پردازش تصاویر پزشکی

1.1. Introduction to MIAP Course - Aims and Goals

18

2.1. Medical Imaging and Medical Images

26

3.1. Imaging Modalities - X-Ray

17

4.1. Imaging Modalities - Fluoroscopy and Ultrasound

17

5.1. Imaging Modalities - Nuclear Medicine and Magnetic Resonance Imaging

22

6.1. Imaging Modalities - Optical Imaging and Basics of Image Processing

21

1.2. Image Enhancement: Definitions and Categorizations

18

2.2. Intensity Transform and Power Law

15

3.2. Histogram Processing - Definitions and Histogram Equalization

19

4.2. Histogram Processing - Local Histogram Equalization and Digital Subtration Angiography

17

5.2. Spatial Filtering and Convolution

23

6.2. Gaussian Filtering and Median Filtering

17

7.2. Image Sharpening Filters

20

1.3. Linear Shift Invariant Systems and Discrete Fourier Transforms (DFT)

20

2.3. Fourier Domain Image Processing - Image Restoration and Wiener Filters

23

3.3. Adaptive Local Wiener Filter

10

1.4. Introduction and Classical Regression

20

2.4. Bilateral Filtering (BF)

21

3.4. Non Local Mean Filtering (NLMF)

20

4.4. Wavelet Denoising - Introduction

18

5.4. Wavelet Denoising - Methods

20

6.4. Block Matching and 3D Filtering (BM3D)

20

7.4. Total Variation and Diffusion Anisotropic Filtering

23

8.4. Machine learning and Denoising

29

1.5. Introduction to Image Segmentation Task

23

2.5. Medical Image Consideration and Segmentation Validation

20

3.5. Strategies - Soft and Hard Segmentation

21

4.5. K-means and Fuzzy C-Means (FCM)

21

5.5. Fuzzy C-Means (FCM) and Gaussian Mixture Models (GMM)

20

6.5. Mean Shifts

11

7.5. Supervised Methods (MLP and CNN)

21

8.5. Deep CNN – Loss Functions

16

9.5. Deep CNN - Architectures

08

10.5. Deformable Models - Introduction

18

11.5. Deformable Models - Methods

15

1.6. Introduction - Preliminary

19

2.6. Problem Importance and Examples and Applications (1)

19

3.6. Problem Importance and Examples and Applications (2)

19

4.6. Basics and Definitions

21

5.6. Registration Categorization (1)

17

6.6. Registration Categorization (2)

16

7.6. Registration Implementation (Two Dimensional)

21

8.6. Registration Implementation (Three Dimensional) and Global Transform

13

9.6. Affine, Projection, and Bilinear Transform

19

10.6. Polynomial, RBF, TPS, and PAR Registration

17

11.6. ICP and Image Information

20

12.6. Similarity Measure (SSD, SAD, CC, RIU)

20

13.6. Similarity Measure (PIU, RC, and Joint Probability)

18

14.6. Similarity Measure – Mutual Information (MI)

14

15.6. Registration Validation and Machine Learning

27

16.6. Algorithm implementation(basic methods)

20

17.6. Algorithm implementation(Enhancement and Denoising)

19

18.6. Algorithm implementation(Segmentation and Registration)

28