Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement.  present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.
['Yaniv Gal', 'Andrew Mehnert', 'Andrew P. Bradley', 'Kerry McMahon', 'Stuart Crozier']
I am writing a research paper on a idea Automatic Segmentation of Enhancing Breast Tissue in Dynamic Contrast-Enhanced MR Images
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Segmentation of Enhancing Breast Tissue in Dynamic Contrast-Enhanced MR Images"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement.  present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue."}]