Development of Features and Feature Reduction Techniques for Mammogram Classification

Beura, Shradhananda (2016) Development of Features and Feature Reduction Techniques for Mammogram Classification. PhD thesis.



Breast cancer is one of the most widely recognized reasons for increased death rate among women. For reduction of the death rate due to breast cancer, early detection and treatment are of utmost necessity. Recent developments in digital mammography imaging systems have aimed to better diagnosis of abnormalities present in the breast. In the current scenario, mammography is an effectual and reliable method for an accurate detection of breast cancer. Digital mammograms are computerized X-ray images of breasts. Reading of mammograms is a crucial task for radiologists as they suggest patients for biopsy. It has been studied that radiologists report several interpretations for the same mammographic image. Thus, mammogram interpretation is a repetitive task that requires maximum attention for the avoidance of misinterpretation. Therefore, at present, Computer-Aided Diagnosis (CAD) system is exceptionally popular which analyzes the mammograms with the usage of image processing and pattern recognition techniques and classify them into several classes namely, malignant, benign, and normal. The CAD system recognizes the type of tissues automatically by collecting and analyzing significant features from mammographic images. In this thesis, the contributions aim at developing the new and useful features from mammograms for classification of the pattern of tissues. Additionally, some feature reduction techniques have been proposed to select the reduced set of significant features prior to classification. In this context, five different schemes have been proposed for extraction and selection of relevant features for subsequent classification. Using the relevant features, several classifiers are employed for classification of mammograms to derive an overall inference. Each scheme has been validated using two standard databases, namely MIAS and DDSM in isolation. The achieved results are very promising with respect to classification accuracy in comparison to the existing schemes and have been elaborated in each chapter. In Chapter 2, hybrid features are developed using Two-Dimensional Discrete Wavelet Transform (2D-DWT) and Gray-Level Co-occurrence Matrix (GLCM) in succession. Subsequently relevant features are selected using t-test. The resultant feature set is of substantially lower dimension. On application of various classifiers it is observed that Back-Propagation Neural Network (BPNN) gives better classification accuracy as compared to others. In Chapter 3, a Segmentation-based Fractal Texture Analysis (SFTA) is used to extract the texture features from the mammograms. A Fast Correlation-Based Filter (FCBF) method has been used to generate a significant feature subset. Among all classifiers, Support Vector Machine (SVM) results superior classification accuracy. In Chapter 4, Two-Dimensional Discrete Orthonormal S-Transform (2D-DOST) is used to extract the features from mammograms. A feature selection methodology based on null-hypothesis with statistical two-sample t-test method has been suggested to select most significant features. This feature with AdaBoost and Random Forest (AdaBoost-RF) classifier outperforms other classifierswith respect to accuracy. In Chapter 5, features are derived using Two-Dimensional Slantlet Transform (2D-SLT) from mammographic images. The most significant features are selected by utilizing the Bayesian Logistic Regression (BLogR) method. Utilizing these features, LogitBoost and Random Forest (LogitBoost-RF) classifier gives the better classification accuracy among all the classifiers. In Chapter 6, Fast Radial Symmetry Transform (FRST) is applied to mammographic images for derivation of radially symmetric features. A t-distributed Stochastic Neighbor Embedding (t-SNE) method has been utilized to select most relevant features. Using these features, classification experiments have been carried out through all the classifiers. A Logistic Model Tree (LMT) classifier achieves optimal results among all classifiers. An overall comparative analysis has also been made among all our suggested features and feature reduction techniques along with the corresponding classifier where they show superior results.

Item Type:Thesis (PhD)
Uncontrolled Keywords:Computer-Aided Diagnosis, DWT, GLCM, DOST, Null-hypothesis SFTA, FCBF, SLT, BLogR, FRST, t-SNE, confusion matrix, ROC curve
Subjects:Engineering and Technology > Computer and Information Science > Information Security
Divisions: Engineering and Technology > Department of Computer Science
ID Code:8029
Deposited By:Mr. Sanat Kumar Behera
Deposited On:20 Jul 2016 09:53
Last Modified:20 Jul 2016 09:53
Supervisor(s):Majhi, B and Dash, R

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