v1.1.0

[cdgatenbee]

• Rigid registration now performed after detecting the tissue and slicing it from the image. This allows higher resolution images to be used for rigid registration, improving accuracy. Implementing this enhancement involved making several changes to VALIS' defaults (see below). Please note that this behavior can be disabled by setting crop_for_rigid_reg=False when initializing the Valis object.
• A new masking method was developed to better detect the tissue and pickup less noise (preprocessing.create_tissue_mask_with_jc_dist)
• Added several new pre-processing methods, including preprocessing.OD, preprocessing.JCDist, preprocessing.ColorDeconvolver, and preprocessing.ColorDeconvolver.
• preprocessing.OD has replaced preprocessing.ColorfulStandardizer as the default RGB pre-processing method. Testing indicates that this method tends to perform better across a range of challenging images, as measured by median number of feature matches and mean TRE. To get results similar to previous versions of VALIS, one can set brightfield_processing_cls=preprocessing.ColorfulStandardizer when calling Valis.register.
• The default image size for rigid registration has been increased from 850 to 1024, (i.e. Valis(max_processed_image_dim_px=1024)).
• Image denoising for rigid registration is no longer the default behavior. This can be turned back on by setting Valis(denoise_rigid=False).
• Reduced memory usage to help enable aligning a large number of images Github issue 105. VALIS has now be tested aligning 556 serially sliced images.
• Passing radians to np.cos and np.sin when determining how much to pad images, as noted in Github issue 103. These images get cropped, so this should not affect registration accuracy.
• Can now read ome.tiff that do not have a SUBIFD, reported in Github issue 101
• Improved saving of non-pyarmid images, related to Github issue 101
• Better compatibility with non-uint8 RGB images.
• Updated code to ensure merged images have channels in the same order as when sorted or specified.
• Dockerfile now compatible with Python 3.12

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This discussion was created from the release v1.1.0.