پردازش تصاویر پزشکی

1.1. Introduction to MIAP Course - Aims and Goals

18:19

2.1. Medical Imaging and Medical Images

26:29

3.1. Imaging Modalities - X-Ray

17:19

4.1. Imaging Modalities - Fluoroscopy and Ultrasound

17:02

5.1. Imaging Modalities - Nuclear Medicine and Magnetic Resonance Imaging

22:33

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

21:04

1.2. Image Enhancement: Definitions and Categorizations

18:36

2.2. Intensity Transform and Power Law

15:17

3.2. Histogram Processing - Definitions and Histogram Equalization

19:37

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

17:34

5.2. Spatial Filtering and Convolution

23:46

6.2. Gaussian Filtering and Median Filtering

17:04

7.2. Image Sharpening Filters

20:03

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

20:47

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

23:13

3.3. Adaptive Local Wiener Filter

10:31

1.4. Introduction and Classical Regression

20:10

2.4. Bilateral Filtering (BF)

21:15

3.4. Non Local Mean Filtering (NLMF)

20:55

4.4. Wavelet Denoising - Introduction

18:04

5.4. Wavelet Denoising - Methods

20:52

6.4. Block Matching and 3D Filtering (BM3D)

20:00

7.4. Total Variation and Diffusion Anisotropic Filtering

23:23

8.4. Machine learning and Denoising

29:08

1.5. Introduction to Image Segmentation Task

23:15

2.5. Medical Image Consideration and Segmentation Validation

20:43

3.5. Strategies - Soft and Hard Segmentation

21:37

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

21:32

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

20:50

6.5. Mean Shifts

11:20

7.5. Supervised Methods (MLP and CNN)

21:33

8.5. Deep CNN – Loss Functions

16:57

9.5. Deep CNN - Architectures

08:09

10.5. Deformable Models - Introduction

18:04

11.5. Deformable Models - Methods

15:56

1.6. Introduction - Preliminary

19:22

2.6. Problem Importance and Examples and Applications (1)

19:38

3.6. Problem Importance and Examples and Applications (2)

19:14

4.6. Basics and Definitions

21:33

5.6. Registration Categorization (1)

17:59

6.6. Registration Categorization (2)

16:09

7.6. Registration Implementation (Two Dimensional)

21:43

8.6. Registration Implementation (Three Dimensional) and Global Transform

13:58

9.6. Affine, Projection, and Bilinear Transform

19:13

10.6. Polynomial, RBF, TPS, and PAR Registration

17:27

11.6. ICP and Image Information

20:09

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

20:05

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

18:11

14.6. Similarity Measure – Mutual Information (MI)

14:29

15.6. Registration Validation and Machine Learning

27:02

16.6. Algorithm implementation(basic methods)

20:56

17.6. Algorithm implementation(Enhancement and Denoising)

19:55

18.6. Algorithm implementation(Segmentation and Registration)

28:13