Cell nuclei segmentation in noisy images using morphological watersheds

Statistics – Applications

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

Scientific paper

A major problem in image processing and analysis is the segmentation of its components. Many computer vision tasks process image regions after segmentation, and the minimization of errors is then crucial for a good automatic inspection system. This paper presents an applied work on automatic segmentation of cell nuclei in digital noisy images. One of the major problems when using morphological watersheds is oversegmentation. By using an efficient homotopy image modification module, we prevent oversegmentation. This module utilizes diverse operations, such as sequential filters, distance transforms, opening by reconstruction, top hat, etc., some in parallel, some in cascade form, leading to a new set of internal and external cell nuclei markers. Very good results have been obtained and the proposed technique should facilitate better analysis of visual perception of cell nuclei for human and computer vision. All steps are presented, as well as the associated images. Implementations wee done in the Khoros system using the MMach toolbox.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Cell nuclei segmentation in noisy images using morphological watersheds does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Cell nuclei segmentation in noisy images using morphological watersheds, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Cell nuclei segmentation in noisy images using morphological watersheds will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-1158884

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.