Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a supervised pixel-based classifier approach for segmenting different anatomical regions in abdominal computed tomography (CT) studies is presented. The approach consists of three steps: texture extraction, classifier creation, and anatomical regions identification. First, a set of co-occurrence texture descriptors is calculated for each pixel from the image data sample; second, a decision tree classifier is built using the texture descriptors and the names of the tissues as class labels. At the conclusion of the classification process, a set of decision rules is generated to be used for classification of new pixels and identification of different anatomical regions by joining adjacent pixels with similar classifications. It is expected that the proposed approach will also help automate different semi-automatic segmentation techniques by providing initial boundary points for deformable models or seed points for split and merge segmentation algorithms. Preliminary results obtained for normal CT studies are presented.
['Mikhail Kalinin', 'Daniela Stan Raicu', 'Jacob D. Furst', 'David S. Channin']
I am writing a research paper on a idea A classification approach for anatomical regions segmentation
[{"from": "human", "value": "I am writing a research paper on a idea A classification approach for anatomical regions segmentation"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , a supervised pixel-based classifier approach for segmenting different anatomical regions in abdominal computed tomography (CT) studies is presented. The approach consists of three steps: texture extraction, classifier creation, and anatomical regions identification. First, a set of co-occurrence texture descriptors is calculated for each pixel from the image data sample; second, a decision tree classifier is built using the texture descriptors and the names of the tissues as class labels. At the conclusion of the classification process, a set of decision rules is generated to be used for classification of new pixels and identification of different anatomical regions by joining adjacent pixels with similar classifications. It is expected that the proposed approach will also help automate different semi-automatic segmentation techniques by providing initial boundary points for deformable models or seed points for split and merge segmentation algorithms. Preliminary results obtained for normal CT studies are presented."}]