Source code for histolab.filters.image_filters

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"""All filters implemented in the image filters submodule take as input a Pillow Image
object. Additionally, some of the image filters in histolab leverage functions and
utilities by scikit-image. Image filters are divided into sub-categories, depending on
their behaviour and output type.
 """

import operator
from abc import abstractmethod
from typing import Any, Callable, List, Union

import numpy as np
import PIL

from .. import util
from . import image_filters_functional as F

try:
    from typing import Protocol, runtime_checkable
except ImportError:
    from typing_extensions import Protocol, runtime_checkable


[docs]@runtime_checkable class Filter(Protocol): """Filter protocol""" @abstractmethod def __call__( self, img: Union[PIL.Image.Image, np.ndarray] ) -> Union[PIL.Image.Image, np.ndarray]: pass # pragma: no cover def __repr__(self) -> str: return self.__class__.__name__ + "()"
[docs]@runtime_checkable class ImageFilter(Filter, Protocol): """Image filter protocol""" @abstractmethod def __call__(self, img: PIL.Image.Image) -> Union[PIL.Image.Image, np.ndarray]: pass # pragma: no cover
[docs]class Compose(ImageFilter): """Composes several filters together. Parameters ---------- filters : list of Filters List of filters to compose """ def __init__(self, filters: List[Filter]) -> None: self.filters = filters def __call__(self, img: PIL.Image.Image) -> Union[PIL.Image.Image, np.ndarray]: for filter_ in self.filters: img = filter_(img) return img
[docs]class Lambda(ImageFilter): """Apply a user-defined lambda as a filter. Inspired from: https://pytorch.org/docs/stable/_modules/torchvision/transforms/transforms.html#Lambda Parameters ---------- lambd : callable Lambda/function to be used as a filter. Returns ------- PIL.Image.Image The image with the function applied. """ # noqa def __init__(self, lambd: Callable[[PIL.Image.Image], PIL.Image.Image]) -> None: assert callable(lambd), repr(type(lambd).__name__) + " object is not callable" self.lambd = lambd def __call__(self, img: PIL.Image.Image) -> Union[PIL.Image.Image, np.ndarray]: return self.lambd(img)
[docs]class ToPILImage(Filter): """Convert a ndarray to a PIL Image, while preserving the value range. Parameters ---------- np_img : np.ndarray The image represented as a NumPy array. Returns ------- PIL.Image.Image The image represented as PIL Image """ def __call__(self, np_img: np.ndarray) -> PIL.Image.Image: return util.np_to_pil(np_img)
[docs]class ApplyMaskImage(Filter): """Mask image with the provided binary mask. Parameters ---------- img : PIL.Image.Image Input image mask : np.ndarray Binary mask Returns ------- PIL.Image.Image Image with the mask applied """ def __init__(self, img: PIL.Image.Image) -> None: self.img = img def __call__(self, mask: np.ndarray) -> PIL.Image.Image: return util.apply_mask_image(self.img, mask)
[docs]class Invert(ImageFilter): r"""Invert an image, i.e. take the complement of the correspondent array. For binary images, the inversion flips True and False values. For RGB images, each pixel value p is replaced with :math:`\hat{p}-p` where :math:`\hat{p}` is the maximum value of pixels of the data type (i.e. 255). Usually, the tissue in a WSI is surrounded by a white light background (values close to 255). Therefore, inverting its values could ease the removal of non-tissue regions (values close or equal to 0). .. figure:: https://user-images.githubusercontent.com/31658006/116548383-6aaad800-a8f4-11eb-8ebd-46c873046447.png Parameters ---------- img : PIL.Image.Image Input image Returns ------- PIL.Image.Image Inverted image Example: >>> from PIL import Image >>> from histolab.filters.image_filters import Invert, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> invert = Invert() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_inv_rgb = invert(image_rgb) >>> image_inv_gray = invert(image_gray) """ # noqa def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: return F.invert(img)
[docs]class RgbToGrayscale(ImageFilter): """Convert an RGB image to a grayscale image. Parameters ---------- img : PIL.Image.Image Input image Returns ------- PIL.Image.Image Grayscale image Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> image_gray = rgb_to_grayscale(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: return PIL.ImageOps.grayscale(img)
