cellmil.features.extractor.morphological

Classes

MorphologicalExtractor(extractor_name)	Class to handle feature extraction.
MorphometricsExtractor()	Placeholder class for morphometric feature extraction.
PyRadiomicsExtractor([enable_all_features, mode])	Class to handle PyRadiomics feature extraction.

class cellmil.features.extractor.morphological.MorphologicalExtractor(extractor_name: ExtractorType)

Bases: object

Class to handle feature extraction.

__init__(extractor_name: ExtractorType) → None

Initialize the Extractor with a PyRadiomics feature extractor.

extract_features(image: ndarray[Any, Any], mask: ndarray[Any, Any], debug: bool = False) → dict[str, Any]

Extract features from the given image and mask.

class cellmil.features.extractor.morphological.PyRadiomicsExtractor(enable_all_features: bool = True, mode: Literal['gray', 'hed', 'hue'] = 'gray')

Bases: object

Class to handle PyRadiomics feature extraction.

__init__(enable_all_features: bool = True, mode: Literal['gray', 'hed', 'hue'] = 'gray') → None

Initialize the PyRadiomics feature extractor.

Parameters:

• enable_all_features – Whether to enable all feature classes or just shape2D

• mode – ‘gray’ for grayscale, ‘hed’ for H channel from HED deconvolution

_preprocess(image: ndarray[Any, Any], mask: ndarray[Any, Any]) → tuple[SimpleITK.Image, SimpleITK.Image]

Convert numpy arrays to SimpleITK images for PyRadiomics, with support for grayscale or HED-H channel.

extract_features(image: ndarray[Any, Any], mask: ndarray[Any, Any]) → dict[str, float]

Extract features from the given image and mask.

class cellmil.features.extractor.morphological.MorphometricsExtractor

Bases: object

Placeholder class for morphometric feature extraction.

extract_features(image: ndarray[Any, Any], mask: ndarray[Any, Any], magnification: float = 0.25) → dict[str, float]

Extract morphometrics features from the given image and mask.