scipy.ndimage and scikit-image is very suitable for dicom image process, and SimpleITK is recommended.and sometimes you may need some interactive operation, so you can have a try https://github.com/Image-Py/imagepy, It is a ui framework you can use many scipy.ndimage and skimage method without coding, and you can extend it by writting plugins, and https://github.com/Image-Py/itk-plgs is a itk plugins, (pydicom can not open some compressed pydicom, but SimpleITK can!)----- 原始邮件 ----- 发件人：Josh Warner email@example.com 收件人："Mailing list for scikit-image (http://scikit-image.org)" firstname.lastname@example.org 主题：[scikit-image] Re: DICOM Image analyses 日期：2018年11月15日 03点54分
Look into pydicom to load your images. Then, you will need to either manually or - more difficult - automatically segment the breast(s), excluding the pectoralis muscles and adjacent air. The data in your images should be Hounsfield units, which are calibrated to quantitatively measure density (0 is water, -1000 is air at the scanner, everything positive is more dense than water). There are many published ranges you could use to start with to differentiate fat from non-fat tissue in your area of interest.
Scikit-Image will likely be most helpful in the segmentation phase. There are several thresholding algorithms which may be of use also, if a simple threshold isn't ideal. Please note that differentiating fat from other soft tissue is relatively easy; differentiating glandular from 'other soft tissue' is not as they will share the same density. If this is a key part of the project, simple density-based thresholding will not be sufficient. On Wed, Nov 14, 2018 at 12:45 PM email@example.com wrote: I have over 500 images from cancer imaging archive. All these are CT images of the chest area (breast). I want to use scikit image to determine which part of the CT represent Fatty tissue and which part represents glands tissue. This might be based of gray scale levels or density I’m not sure.
For instance I want to be able to label image 01 as : 10% fatty tissue 80% fibro glandular tissue and 10% other soft tissue
I have never used scikit image for anything but I have some python programming experience
I really need a good beginner explanation to get this done using sci kit image.
I have seen the documentation but it’s too general. I need something specific for what I want to accomplish as stated above.
Please assist if possible.
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