TY - GEN
T1 - A content based pattern analysis system for a biological specimen collection
AU - Mallik, Joyita
AU - Samal, Ashok
AU - Gardner, Scott L.
PY - 2007
Y1 - 2007
N2 - Over the years many research collections of biological specimen have been developed for research in biological sciences. Number of specimens in some of these collections can be as high as several millions. There is a move to convert these physical specimens into digital images. This research is motivated by the need to develop techniques to mine useful information from these large collections of specimen images. Specific focus of this research is on the collection of parasites in the Harold W. Manter Laboratory (HWML) Parasite Collection, one of the top four parasite collections in the world. These parasites closely resemble in shape and have flexible bodies with rigid extremities. They have only a few specific structural differences. In this paper we present a technique to retrieve specimens based on shape of a given sample. This form of mining based on the shape of the specimen has the potential to discover linkages between specimens not otherwise known.
AB - Over the years many research collections of biological specimen have been developed for research in biological sciences. Number of specimens in some of these collections can be as high as several millions. There is a move to convert these physical specimens into digital images. This research is motivated by the need to develop techniques to mine useful information from these large collections of specimen images. Specific focus of this research is on the collection of parasites in the Harold W. Manter Laboratory (HWML) Parasite Collection, one of the top four parasite collections in the world. These parasites closely resemble in shape and have flexible bodies with rigid extremities. They have only a few specific structural differences. In this paper we present a technique to retrieve specimens based on shape of a given sample. This form of mining based on the shape of the specimen has the potential to discover linkages between specimens not otherwise known.
UR - http://www.scopus.com/inward/record.url?scp=49549107945&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=49549107945&partnerID=8YFLogxK
U2 - 10.1109/ICDMW.2007.69
DO - 10.1109/ICDMW.2007.69
M3 - Conference contribution
AN - SCOPUS:49549107945
SN - 0769530192
SN - 9780769530192
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 107
EP - 112
BT - ICDM Workshops 2007 - Proceedings of the 17th IEEE International Conference on Data Mining Workshops
T2 - 17th IEEE International Conference on Data Mining Workshops, ICDM Workshops 2007
Y2 - 28 October 2007 through 31 October 2007
ER -