پردازش تصاویر پزشکی

1.1. Introduction to MIAP Course - Aims and Goals

00:18′

2.1. Medical Imaging and Medical Images

00:26′

3.1. Imaging Modalities - X-Ray

00:17′

4.1. Imaging Modalities - Fluoroscopy and Ultrasound

00:17′

5.1. Imaging Modalities - Nuclear Medicine and Magnetic Resonance Imaging

00:22′

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

00:21′

1.2. Image Enhancement: Definitions and Categorizations

00:18′

2.2. Intensity Transform and Power Law

00:15′

3.2. Histogram Processing - Definitions and Histogram Equalization

00:19′

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

00:17′

5.2. Spatial Filtering and Convolution

00:23′

6.2. Gaussian Filtering and Median Filtering

00:17′

7.2. Image Sharpening Filters

00:20′

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

00:20′

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

00:23′

3.3. Adaptive Local Wiener Filter

00:10′

1.4. Introduction and Classical Regression

00:20′

2.4. Bilateral Filtering (BF)

00:21′

3.4. Non Local Mean Filtering (NLMF)

00:20′

4.4. Wavelet Denoising - Introduction

00:18′

5.4. Wavelet Denoising - Methods

00:20′

6.4. Block Matching and 3D Filtering (BM3D)

00:20′

7.4. Total Variation and Diffusion Anisotropic Filtering

00:23′

8.4. Machine learning and Denoising

00:29′

1.5. Introduction to Image Segmentation Task

00:23′

2.5. Medical Image Consideration and Segmentation Validation

00:20′

3.5. Strategies - Soft and Hard Segmentation

00:21′

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

00:21′

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

00:20′

6.5. Mean Shifts

00:11′

7.5. Supervised Methods (MLP and CNN)

00:21′

8.5. Deep CNN – Loss Functions

00:16′

9.5. Deep CNN - Architectures

00:08′

10.5. Deformable Models - Introduction

00:18′

11.5. Deformable Models - Methods

00:15′

1.6. Introduction - Preliminary

00:19′

2.6. Problem Importance and Examples and Applications (1)

00:19′

3.6. Problem Importance and Examples and Applications (2)

00:19′

4.6. Basics and Definitions

00:21′

5.6. Registration Categorization (1)

00:17′

6.6. Registration Categorization (2)

00:16′

7.6. Registration Implementation (Two Dimensional)

00:21′

8.6. Registration Implementation (Three Dimensional) and Global Transform

00:13′

9.6. Affine, Projection, and Bilinear Transform

00:19′

10.6. Polynomial, RBF, TPS, and PAR Registration

00:17′

11.6. ICP and Image Information

00:20′

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

00:20′

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

00:18′

14.6. Similarity Measure – Mutual Information (MI)

00:14′

15.6. Registration Validation and Machine Learning

00:27′

16.6. Algorithm implementation(basic methods)

00:20′

17.6. Algorithm implementation(Enhancement and Denoising)

00:19′

18.6. Algorithm implementation(Segmentation and Registration)

00:28′