MedSimilarity 1.0.0

MedSimilarity is an open source Python to compare 2D medical images.

MedSimilarity

What is MedSimilarity?

MedSimilarity is an open source Python to compare 2D medical images.

Citation

If you use this software, please cite it in your publication using:

@software{medsimilarity,
  title={MedSimilarity},
  author={Kulkarni, Pranav},
  month={May},
  year={2023},
  url={https://github.com/UM2ii/MedSimilarity},
  doi={10.5281/zenodo.7937894}
}

Getting Started

MedSimilarity is currently not available through pip, but you can manually install it.

Manual Installation

You can manually install MedSimilarity as follows:

$ git clone https://github.com/UM2ii/MedSimilarity
$ pip install MedSimilarity/

Example Notebook

We have provided an example notebook in this repository, along with 100 test images to experiment with. You can find the example notebook here.

Documentation

medsimilarity.structural_similarity

Computes the mean structural similarity index measure (SSIM) between two images. This implementation is an extension of skimage.metrics.structural_similarity (https://scikit-image.org/docs/stable/api/skimage.metrics.html#skimage.metrics.structural_similarity) with preprocessing steps for medical images.

Arguments:

img1, img2: PIL.Image Input images

Returns:

score: float The mean structural similarity index measure over the image grad: ndarray The gradient of the structural similarity between img1 and img2 diff: ndarray The full SSIM image

Notes:

• Structural similarity is not invariant to transformations

medsimilarity.structural_comparison

Computes the pairwise structural similarity index measure (SSIM) between an image and a dataset and returns the top K matches.

Arguments:

img: str Path to image dataset: list List containing paths to each image in dataset top_k: int, optional Number of best matches for img in dataset use_multiprocessing: bool, optional Enables spawning of multiple processes to speed up pairwise SSIM calculation

Returns:

score: ndarray The top_k matches for img in dataset with SSIM score

medsimilarity.dense_vector_comparison

Computes the cosine similarity scores using dense vector representations (DVRS) between an image and dataset and returns the top K matches. This method uses SentenceTransformers ViT-B transformer for computation.

Arguments:

img: str Path to image dataset: list List containing paths to each image in dataset top_k: int, optional Number of best matches for img in dataset use_multiprocessing: bool, optional Enables encoding images into embeddings using multiprocessing. If device is 'cuda', images are encoded using multiple GPUs. If device is 'cpu', multiple CPUs are used device: str, optional Specifies device to move all resources to. Use 'cuda' to enable GPU acceleration. If left blank, by default 'cuda' is used if available. If not, 'cpu' is used

Returns:

score: ndarray The top_k matches for img in dataset with DVRS score

medsimilarity.combined_score

Experimental!

Computes the combined score from structural similarity index measure (SSIM) and dense vector representations (DVRS) scores for a pair of images using the formula:

x_combined = sqrt(x_ssim)*(x_dvrs)^2

Arguments:

x_ssim: float The SSIM score for pair of images x_dvrs: float The DVRS score for pair of images

Returns:

x_combined: float Combined score for pair of images

Notes:

• This worked well in my testing but please take this with a grain of salt!