scholarly journals Classification of Region of Interest in Mammograms Using Dual Contourlet Transform and Improved KNN

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Min Dong ◽  
Zhe Wang ◽  
Chenghui Dong ◽  
Xiaomin Mu ◽  
Yide Ma
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Nearest Neighbor ◽  
Survival Rates ◽  
Region Of Interest ◽  
Texture Features ◽  
Breast Cancer Diagnosis ◽  
Classification Performance ◽  
Contourlet Transform ◽  
Feature Extraction Method

Goal. Breast cancer is becoming one of the most common cancers among women. Early detection can help increase the survival rates. Feature extraction directly affects diagnosis result. In this work, a novel feature extraction method based on Dual Contourlet Transform (Dual-CT) is presented, and improved K nearest neighbor (KNN) is employed to improve the classification performance. Method. This presented method includes three main sections: firstly, the Region of Interest (ROI) is cropped manually according to gold standard from Mammographic Image Analysis Society (MIAS) database; secondly, the ROIs are decomposed into different resolution levels using Dual-CT, contourlet, and wavelet; a set of texture features are extracted. Then improved KNN and traditional KNN are implemented for classification. Experiments are performed on 324 ROIs which include 206 normal cases and 118 abnormal cases; the abnormal cases are composed of 66 benign cases and 52 malignant cases. Results. Experimental results prove the validity and superiority of Dual-CT-based feature and improved KNN. In particular, 94.14% and 95.76% classification accuracy is achieved based on Dual-CT domain. Moreover, the proposed method is comparable with state-of-the-art methods in terms of accuracy. Contribution. Dual-CT-based feature is used for analyzing mammogram and can help improve breast cancer diagnosis accuracy.

Comparative analysis of breast cancer detection in mammograms and thermograms

2015 ◽  
Vol 60 (1) ◽  
Author(s):  
Marina Milosevic ◽  
Dragan Jankovic ◽  
Aleksandar Peulic
Keyword(s):  
Nearest Neighbor ◽  
Region Of Interest ◽  
Texture Features ◽  
Classification Performance ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Characteristic Analysis ◽  
Analysis Society ◽  
Fold Cross Validation ◽  
Neighbor Classifier

AbstractIn this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed.

scholarly journals Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

2021 ◽  
Vol 12 (2) ◽  
pp. 1147-1153
Author(s):  
T. Sathya Priya, Et. al.
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Decision Tree ◽  
Breast Cancer Diagnosis ◽  
Classification Performance ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
In The Beginning ◽  
Sensitivity Specificity ◽  
Mammogram Images

Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.  

scholarly journals The Research of Feature Extraction Method of Liver Pathological Image Based on Multispatial Mapping and Statistical Properties

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Huiling Liu ◽  
Huiyan Jiang ◽  
Bingbing Xia ◽  
Dehui Yi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Features ◽  
Classification Performance ◽  
Statistical Properties ◽  
Feature Extraction Method ◽  
The Difference ◽  
New Feature ◽  
Value Changes ◽  
Pathological Image

We propose a new feature extraction method of liver pathological image based on multispatial mapping and statistical properties. For liver pathological images of Hematein Eosin staining, the image of R and B channels can reflect the sensitivity of liver pathological images better, while the entropy space and Local Binary Pattern (LBP) space can reflect the texture features of the image better. To obtain the more comprehensive information, we map liver pathological images to the entropy space, LBP space, R space, and B space. The traditional Higher Order Local Autocorrelation Coefficients (HLAC) cannot reflect the overall information of the image, so we propose an average correction HLAC feature. We calculate the statistical properties and the average gray value of pathological images and then update the current pixel value as the absolute value of the difference between the current pixel gray value and the average gray value, which can be more sensitive to the gray value changes of pathological images. Lastly the HLAC template is used to calculate the features of the updated image. The experiment results show that the improved features of the multispatial mapping have the better classification performance for the liver cancer.

Breast Cancer Diagnosis System Based on Wavelet Analysis and Neural Networks

2014 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
K. Taifi ◽  
S. Safi ◽  
M. Fakir ◽  
A. Elbalaoui
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Nearest Neighbor ◽  
Breast Cancer Diagnosis ◽  
Extraction Methods ◽  
Diagnosis System ◽  
Screening Programs ◽  
Mammographic Images ◽  
Aided Diagnosis

The high incidence of breast cancer has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcifications (Mcs). Mammogram is considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this work, the authors present a preprocessing method, based on homomorphic filtering and wavelet, to extract the abnormal Mcs in mammographic images. The authors use four different methods of feature extraction for classification of normal and abnormal patterns in mammogram. Four different feature extraction methods are used here are Wavelet, Gist, Gabor and Tamura. A classification system based on neural network and nearest neighbor classification is used.

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