Digital Image Processing

---

Due Monday, October 5

Part 1: Intensity Transformations & Histogram Processing

• You will use Figures 1 and 2 for this part of the project.
• Import both these images into Matlab, convert to 256 gray level intensity images and obtain their histograms.
>>imread, >> double, >>mat2gray, >>imshow, >> imhist
• Perform a series of intensity transformations by segmenting the original gray levels in these images in 10% intervals. Map the minimum gray level and maximum gray level in each interval to 0 and 1 respectively. Display your results.
>>imadjust
• Write a Matlab m-file capable of reducing the number of intensity levels in each image from 256 to 2, in integer powers of 2.  The desired number of intensity levels needs to be a variable input to your program.
• Perform a histogram equalization operation on each of the images; display the resulting images and their histograms.
>>histeq

Figure 1: Lenna

Figure 2: Cameraman

Part 2: Labeling of Connected Components

• Figures 3 and 4 show micrographs of red blood cells (RBCs) in a blood sample.Figure 3 is the original photograph and Figure 4 is an image processed version that highlights the edges of the features (cells).
• Using the techniques that you have learned so far, design a digital image processing system that provides the RBC count in a blood sample micrograph. Use the image shown in Figure 4 as input to your system.
• Verify your RBC-counting algorithm by counting the number of bacteria in the image shown in Figure 6.

 

Figure 3: Original blood sample micrograph	Figure 4: Edge-detected version of Figure 3
Figure 5: Original bacteria micrograph	Figure 6: Edge-detected version of Figure 5