[docs]class RgbToHed(ImageFilter): """Convert RGB channels to HED channels. image color space (RGB) is converted to Hematoxylin-Eosin-Diaminobenzidine space. Parameters ---------- img : PIL.Image.Image Input image Returns ------- np.ndarray Array representation of the image in HED space Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RgbToHed >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_hed = RgbToHed() >>> image_hed = rgb_to_hed(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> np.ndarray: hed = F.rgb_to_hed(img) return hed
[docs]class RgbToLab(ImageFilter): """Convert from the sRGB color space to the CIE Lab colorspace. sRGB color space reference: IEC 61966-2-1:1999 Parameters ---------- img : PIL.Image.Image Input image illuminant : {"A", "B", "C", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). observer : {"2", "10", "R"}, optional The aperture angle of the observer. Returns ------- np.ndarray Array representation of the image in LAB space Raises ------ Exception If the ``img`` mode is not RGB Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RgbToLab >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_lab = RgbToLab() >>> image_lab = rgb_to_lab(image_rgb) """ # noqa def __init__(self, illuminant: str = "D65", observer: int = "2") -> None: self.illuminant = illuminant self.observer = observer def __call__(self, img: PIL.Image.Image) -> np.ndarray: lab = F.rgb_to_lab(img, self.illuminant, self.observer) return lab
[docs]class RgbToOd(ImageFilter): """Convert from RGB to optical density (OD_RGB) space. Parameters ---------- img : PIL.Image.Image Input image Returns ------- np.ndarray Array representation of the image in OD space Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RgbToOd >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_od = RgbToOd() >>> image_od = rgb_to_od(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> np.ndarray: od = F.rgb_to_od(img) return od
[docs]class HedToRgb(ImageFilter): """Convert HED channels to RGB channels. Parameters ---------- img_arr : np.ndarray Array representation of the image in HED color space Returns ------- PIL.Image.Image Image in RGB space Example: >>> import numpy as np >>> from histolab.filters.image_filters import HedToRgb >>> hed_arr = np.load("tests/fixtures/arrays/diagnostic-slide-thumb-hed.npy") >>> hed_to_rgb = HedToRgb() >>> rgb = hed_to_rgb(hed_arr) """ # noqa def __call__(self, img_arr: np.array) -> PIL.Image.Image: rgb = F.hed_to_rgb(img_arr) return rgb
[docs]class HematoxylinChannel(ImageFilter): """Obtain Hematoxylin channel from RGB image. Input image is first converted into HED space and the hematoxylin channel is extracted via color deconvolution. Parameters ---------- img : PIL.Image.Image Input RGB image Returns ------- PIL.Image.Image RGB image with Hematoxylin staining separated. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import HematoxylinChannel >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> hematoxylin_channel = HematoxylinChannel() >>> image_h = hematoxylin_channel(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: hematoxylin = F.hematoxylin_channel(img) return hematoxylin
[docs]class EosinChannel(ImageFilter): """Obtain Eosin channel from RGB image. Input image is first converted into HED space and the Eosin channel is extracted via color deconvolution. Parameters ---------- img : PIL.Image.Image Input RGB image Returns ------- PIL.Image.Image RGB image with Eosin staining separated. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import EosinChannel >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> eosin_channel = EosinChannel() >>> image_e = eosin_channel(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: eosin = F.eosin_channel(img) return eosin
[docs]class DABChannel(ImageFilter): """Obtain DAB channel from RGB image. Input image is first converted into HED space and the DAB channel is extracted via color deconvolution. Parameters ---------- img : PIL.Image.Image Input RGB image Returns ------- PIL.Image.Image RGB image with Eosin staining separated. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import DABChannel >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> dab_channel = DABChannel() >>> image_d = dab_channel(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: dab = F.dab_channel(img) return dab
[docs]class RgbToHsv(ImageFilter): """Convert RGB channels to HSV channels. image color space (RGB) is converted to Hue - Saturation - Value (HSV) space. Parameters ---------- img : PIL.Image.Image Input image Returns ------- np.ndarray Array representation of the image in HSV space Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RgbToHsv >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_hsv = RgbToHsv() >>> image_hsv = rgb_to_hsv(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> np.ndarray: hsv = F.rgb_to_hsv(img) return hsv
