Feature Extraction and Different Classifiers Applied for Detection of Abnormalities in Computer Tomography (CT) Images

Inhaltsverzeichnis (Table of Contents)

• INTRODUCTION
• SELECTING THE REGION OF INTEREST
• FEATURE EXTRACTION OF MEDICAL IMAGES
• FEATURE CLASSIFICATION USING SVD
• RESULTS AND DISCUSSION

Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)

This book aims to introduce newcomers to the field of feature extraction concepts for medical images, a rapidly developing area in engineering. It provides a brief overview of different classifiers used for detecting abnormalities in medical images, outlining their role in the design, implementation, research, and invention of new image processing techniques. The book caters to practicing engineers, researchers, and students at both undergraduate and graduate levels.

• Feature extraction techniques for medical images.
• Classifiers used in medical image abnormality detection.
• Preprocessing techniques, including region of interest selection.
• Performance evaluation of classifiers using metrics like sensitivity, selectivity, and F-score.
• Application of feature extraction and classification in medical image analysis, specifically for Computer Tomography (CT) images of the brain and skull.

Zusammenfassung der Kapitel (Chapter Summaries)

• Chapter 1: INTRODUCTION This chapter introduces the fundamental concepts of selecting the region of interest (ROI), feature extraction, and various classifiers applied for detecting abnormalities in Computer Tomography (CT) images. It highlights the importance of selecting the region of interest as a preprocessing step that enhances classifier performance.
• Chapter 2: SELECTING THE REGION OF INTEREST This chapter delves into the importance of selecting the region of interest (ROI) as a crucial preprocessing stage in abnormality detection. It explains how efficient ROI selection directly impacts the effectiveness of the detection process, particularly in cancer detection. This stage can improve the efficiency and performance of both feature extraction and classification by focusing on relevant regions for further processing.
• Chapter 3: FEATURE EXTRACTION OF MEDICAL IMAGES This chapter explores the process of feature extraction in medical images, aiming to represent data in a compact and unique form. It emphasizes the importance of feature selection in reducing redundancy and maximizing the joint dependency with the target variable. The chapter discusses the challenges posed by medical image processing and classification, including the need for extensive storage space and processing time. It highlights the wavelet transform as an efficient technique for data analysis, particularly in image processing applications.
• Chapter 4: FEATURE CLASSIFICATION USING SVD This chapter focuses on the application of Singular Value Decomposition (SVD) for feature classification. It explores the use of SVD for dual class classification procedures and discusses its role in abnormality detection. The chapter also examines the performance analysis of SVD, utilizing measures such as sensitivity, selectivity, and F-score to evaluate its efficiency.

Schlüsselwörter (Keywords)

The main keywords and focus topics include medical image processing, feature extraction, classification, Computer Tomography (CT) images, region of interest (ROI), Singular Value Decomposition (SVD), Principle Component Analysis (PCA), sensitivity, selectivity, and F-score.