medical

I am working with images processing in Matlab. I am trying to segment out only malignant (cancerous) lung nodules. Initially I managed to segment out Lung and all possible nodules. I have used following Matlab code: segM = % Segmented Lung % Segment nodules BW = im2bw(segM, 0.55); Now, I want to apply some filter that will filter all benign (noncancerous) nodules. I am looking for solution from long time but I haven’t found any way to continue it. Here is the segmented Lung: Update: Consider the nodule size greater than 3mm is malignant (cancerous). How to calculate the size in mm from the

I am researching about electronic health records (EHR). OpenEHR seems to be quite extended and appreciated in this field, as it is widely adopted. However, I fail to find how it is used. I mean, I can see all the definitions for archetypes, and how those definitions are written in ADL or XML. But, once I have an archetype which is just that, a definition of a certain data model, how do I use that? Is there another type of representation, maybe also in ADL or XML? Are there any examples of actual health records for a patient? I have spent hours looking for a John Doe health record example, with

I tried to run the graph cut algorithm for a slice of an MRI after converting it into PNG format. I keep encountering the following problem: Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers). This is even after setting vmin and vmax as follows: plt.imshow(out,vmin=0,vmax=255) Cast the image to np.uint8 after scaling [0, 255] range will dismiss this warning. It seems like a feature in matplotlib , as discussed in this issue . plt.imshow((out * 255).astype(np.uint8)) L.YS If you want to show it, you can use img/255 . I haven't figured

I am trying to use MATLAB to implement a CT (computed tomography) projection operator, A, which I think is also referred as "system matrix" often times. Basically, for a N x N image M, the projection data, P, can be obtained by multiplication of the project operator to the image: P = AM and the backprojection procedure can be performed by multiplying the (conjugate) transpose of the projection operator to the projection data: M = A'P Anyone has any idea/example/sample code on how to implement matrix A (for example: Radon transform)? I would really like to start with a small size of matrix, say

We have been asked by a client to incorporate ICD-9 codes into a system. I'm looking for a good resource to get a complete listing of codes and descriptions that will end up in a SQL database. Unfortunately a web service is out of the question as a fair amount of the time folks will be off line using the application. I've found http://icd9cm.chrisendres.com/ and http://www.icd9data.com/ but neither offer downloads/exports of the data that I could find. I also found http://www.cms.hhs.gov/MinimumDataSets20/07_RAVENSoftware.asp which has a database of the ICD-9 codes but they are not in the

I am working on HCUP data and this has range of values in one single column that needs to be split into multiple columns. Below is the HCUP data frame for reference : code label 61000-61003 excision of CNS 0169T-0169T ventricular shunt The desired output should be : code label 61000 excision of CNS 61001 excision of CNS 61002 excision of CNS 61003 excision of CNS 0169T ventricular shunt My approach to this problem is using the package splitstackshape and using this code library(data.table) library(splitstackshape) cSplit(hcup, "code", "-")[, list(code = code_1:code_2, by = label)] This

I've been studying some dicom series and find that the thickness attribute and the itkimage.GetSpacing()[2] value are not always consistent. For example the thickness (0018, 0050) value encoded in the dcm file is 1.5 mm but the corresponding spacing indicated simpleITK on z axis is 1.00 . Then what value should I use to indicate the physical distance between adjacent voxel center s on the z axis? If they are different things then What do spacings actually mean? I retrieve thickness and spacing values in python like this: //thickness using dicom thickness = dicom.read_file(dcm_file)[0x0018,