Video Lectures on "Biomedical Image Analysis" by Prof. Rangaraj M. Rangayyan

Lecture Videos:

Rangaraj M. Rangayyan: Biomedical Image Analysis

 Click on Computer image to view the lecture

1	Introduction Evaluation and grading
2	Course book 1. The Nature of Biomedical Images
3	1.1 Body Temperature as an Image 1.2 Transillumination 1.3 Light Microscopy
4	1.4 Electron Microscopy 1.5 X-ray Imaging
5	1.5 X-ray Imaging (cont.)
6	1.5 X-ray Imaging (cont.)
7	1.5.1 Breast cancer and mammography 1.6 Tomography
8	1.7 Nuclear Medicine Imaging
9	1.7 Nuclear Medicine Imaging (cont.) 1.8 Ultrasonography 1.9 Objectives of Biomedical Image Analysis
10	1.10 Objectives of Biomedical Image Analysis (cont.) 1.11 Computer-aided Diagnosis (CAD)
11	2. Image Quality and Information Content 2.1 Difficulties in Image Acquisition and Analysis 2.2 Characterization of Image Quality 2.3 Digitization of Images      2.3.1 Sampling
12	2.3.2 Quantization
13	2.3.3 Array and matrix representation of images 2.4 Optical Density 2.5 Dynamic Range 2.6 Contrast
14	2.7 Histogram 2.8 Entropy
15	2.8 Entropy (cont.) 2.9 Blur and Spread Functions
16	2.9 Blur and Spread Functions (cont.)
17	2.10 Resolution 2.11 The Fourier Transform and Spectral Content
18	2.11 The Fourier Transform and Spectral Content      2.11.1 Important properties of the Fourier transform (FT)
19	2.11.1 Important properties of the Fourier transform (FT) (cont.)
20	2.11.1 Important properties of the Fourier transform (FT) (cont.) 2.1.2 Modulation Transfer Function (MTF)
21	2.12 Modulation Transfer Function (MTF) 2.13 Signal-to-Noise Ratio 2.14 Error-based Measures
22	2.15 Application: Image Sharpness and Acutance
23	3 Removal of Artifacts 3.1 Characterization of Artifacts      3.1.1 Random noise
24	3.1.2 Examples of noise PDFs
25	3.1.3 Structured noise      3.1.4 Physical Interference      3.1.5 Other types pf noise and artifact      3.1.6 Stationary versus nonstationary processes
26	3.1.7 Covariance and cross-correlation      3.1.8 Signal-dependant noise 3.2 Synchronized or Multiframe Averaging 3.3 Space-domain Local-statistics-based Filters      3.3.1 The mean filter
27	3.3.1 The mean filter (cont.)      3.3.2 The median filter
28	3.3.3 Order-statistic filters 3.4 Frequency-domain Filters      3.4.1 Removal of high-frequency noise
29	3.4.2 Removal of periodic artifacts 3.5 Matrix Representation of Image Processing      3.5.1 Matrix representation of images
30	3.5.2 Matrix representation of transforms
31	3.5.3 Matrix representation of convolution
32	3.5.4 Illustration of convolution      3.5.5 Diagonalization of a circulant matrix
33	3.5.6 Block-circulant matrix representation of a 2D filter
34	3.6 Optimal filtering      3.6.1 The Wiener filter
35	3.7 Adaptive filters      3.7.1 The local LMMSE filter
36	3.7.1 The local LMMSE filter (cont.)      3.7.2 The noise-updating repeated Wiener filter      3.7.3 The adaptive 2D LMS filter      3.7.4 The adaptive rectangular window LMS filter      3.7.5 The adaptive-neighborhood filter
37	3.7.5 The adaptive-neighborhood filter (cont.)
38	3.8 Application: Multiframe Averaging in Confocal Microscopy 3.9 Application: Noise Reduction in Nuclear Medicine Imaging
39	4 Image Enhancement 4.1 Digital Subtraction Angiography 4.2 Dual-energy and Energy-subtraction X-ray Imaging 4.3 Temporal Subtraction 4.4 Gray-scale Transforms      4.4.1 Grey-scale thresholding
40	4.4.2 Gray-scale windowing      4.4.3 Gamma correction 4.5 Histogram Transformation      4.5.1 Histogram equalization
41	4.5.1 Histogram equalization (cont.)      4.5.2 Histogram specification      4.5.2 Histogram specification      4.5.4 Local-area histogram equalization
42	4.5.5 Adaptive-neighborhood histogram equalization 4.6 Convolution Mask Operators      4.6.1 Unsharp masking
43	4.6.2 Subtracting Laplacian      4.6.3 Limitations of fixed operators 4.7 High-frequency Emphasis
44	4.8 Homomorphic Filtering for Enhancement      4.8.1 Generalized linear filtering
45	4.9 Adaptive Contrast Enhancement      4.9.1 Adaptive-neighborhood contrast enhancement 4.10 Objective Assessment of Contrast Enhancement
46	5 Detection of Regions of Interest 5.1 Thresholding and Binarization 5.2 Detection of Isolated Points and Lines
47	5.3 Edge Detection      5.3.1 Convolution mask operators for edge detection      5.3.2 The Laplacian of Gaussian
48	5.3.3 Scale-space methods for multiscale edge detection      5.3.4 Canny's method for edge detection      5.3.5 Fourier-domain methods for edge detection      5.3.6 Edge linking
49	5.4 Segmentation and Region Growing      5.4.1 Optimal thresholding      5.4.2 Region-oriented segmentation of images      5.4.3 Splitting and merging of regions
50	5.4.4 Region growing using an additive tolerance      5.4.5 Region growing using a multiplicative tolerance      5.4.6 Analysis of region growing in the presence of noise      5.4.7 Iterative region growing with multiplicative tolerance      5.4.8 Region growing based upon the human visual system      5.4.9 Detection of calcifications by multitolerance region growing
51	5.4.9 Detection of calcifications by multitolerance region growing (cont.)      5.4.10 Application: Detection of calcifications by linear prediction error 5.5 Fuzzy-set-based Region Growing to Detect Breast Tumors 5.6 Detection of Objects of Known Geometry      5.6.1 The Hough transform      5.6.2 Detection of straight lines      5.6.3 Detection of circles 5.7 Methods for the Improvements of Contour or Region Estimates
52	5.8 Application: Detection of the Spinal Canal
53	5.5 Fuzzy-set-based Region Growing to Detect Breast Tumors      5.5.1 Preprocessing based upon fuzzy sets      5.5.2 Fuzzy segmentation based upon region growing      5.5.3 Fuzzy region growing
54	5.10 Application: Detections of the Pectoral Muscle in Mammograms      5.10.1 Detection using the Hough transform