Classification of mammogram images / Supriya Salve.

Format:

Book

Author/Creator:

Salve, Supriya, author.

Language:

English

Subjects (All):

Breast--Radiography.

Breast.

Physical Description:

1 online resource (50 pages) : illustrations, tables

Edition:

1st ed.

Place of Publication:

Hamburg, Germany : Anchor Academic Publishing, 2017.

Summary:

Breast cancer is the most common type of cancer in women, which also causes the most cancer deaths among them today. Mammography is the only reliable method to detect breast cancer in the early stage among all diagnostic methods available currently. Breast cancer can occur in both men and women and is defined as an abnormal growth of cells in the breast that multiply uncontrollably. The main factors which cause breast cancer are either hormonal or genetic. Masses are quite subtle, and have many shapes such as circumscribed, speculated or ill-defined. These tumors can be either benign or malignant. Computer-aided methods are powerful tools to assist the medical staff in hospitals and lead to better and more accurate diagnosis. The main objective of this research is to develop a Computer Aided Diagnosis (CAD) system for finding the tumors in the mammographic images and classifying the tumors as benign or malignant. There are five main phases involved in the proposed CAD system: image pre-processing, extraction of features from mammographic images using Gabor Wavelet and Discrete Wavelet Transform (DWT), dimensionality reduction using Principal Component Analysis (PCA) and classification using Support Vector Machine (SVM) classifier.

Contents:

Classification ofMammogram Images

TABLE OF CONTENTS

LIST OF FIGURES

LIST OF TABLES

CHAPTER 1 : INTRODUCTION

1.1 Introduction

1.2 Necessity

1.3 Objective

CHAPTER 2 : LITERATURE SURVEY

2.1 What is Mammography?

2.1.1 What is a Mammogram?

2.1.2 Limitations of Mammograms

2.1.3 How is Mammography Performed?

2.1.4 View Taken During Screening and Diagnostic Mammography

2.2 Wavelet: A Brief Historical Review

2.3 Wavelet Analysis

2.4 Applications of Wavelet Transform

2.5 Image Preprocessing

2.6 Principal Component Analysis

2.7 Classification

2.7.1 Support Vector Machine

2.7.2 Advantages of Support Vector Machine (SVM)

CHAPTER 3 : SYSTEM DEVELOPMENT

3.1 Matlab Environment

3.2 The Proposed System

3.3 Implementation of system

3.3.1 Image Preprocessing

3.3.2 Feature Extraction by Gabor Wavelets

3.3.3 Feature Extraction by Discrete Wavelet Transform

3.3.4 Dimensionality Reduction

3.3.5 Classification by Support Vector Machine

3.4 Graphical User Interface

3.4.1 Screenshots

CHAPTER 4 : PERFORMANCE ANALYSIS

4.1 Experimental Analysis

4.2. Performance Analysis

4.3 Comparison of Experimental Analysis

4.4 Experimental Results

CHAPTER 5 : CONCLUSION

5.1. Conclusions

5.2. Future Scope

REFERENCES.

Notes:

Description based on online resource; title from PDF cover (EBC, viewed December 14, 2017).

ISBN:

3-96067-641-7