Image Processing and Analysis I

1. Image Processing and Analysis I Materials extracted from Gonzalez & Wood and Castleman

2. A typical biomedical optics experiment Detector Image Processing Light source Intermediate Optics Specimen

3. Digital Image Processing A process to extract information from image data Preprocessing Segmentation Representation Recognition Interpretation, Modeling Image Annotation, Compression, Storage/Retrieval Image Data Base Knowledge Base Image Acquisition Problem Definition Results Display, Animation Materials extracted from References texts: Gonzalez & Woods, and Castleman

4. Image Processing Example 1 – Pap Smear Squamous Cell Carcinoma Benign Squamous Cells One of the few histopathological tasks where image recognition system is becoming commercial

5. Image Processing Example 2 – Breast X-Ray The distinction between benign and malignant can be difficult for breast x-ray Radiologist are highly trained in image recognition Most biomedical imaging today does not address underlying molecular and cellular based mechanisms

6. Image Data Base – Format and Data Base Header Monochrome Color Hyper-Data 1 bit – binary RGB R (8 bit) G (8 bit) B (8 bit) 8 bit Data 16 bit Gray Scale 32 bit 32 bits (float)

7. Image Preprocessing – histogram and contrast

8. Image Preprocessing – histogram equalization

9. Image Preprocessing – histogram and contrast

10. Convolution and Image Processing Recall the definition of convolution: Graphical explanation of convolution:

11. Convolution Theorem where

12. Image Preprocessing – Noise Reduction, averaging, low pass filter, median filter Median Filter Averaging M images reduce noise by sqrt(M) Low Pass Filter Replace center pixel value by the median value from a nxn pixel neighorhood 3x3 kernel 5x5 kernel Avg = 30; Median =10 Avg by 2,8,16,32,128 Kernel 3,5,7,15,25 5x5 low pass vs median