing abnormal mammograms with architectural distortion or spiculated masses from normal mammograms. Five types of texture features GLCM, GLRLM, fractal texture, spectral texture and HOG features for the regions of suspicion are extracted. Support vector machine has been used as classier in this work. The proposed system yielded an overall accuracy of 97.29% for mammogram images collected from mini-MIAS database which is better as compared to existing methods.

scholarly journals Automated Assistance for Breast Cancer Identification on Mammograms Using Computer Vision Algorithms

2021 ◽  
Vol 12 (5) ◽  
pp. 1512-1520
Author(s):  
K. Nagaiah, Et. al.
Keyword(s):  
Breast Cancer ◽  
Low Cost ◽  
Screening Mammography ◽  
Support Vector ◽  
Cad Systems ◽  
Cad System ◽  
Digital Database ◽  
Detection Approach ◽  
Screening Strategies ◽  
Mammogram Image

One of the greatest health problems in the world is breast cancer. If these breast cancer abnormalities are identified early, there is a maximum chance of recovery. We can go for this early prediction. It is one of the most effective detection and screening strategies and is widely used. The basic goal of CAD systems is to support physicians in the process of diagnosis. CAD systems, however, are very expensive. Our emphasis is on developing a CAD system that is low-cost and effective. To categorize breast cancer as either benign or malignant, a computer-aided detection approach is suggested. The standard mammogram image corpus, Digital Database used for Screening Mammography, images are used for enhancement, segmented and GLCM, intensity and histogram methods are used to extract features. The work is carried out by effective multilayer perceptron classifier (MLP) and support vector machine (SVM). Compare the performance of the classifiers. The proposed approach achieved 96 % accuracy and 8% improvement in accuracy compared to previous approaches with same dataset [4].

Computer-Assisted Diagnosis System for Abnormalities Classification in Digital Mammography Based on Multi-Threshold Modified Local Ternary Pattern (MtMLTP)

2021 ◽  
Vol 49 ◽  
pp. 75-89
Author(s):  
Norhene Gargouri ◽  
Mouna Zouari ◽  
Randa Boukhris ◽  
Alima Damak ◽  
Dorra Sellami ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Tissue Characterization ◽  
Area Under The Curve ◽  
Breast Cancer Diagnosis ◽  
Screening Mammography ◽  
Computer Assisted ◽  
Local Ternary Pattern ◽  
Digital Database ◽  
Breast Tissues

The aim of this paper is to develop an efficient breast cancer Computer Aided Diagnosis (CAD) system allowing the analysis of different breast tissues in mammograms and performing textural classification (normal, mass or microcalcification). Although several feature extraction algorithms for breast tissues analysis have been used, the findings concerning tissue characterization show no consensus in the literature. Specifically, the challenge may be great for mass and microcalcification detection on dense breasts. The proposed system is based on the development of a new feature extraction approach, the latter is called Multi-threshold Modified Local Ternary Pattern (MtMLTP), it allows the discrimination between various tissues in mammographic images allowing significant improvements in breast cancer diagnosis. In this paper, we have used 1000 ROIs obtained from Digital Database for Screening Mammography (DDSM) database and 100 ROIs from a local Tunisian database named Tunisian Digital Database for Screening Mammography (TDDSM). The Artificial Neural Network (ANN) shows good performance in the classification of abnormalities since the Area Under the Curve (AUC) of the proposed system has been found to be 0.97 for the DDSM database and 0.99 for the TDDSM Database.

scholarly journals A Research on Landslides Automatic Extraction Model Based on the Improved Mask R-CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 168
Author(s):  
Peng Liu ◽  
Yongming Wei ◽  
Qinjun Wang ◽  
Jingjing Xie ◽  
Yu Chen ◽  
...  
Keyword(s):  
Remote Sensing Data ◽  
Extraction Methods ◽  
New Method ◽  
Detection Accuracy ◽  
Sufficient Information ◽  
Automatic Extraction ◽  
Emergency Rescue ◽  
Feature Pyramid ◽  
High Level ◽  
Extraction Model

Landslides are the most common and destructive secondary geological hazards caused by earthquakes. It is difficult to extract landslides automatically based on remote sensing data, which is import for the scenario of disaster emergency rescue. The literature review showed that the current landslides extraction methods mostly depend on expert interpretation which was low automation and thus was unable to provide sufficient information for earthquake rescue in time. To solve the above problem, an end-to-end improved Mask R-CNN model was proposed. The main innovations of this paper were (1) replacing the feature extraction layer with an effective ResNeXt module to extract the landslides. (2) Increasing the bottom-up channel in the feature pyramid network to make full use of low-level positioning and high-level semantic information. (3) Adding edge losses to the loss function to improve the accuracy of the landslide boundary detection accuracy. At the end of this paper, Jiuzhaigou County, Sichuan Province, was used as the study area to evaluate the new model. Results showed that the new method had a precision of 95.8%, a recall of 93.1%, and an overall accuracy (OA) of 94.7%. Compared with the traditional Mask R-CNN model, they have been significantly improved by 13.9%, 13.4%, and 9.9%, respectively. It was proved that the new method was effective in the landslides automatic extraction.

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