Abnormality detection using classifiers is one of the recent research areas where much importance is given. It is one of the critical issues where excessive care needs to be taken for better diagnosis. An input image may contain excessive information either wanted or unwanted which depends upon the problem formulation. The problem in this project is to analyze the performance of the classifier in terms of its efficiency in detecting abnormalities in medical images. Any classifier needs to detect the carcinogenesis with respect to the efficiency in time of detection and performance. Here two classifiers are selected namely Singular Value Decomposition (SVD), and Principle Component Analysis (PCA). Both the SVD and PCA are applied for dual class classification procedure. The performance analysis of all these classifiers are analyzed using the classifier performance measures like, Sensitivity, Selectivity, Average Detection, Perfect Classification, Missed Classification, False Alarm, F-score and Quality Metrics. Here CT images of brain and skull are used for analysis. Two sets of 30 images are taken which contain both normal and abnormal ones.
Table of Contents
1 INTRODUCTION
2 SELECTING THE REGION OF INTEREST
3 FEATURE EXTRACTION OF MEDICAL IMAGES
4 FEATURE CLASSIFICATION USING SVD
5 RESULTS AND DISCUSSION
Target Objectives and Core Topics
This work aims to evaluate and compare the performance of Singular Value Decomposition (SVD) and Principle Component Analysis (PCA) classifiers in detecting abnormalities within Computer Tomography (CT) brain and skull images, focusing on efficiency and classification accuracy.
- Image preprocessing through Region of Interest (ROI) selection
- Feature extraction techniques including mean, variance, entropy, and wavelet decomposition
- Application of SVD and PCA for binary image classification
- Performance evaluation using metrics like sensitivity, specificity, and F-score
Extract from the Book
4.2. SINGULAR VALUE DECOMPOSITION (SVD)
A mathematical approach that directly reveals the rank and corresponding ideal basis of a dataset is the singular value decomposition (SVD). For a dataset in n dimensional space, for any k < n, the SVD will show the ideal basis for representing that data using only k dimensions [6]. If the SVD reveals that the dataset is full rank and no feature reduction is possible along the calculated axes, then no axes exist for which a reduction is possible. Furthermore, if no reduction is possible, this will be shown by the magnitudes of the singular values revealed by the SVD. An operation such as a classification that would be performed on the entire m × n matrix A can now be equivalently performed on the entire k × n matrix where k < m, resulting in a reduction in the number of bands present in each vector.
For practical purposes, singular values may in fact be nonzero yet be sufficiently close to zero to reduce the dimension of the data. The singular values ߪ,…,ߪrepresent distances from the subspace spanned by .,…,. and very small distances may not affect the operation that will be performed on the reduced data, such as classification. If none of the singular values on the diagonal are close to zero, then the data is already represented using as few dimensions as possible. Practically speaking, it would be necessary to think of a three dimensional (pixel row, pixel column, data bands) image in two dimensions in order to take advantage of the feature reduction made possible by using the SVD.
Chapter Summaries
1 INTRODUCTION: This chapter provides the foundation for medical image processing, identifies the problem of automatic abnormality detection, and outlines the organizational structure of the research.
2 SELECTING THE REGION OF INTEREST: This section details the essential preprocessing stage of defining binary masks to isolate specific, relevant regions within brain CT images for further analysis.
3 FEATURE EXTRACTION OF MEDICAL IMAGES: This chapter explores techniques such as mean, variance, entropy, and wavelet approximation coefficients to transform input data into a compact feature set.
4 FEATURE CLASSIFICATION USING SVD: This section describes the implementation of SVD and PCA for binary classification, using predefined thresholds to distinguish between normal and abnormal medical images.
5 RESULTS AND DISCUSSION: This final chapter presents the experimental findings by applying performance metrics like sensitivity, specificity, and confusion matrices to assess the efficacy of the chosen classifiers.
Keywords
Medical Image Processing, Computer Tomography, Feature Extraction, Singular Value Decomposition, SVD, Principle Component Analysis, PCA, Region of Interest, ROI, Abnormality Detection, Classifier Performance, Sensitivity, Specificity, F-Score, Wavelet Decomposition
Frequently Asked Questions
What is the core focus of this research?
The research focuses on developing and analyzing methods for the automatic detection of abnormalities in CT images of the brain and skull using machine learning classifiers.
What are the primary themes discussed in the book?
The main themes include image preprocessing via ROI selection, dimensionality reduction through feature extraction, and the comparative performance of SVD and PCA classifiers.
What is the main objective of this study?
The primary goal is to assess the performance of SVD and PCA in classifying medical images, specifically measuring their efficiency and accuracy in identifying abnormal tissue.
Which scientific methods are employed?
The study utilizes wavelet transforms for feature extraction and applies SVD and PCA as classification algorithms, evaluated through various statistical performance metrics.
What topics are covered in the main section?
The main sections cover image acquisition, ROI segmentation, extracting descriptive features like entropy and wavelet coefficients, and the execution of classification procedures.
Which keywords best characterize this work?
The work is best characterized by terms such as medical image processing, feature extraction, SVD, PCA, and classification performance measures.
How does the ROI selection influence the classification?
Selecting the correct region of interest improves the efficiency of subsequent stages by isolating the required regions and reducing unnecessary data for the classification algorithm.
Why is the confusion matrix significant in this analysis?
The confusion matrix is used to visualize and compare classification results against ground truth, allowing for a clear understanding of the nature and frequency of classification errors.
- Quote paper
- Sunil Kumar Prabhakar (Author), Harikumar Rajaguru (Author), Vinoth Kumar Bojan (Author), 2014, Feature Extraction and Different Classifiers Applied for Detection of Abnormalities in Computer Tomography (CT) Images, Munich, GRIN Verlag, https://www.grin.com/document/287937
