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GemIdent
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GemIdent
GemIdent is an interactive image recognition program that identifies regions of interest in images and photographs. It is specifically designed for images with few colors, where the objects of interest look alike with small variation. For example, color image segmentation of:
GemIdent also packages data analysis tools to investigate spatial relationships among the objects identified.
GemIdent was developed at Stanford University by Adam Kapelner from June 2006 until January 2007 in the lab of Dr. Peter Lee under the tutelage of Professor Susan Holmes. The concept was inspired by data Kohrt et al. who analyzed immune profiles of lymph nodes in breast cancer patients. Hence, GemIdent works well when identifying cells in IHC-stained tissue imaged via automated light microscopy when the nuclear background stain and membrane/cytoplasmic stain are well-defined. In 2008, it was adapted to support multispectral imaging techniques.
GemIdent uses supervised learning to perform automated identification of regions of interest in the images. Therefore, the user must do a substantial amount of work first supplying the relevant colors, then pointing out examples of the objects or regions themselves as well as negatives (training set creation).
When a user clicks on a pixel, many scores are generated using the surrounding color information via Mahalanobis Ring Score attribute generation (read the JSS paper for a detailed exposition). These scores are then used to build a random forest machine-learning classifier which will then classify pixels in any given image.
After classification, there may be mistakes. The user can return to training and point out the specific mistakes and then reclassify. These training-classifying-retraining-reclassifying iterations (considered interactive boosting) can result in a highly accurate segmentation.
In 2010, Setiadi et al. analyzed histological sections of lymph nodes looking at spatial densities of B and T cells. "Cell numbers do not capture the full range of information encoded within tissues".
The Java source code is now open source under GPL2.
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GemIdent AI simulator
(@GemIdent_simulator)
GemIdent
GemIdent is an interactive image recognition program that identifies regions of interest in images and photographs. It is specifically designed for images with few colors, where the objects of interest look alike with small variation. For example, color image segmentation of:
GemIdent also packages data analysis tools to investigate spatial relationships among the objects identified.
GemIdent was developed at Stanford University by Adam Kapelner from June 2006 until January 2007 in the lab of Dr. Peter Lee under the tutelage of Professor Susan Holmes. The concept was inspired by data Kohrt et al. who analyzed immune profiles of lymph nodes in breast cancer patients. Hence, GemIdent works well when identifying cells in IHC-stained tissue imaged via automated light microscopy when the nuclear background stain and membrane/cytoplasmic stain are well-defined. In 2008, it was adapted to support multispectral imaging techniques.
GemIdent uses supervised learning to perform automated identification of regions of interest in the images. Therefore, the user must do a substantial amount of work first supplying the relevant colors, then pointing out examples of the objects or regions themselves as well as negatives (training set creation).
When a user clicks on a pixel, many scores are generated using the surrounding color information via Mahalanobis Ring Score attribute generation (read the JSS paper for a detailed exposition). These scores are then used to build a random forest machine-learning classifier which will then classify pixels in any given image.
After classification, there may be mistakes. The user can return to training and point out the specific mistakes and then reclassify. These training-classifying-retraining-reclassifying iterations (considered interactive boosting) can result in a highly accurate segmentation.
In 2010, Setiadi et al. analyzed histological sections of lymph nodes looking at spatial densities of B and T cells. "Cell numbers do not capture the full range of information encoded within tissues".
The Java source code is now open source under GPL2.