Feature extraction

1. FEATURE EXTRACTION

2. Feature Extraction
•After segmentation, specific features representing the characteristics and
properties of the segmented regions in the image need to be computed for object
classification and understanding.
2/14/2024 Department of Biomedical Engineering, SRMIST, KTR 2
Segmented
ROI
Feature
Extraction &
Feature
Selection
Normalization
Decision
Procedure
Input
Image
Classification

3. 2/14/2024 Department of Biomedical Engineering, SRMIST, KTR 3
Feature Extraction
 Statistical Features
• Provide quantitative information about the pixels within a segmented region.
• Ex: Histogram, Moments, Energy, Entropy, Contrast, Edges
 Shape Features
• Prvide information about the characteristic shape of the region boundary.
• Ex: Boundary encoding, Moments, Hough Transform, Region Representation,
Morphological Features
 Texture Features
• Provide information about the local texture within the region or the corresponding
part of the image.
• Ex: second-order histogram statistics, co-occurrence matrix, Run length matrix,
Texture energy measures, wavelet processing.
 Relational Features
• Provide information about the relational and hierarchical structure of the regions
associated with a single or a group of objects.

4. 2/14/2024 Department of Biomedical Engineering, SRMIST, KTR 4
Statistical Pixel-Level Features
Histogram Features
• The histogram of an image is a
plot of the gray-level values
versus the number of pixels at
that value.
• The shape of the histogram
provides us with information
about the nature of the image.
– The characteristics of the
histogram has close
relationship with
characteristic of image such
as brightness and contrast.

5. Statistical Pixel-Level Features
First order histogram features
2/14/2024 Department of Biomedical Engineering, SRMIST, KTR 5
• The features based on the first-order histogram
probability are
– Mean
– Standard deviation
– Skew
– Energy
– Entropy

6. Statistical Pixel-Level Features
Histogram of the gray values of pixels
Mean of the gray values of the pixels
Variance and central moments in the region
where n=2 is the variance of the region.
n=3 is a measure of noncentrality
n=4 is a measure of flatness of the histogram.
Standard Deviation
2/14/2024 Department of Biomedical Engineering, SRMIST, KTR 6
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7. Energy Total energy of the gray-values of pixels
Entropy
Local contrast
Maximum and minimum gray values
Skew
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