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EXTRACTION OF FEATURE FOR EFFICIENT ANALYSIS AND CLASSIFICATION OF BIOMEDICAL IMAGE
TABLE OF CONTENTS
Title Page i
Declaration ii
Approval Page iii
Dedication iv
Abstract vi
Table of Contents vii
CHAPTER ONE: INTRODUCTION
1.1 Introduction 1
1.2 Background of the study 3
1.3 Statement of the General Problem 4
1.4 Objective of the study 5
1.5 Significance of the study 5
1.6 Statement of hypothesis 6
1.7 Scope of the study 6
1.8 Limitation of the study 7
1.9 Definition of terms 7
CHAPTER TWO: LITERATURE REVIEW
2.0 Introduction 9
2.1 Review of related literature 9
2.2 Theoretical framework
2.3 Summary of review 33
CHAPTER THREE: RESEARCH METHODOLOGY
3.1 Introduction 35
3.2 Research design 35
3.3 Area of study 35
3.4 Population of the study 36
3.5 Sample size 36
3.6 Instrument for data collection 36
3.7 Reliability of the instrument 37
3.8 Validity of the Instrument 38
3.9 Method of data Collection 38
3.10 Method of Data Analysis 39
CHAPTER FOUR: DATA PRESENTATION AND ANALYSIS
4.1 Introduction 41
4.2 Characteristics of the respondents 41
4.3 Presentation of Data Analysis 43
4.4 Discussion of Findings 48
4.5 Summary of findings 49
CHAPTER FIVE: SUMMARY, CONCLUSION AND RECOMMENDATIONS
5.1 Summary 51
5.2 Conclusion 52
5.3 Recommendation 53
Biography 54
Appendix 56
Abstract:
Biomedical imaging plays a crucial role in diagnosing and monitoring various diseases, making it an essential tool in modern healthcare. The analysis and classification of biomedical images are challenging tasks due to the large volume of data and the complex nature of the images. To address these challenges, the extraction of informative features plays a vital role in enhancing the efficiency and accuracy of analysis and classification.
This paper presents a comprehensive review of feature extraction techniques for the efficient analysis and classification of biomedical images. The review encompasses a wide range of imaging modalities, including but not limited to, magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and microscopy. Various feature extraction methods, such as texture analysis, shape analysis, intensity-based features, and wavelet transforms, are discussed in detail.
Texture analysis techniques, such as gray-level co-occurrence matrices (GLCM) and local binary patterns (LBP), exploit the spatial distribution of pixel intensities to capture textural information in biomedical images. Shape analysis methods, including boundary-based and region-based approaches, focus on extracting geometric and morphological features to characterize the shape of anatomical structures or lesions.
Intensity-based features, such as statistical moments and histogram-based descriptors, provide information about the pixel intensities within a region of interest. Wavelet transforms are effective in capturing both local and global frequency content in biomedical images. These techniques enable the extraction of multi-resolution features that can capture fine details and coarser structural information simultaneously.
Furthermore, machine learning algorithms, such as support vector machines (SVM), random forests, and convolutional neural networks (CNN), are commonly employed for image classification using the extracted features. These algorithms leverage the discriminative power of the extracted features to differentiate between normal and abnormal tissues or classify different disease types.
The paper also discusses the challenges and limitations associated with feature extraction techniques,such as the curse of dimensionality, robustness to noise and artifacts, and the need for expert knowledge in feature selection. Additionally, emerging trends and advancements in feature extraction, such as deep learning-based approaches and transfer learning, are highlighted.
In conclusion, the extraction of informative features is crucial for efficient analysis and classification of biomedical images. By leveraging various feature extraction techniques and machine learning algorithms, researchers and clinicians can improve the accuracy and reliability of disease diagnosis, treatment planning, and monitoring. However, further research is needed to address the existing challenges and explore novel approaches to feature extraction in biomedical imaging, ultimately leading to improved healthcare outcomes.
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