Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1),  propose intensity ighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2),  integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition,  use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as ll as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning,  also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship beten the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance.
['Ulas Bagci', 'Xinjian Chen', 'Jayaram K. Udupa']
I am writing a research paper on a idea Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures
[{"from": "human", "value": "I am writing a research paper on a idea Hierarchical Scale-Based Multiobject Recognition of 3-D Anatomical Structures"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Segmentation of anatomical structures from medical images is a challenging problem, which depends on the accurate recognition (localization) of anatomical structures prior to delineation. This study generalizes anatomy segmentation problem via attacking two major challenges: 1) automatically locating anatomical structures without doing search or optimization, and 2) automatically delineating the anatomical structures based on the located model assembly. For 1),  propose intensity ighted ball-scale object extraction concept to build a hierarchical transfer function from image space to object (shape) space such that anatomical structures in 3-D medical images can be recognized without the need to perform search or optimization. For 2),  integrate the graph-cut (GC) segmentation algorithm with prior shape model. This integrated segmentation framework is evaluated on clinical 3-D images consisting of a set of 20 abdominal CT scans. In addition,  use a set of 11 foot MR images to test the generalizability of our method to the different imaging modalities as ll as robustness and accuracy of the proposed methodology. Since MR image intensities do not possess a tissue specific numeric meaning,  also explore the effects of intensity nonstandardness on anatomical object recognition. Experimental results indicate that: 1) effective recognition can make the delineation more accurate; 2) incorporating a large number of anatomical structures via a model assembly in the shape model improves the recognition and delineation accuracy dramatically; 3) ball-scale yields useful information about the relationship beten the objects and the image; 4) intensity variation among scenes in an ensemble degrades object recognition performance."}]