Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Since several lung diseases are diagnosed based on the patterns of lung tissue in medical images, texture segmentation is an essential part of the most computer aided diagnosis (CAD) systems.  a novel composite method is proposed to segment the abnormality in lung tissue in pediatric CT images. The proposed approach is based on wavelet transform and intensity similarities. Our focus is on the honeycomb texture in lung tissue. After segmenting lung regions, wavelet transform is applied to decompose the image. The vertical subimage of lung is thresholded to extract high resolution areas. Then the regions with low pixel intensities are kept and grown to segment the honeycomb regions. The proposed method has been tested on 91 pediatric chest CT images containing healthy and unhealthy lung images. Statistical analysis shows the sensitivity of 100% along with the specificity of 94.44%.
['Rushin Shojaii', 'Javad Alirezaie', 'Paul Babyn']
I am writing a research paper on a idea Automatic Segmentation of Abnormal Lung Parenchyma Utilizing Wavelet Transform
[{"from": "human", "value": "I am writing a research paper on a idea Automatic Segmentation of Abnormal Lung Parenchyma Utilizing Wavelet Transform"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Since several lung diseases are diagnosed based on the patterns of lung tissue in medical images, texture segmentation is an essential part of the most computer aided diagnosis (CAD) systems.  a novel composite method is proposed to segment the abnormality in lung tissue in pediatric CT images. The proposed approach is based on wavelet transform and intensity similarities. Our focus is on the honeycomb texture in lung tissue. After segmenting lung regions, wavelet transform is applied to decompose the image. The vertical subimage of lung is thresholded to extract high resolution areas. Then the regions with low pixel intensities are kept and grown to segment the honeycomb regions. The proposed method has been tested on 91 pediatric chest CT images containing healthy and unhealthy lung images. Statistical analysis shows the sensitivity of 100% along with the specificity of 94.44%."}]