SimpleITK

v2.5.5 safe
4.0
Medium Risk

SimpleITK is a simplified interface to the Insight Toolkit (ITK) for image registration and segmentation

🤖 AI Analysis

Final verdict: SAFE

The package SimpleITK v2.5.5 appears to be safe for use with low risks associated with network calls, shell execution, and credential harvesting. The moderate obfuscation risk due to pickle usage warrants closer inspection but does not conclusively indicate malicious intent.

  • No network calls or shell executions detected.
  • Usage of pickle.loads and pickle.dumps may indicate obfuscation.
Per-check LLM notes
  • Network: No network calls detected, which is normal for SimpleITK as it primarily deals with image processing without external communications.
  • Shell: No shell execution patterns detected, aligning with expectations for a library focused on image processing tasks.
  • Obfuscation: The usage of pickle.loads and pickle.dumps might indicate an attempt at obfuscation or data serialization, which could be suspicious without clear justification.
  • Credentials: No patterns indicative of credential harvesting were detected.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, but no other red flags were raised.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • .deepcopy(img)) ret = pickle.loads(p) self.assertEqual(img, ret, msg="pickle with defa
  • tocol=prot) ret = pickle.loads(p) self.assertEqual(img, ret, msg="pickle with {
  • ickle.dumps(tx) tx2 = pickle.loads(dump) self.assertEqual(tx, tx2) def test_bspli
  • ickle.dumps(tx) tx2 = pickle.loads(dump) self.assertEqual(tx, tx2) def test_affin
  • ickle.dumps(tx) tx2 = pickle.loads(dump) self.assertEqual(tx, tx2) def test_trans
  • ickle.dumps(tx) tx2 = pickle.loads(dump) self.assertEqual(tx, tx2) def test_compo
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: itk.org

⚠ Suspicious Page Links score 10.0

Found 7 suspicious link(s) on the package page

  • Non-HTTPS external link: http://www.java.com
  • Non-HTTPS external link: http://msdn.microsoft.com/en-us/vcsharp/default.aspx
  • Non-HTTPS external link: http://www.lua.org
  • Non-HTTPS external link: http://www.swig.org
  • Non-HTTPS external link: http://www.opensource.org/licenses/apache2.0.php
  • Non-HTTPS external link: http://insightsoftwareconsortium.github.io/SimpleITK-Notebooks/
✓ Git Repository History

Repository SimpleITK/SimpleITK appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Insight Software Consortium" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with SimpleITK 
Create a medical imaging tool using the SimpleITK package in Python. This tool will focus on basic image segmentation and registration, which are crucial processes in medical imaging analysis. The application should allow users to upload MRI or CT scan images, apply different segmentation techniques to identify specific structures within the images, and align multiple images from the same patient to improve diagnostic accuracy.

### Features:
- **Image Upload:** Users should be able to upload their MRI or CT scan images.
- **Segmentation Techniques:** Implement at least three different segmentation methods such as thresholding, region growing, or watershed segmentation.
- **Image Registration:** Integrate functionality for registering two images, allowing users to align them based on common anatomical landmarks.
- **Visualization:** Provide a visual interface where users can see the original images, segmented results, and aligned images side by side.
- **Save Results:** Allow users to save the segmented and registered images for further use.

### Utilizing SimpleITK:
- Use SimpleITK to load the uploaded images into your application.
- Apply the chosen segmentation algorithms using SimpleITK's built-in functions.
- Use SimpleITK's registration framework to align the images based on user-defined or automatically detected landmarks.
- Display the processed images using matplotlib or any other visualization library, but ensure that SimpleITK is used for all processing steps.

This project aims to provide a hands-on experience with SimpleITK while building a useful tool for medical professionals.