Cryo Correction and Color Based Tissue Extraction

by Charles Haben and Rose Hennessy

Abstract

The initial goal of this project was to adjust a number of the nonsymmetric color cryosection images of the National Library of Medicine's Visible Human Data Set. To do this we rotated the images by a small angle, splicing appropriate image sections together. We then moved on to investigate feature extraction methods based on color in order to isolate a specific tissue type from the cryosection images of a male cadaver. This work is part of the Visible Human Project, which will enhance the undergraduate study of anatomy. Students will be able to access and manipulate the cadaver data from the type of workstation found on the average college campus as opposed to access being limited to students at institutions which possess the high powered workstations for which most manipulation of these large data sets has previously been done. Simple, rapid segmentation techniques based on color will aid in viewing individual organs and tissues on such hardware.

Two methods of tissue extraction were compared. The first was a histogram intersection method.[1] The image was traversed and histograms of extracted pixel blocks were compared. The program allowed us to manipulate the number of bins in the histograms, the comparison threshold, the size of the blocks, number of library images used, and the histogram's color space (OPP vs. HSV). The second method segmented images through a Zero Crossing edge detector and a noise reduction program. Thus, we allowed for non-square images to be compared with the library images. In both cases the sections that exceeded the given threshold were then written to an outfile image.

For the tissue retrieval we ran our tests searching for fat and muscle on an abdominal image which also contained liver, kidney, and bone, as well as several other tissues. Our nonsegmented method, in its attempt to isolate muscle, was unable to keep parts of the liver and kidneys from being interpreted as muscle tissue. Setting the threshold to a higher value eliminated some of the detected muscle tissue along with the unwanted liver and kidney tissue. Using the two different color spaces gave useful results, but was still plagued by the detection of incorrect tissue. Using the Zero Crossing edge detector for preliminary image segmentation succeeded somewhat in separating the segmentation process from the tissue identification but, due to incomplete segmentation, resulted in some ambiguities in tissue identification.

This work has shown that standard image processing techniques based on color can be extended to isolate specific tissues from cryosection images. The algorithms used here were implemented on readily available workstations and shown to execute in minimal time.

In conclusion, since much of human tissue has similar color characteristics, color based differentiating will always run into problems of misdetecting other tissue. However, we have found that although color may not be a strong enough characteristic to perfectly extract tissue from cryosection images, it is a strong key for image segmentation and tissue identification. Incorporating color segmentation techniques into those used for texture, location, or other attributes could minimize the problems inherent with each individual method.

Link to Our Technical Report


tech report (4.5 MB)

About the Authors



Charles Haben

A senior at Augustana College, Charles Haben is majoring in Physics, Mathematics, Computer Science, and Secondary Education. He has served as Assistant Residence Director and captain of the Track and Field team. He recently presented a project on utilizing local campus resources at the combined Indiana and Illinois Physics Teachers Conference.
cah@helios.augustana.edu

Rose Hennessy

A junior at the College of St. Catherine, Rose Hennessy is majoring in Mathematics and International Business and Economics, and minoring in Computer Science. She has served as a Mathematics tutor, treasurer of the business and economics clubs, and administrative coordinator of the campus programming board. Her work on CT Data Manipulation for Cryosection Enhancement was presented at the 1997 Argonne National Laboratory Symposium for Undergraduates in Science, Engineering and Mathematics.
rahennessy@stkate.edu