Blob analysis allows you to identify connected regions of pixels within an image, then calculate selected features of those regions. The regions are commonly known as blobs. 

Blobs are areas of touching pixels that are in the same logical pixel state. This pixel state is called the foreground state, while the alternate state is called the background state. Typically, the background has the value zero and the foreground is everything else (although some control is generally provided to reverse the sense). Feature extraction In many applications, we are interested only in blobs whose features satisfy certain criteria. Since computation is time-consuming, blob analysis is often performed as an elimination process whereby only blobs of interest are considered in further analysis. The steps involved in feature extraction are: 

• Analyze an image and exclude or delete blobs that don’t meet determined criteria. 
• Analyze the remaining blobs to extract further features and determine their criteria. 

Repeat these steps, as necessary, until you have all the blob measurement results you need. Reducing the raw data to just a few feature measurements generally produces more comprehensible and useful results.