Classification of Mammogram Images
(Sprache: Englisch)
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...
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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.
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Text Sample:CHAPTER 3: SYSTEM DEVELOPMENT:
This chapter describes techniques used for implementation of the system. The proposed system consists of image preprocessing, feature extraction, dimensionality reduction and classification.
3.1 Matlab Environment:
MATLAB, which stands for MATrix LABoratory. Matlab is a mathematical software package used extensively in both academia and industry. It is an interactive tool for numerical computation and data visualization. MATLAB is a high-level computing language with technical applications and environment for data visualization, data analysis, algorithm development and numeric computation. MATLAB is used for these areas of programming and can be used to great effect as extensive specialized libraries of usage definable functions are available to the user that are implemental by simply naming and passing parameters to the function. Matlab has several advantages over other traditional means of numerical computing. It allows quick and easy coding in a very high level language.
- An interactive interface allows easy debugging and rapid experimentation.
- Visualization and High-quality graphic facilities are available.
- Matlab M-files are portable in a wide range of platforms.
- Toolboxes can be added to extend the system.
- Matlab has a problem-solving environment.
- It has sophisticated data structures, contains built in debugging and profiling tools, and supports object oriented programming.
Thus, Matlab is a powerful tool for research and practical problem solving and an excellent language for teaching.
3.2 The Proposed System:
The system is divided into three main stages. The first step involves an enhancement which is used to improve an image quality. The next stage is the Gabor Wavelet and DWT based features extraction from the mammogram. The last stage involves classification using multiclass SVM classifier [...].
The digitized mammogram images are given as an input. The Mammographic Image Analysis
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Society (MIAS) [23] Mini Mammographic Database from the Royal Marsden Hospital in London is used for performing experiment. It contains 322 images (Medio-Lateral Oblique (MLO)) representing 161 bilateral pairs. The database is divided into seven categories which include circumscribed masses, micro-calcifications, architectural distortion, spiculated lesions, ill-defined masses and asymmetric densities. The input digitized images are not clean; it may contain some noise which should be removed, so that they can be used for further processing. 2D Median Filter is used to remove noise from an image. Further Adaptive histogram equalization is applied to it. Once noise is removed from the image, to discard irrelevant information like breast contour, 140 × 140 pixels patches of surrounding the abnormality region were extracted from the original 1024 × 1024 pixels images. The patches assures that the abnormality region is captured, providing the information about the abnormality shape. For the normal case, the patches are extracted from random position. In order to reduce the computational load each image is down sampled to a final size of 30 × 30 pixels. At last, Gabor filtered image is generated. The 2-D wavelet decomposition is performed by applying one dimensional DWT along the rows of the image first and then, the results are decomposed along the columns. Once features are extracted, they are stored in one vector. But the extracted information may require large space for storage as well as while processing it may take more time to compute the operation and produce result. Thus dimensionality reduction mechanism is implemented in which the given feature set is reduced. Here Principle Component Analysis is used. The extracted ROIs can be classified as benign or malign. For classification Support Vector Machine (SVM) is used.
3.3 Implementation of system:
In the proposed system Gabor wavelets based features are extracted from mammogram images. It may contain normal tissue
3.3 Implementation of system:
In the proposed system Gabor wavelets based features are extracted from mammogram images. It may contain normal tissue
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Bibliographische Angaben
- Autor: Supriya Salve
- 2017, 52 Seiten, 34 Abbildungen, Maße: 15,5 x 22 cm, Kartoniert (TB), Englisch
- Verlag: Anchor Academic Publishing
- ISBN-10: 3960671415
- ISBN-13: 9783960671411
Sprache:
Englisch
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