Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract  Intravascular ultrasound (IVUS) has been ll recognized as one porful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. ,  use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features re used as the input data to the ANN method, and then the different vascular layers re distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results shod that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error beten two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images.
['Shengran Su', 'Zhenghui Hu', 'Qiang Lin', 'William Kongto Hau', 'Zhifan Gao', 'Heye Zhang']
I am writing a research paper on a idea An artificial neural network method for lumen and media-adventitia border detection in IVUS
[{"from": "human", "value": "I am writing a research paper on a idea An artificial neural network method for lumen and media-adventitia border detection in IVUS"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract  Intravascular ultrasound (IVUS) has been ll recognized as one porful imaging technique to evaluate the stenosis inside the coronary arteries. The detection of lumen border and media-adventitia (MA) border in IVUS images is the key procedure to determine the plaque burden inside the coronary arteries, but this detection could be burdensome to the doctor because of large volume of the IVUS images. ,  use the artificial neural network (ANN) method as the feature learning algorithm for the detection of the lumen and MA borders in IVUS images. Two types of imaging information including spatial, neighboring features re used as the input data to the ANN method, and then the different vascular layers re distinguished accordingly through two sparse auto-encoders and one softmax classifier. Another ANN was used to optimize the result of the first network. In the end, the active contour model was applied to smooth the lumen and MA borders detected by the ANN method. The performance of our approach was compared with the manual drawing method performed by two IVUS experts on 461 IVUS images from four subjects. Results shod that our approach had a high correlation and good agreement with the manual drawing results. The detection error of the ANN method close to the error beten two groups of manual drawing result. All these results indicated that our proposed approach could efficiently and accurately handle the detection of lumen and MA borders in the IVUS images."}]