[docs]class StretchContrast(ImageFilter): r"""Increase image contrast. A simple way to enhance the contrast in an image is to linearly rescale the intensity values within a desired range :math:`[v_{o,l}, v_{o,h}]`. In particular, if the lowest and highest pixel values of the input image are, respectively, :math:`v_{i,l}` and :math:`v_{i,h}`, an input pixel :math:`p_i` is remapped to the output pixel value: .. math:: p_o = (p_i - v_{i,l})\left(\frac{v_{o,h}- v_{o,l}}{v_{i,h}- v_{i,l}}\right)+v_{o,l} The Stretch Contrast filter stretches the intensity values in an image, with :math:`v_{o,l}=40` and :math:`v_{o,l}=60` as default values. This filter is useful to highlight details in the input image. .. figure:: https://user-images.githubusercontent.com/31658006/116539805-9f656200-a8e9-11eb-913b-864c0a9d8baf.png Parameters ---------- img : PIL.Image.Image Input image low : int, optional Range low value (0 to 255). Default is 40. high : int, optional Range high value (0 to 255). Default is 60 Returns ------- PIL.Image.Image Image with contrast enhanced. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RgbToGrayscale, StretchContrast >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> stretch_contrast = StretchContrast() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_stretched = stretch_contrast(image_gray) """ # noqa def __init__(self, low: int = 40, high: int = 60) -> None: self.low = low self.high = high def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: stretch_contrast = F.stretch_contrast(img, self.low, self.high) return stretch_contrast
[docs]class HistogramEqualization(ImageFilter): r"""Increase image contrast using histogram equalization. The input image (gray or RGB) is filterd using histogram equalization to increase contrast. In particular, this filter expands the range of intensity values in low contrast images. It first computes the normalized histogram H of an image: H(k) counts pixels with intensity values k, divided by the total number of pixels in the image. Then, it computes the cumulative sum of the histogram values as .. math:: C[i] = \sum_{k=0}^{i} H[k] for i =0...255. Finally, for each pixel P, the algorithm computes a new value .. math:: p\prime = 255 \cdot C[p]. The resulting image will have a uniform intensity distribution. The algorithm described is also called non-adaptive uniform histogram equalization, as it works uniformly on the whole image and the transformation of one pixel is independent from the transformation used on the neighboring pixels [4]_. .. figure:: https://user-images.githubusercontent.com/20052362/139671873-fa61fc20-a6b8-4302-8596-b6542b5b3aaf.png Notice that the histogram equalization method can be used for RGB images by applying the same algorithm on the R, G, and B channels separately [5]_; nonetheless, the high correlation of the three channels may distort the image and the color balance can change drastically. Parameters ---------- img : PIL.Image.Image Input image. n_bins : int. optional Number of histogram bins. Default is 256. Returns ------- PIL.Image.Image Image with contrast enhanced by histogram equalization. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import HistogramEqualization, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> histogram_equalization = HistogramEqualization() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_he = histogram_equalization(image_gray) References -------- .. [4] T Strothotte and S Schlechtweg. “Non-photorealistic computer graphics: modeling, rendering, and animation”. Morgan Kaufmann (2002) .. [5] Z Rong and et al. “Study of color heritage image enhancement algorithms based on histogram equalization”. Optik 126.24 (2015) """ # noqa def __init__(self, n_bins: int = 256) -> None: self.n_bins = n_bins def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: hist_equ = F.histogram_equalization(img, self.n_bins) return hist_equ
[docs]class AdaptiveEqualization(ImageFilter): """Increase image contrast using adaptive equalization. Rather than considering the global contrast in the image, the adaptive histogram equalization method applies the histogram equalization to smaller regions, or tiles, of the image; the tiles are then combined together using bilinear interpolation. This local approach is preferred when the image presents significantly darker or lighter regions that may be poorly enhanced by the global histogram equalization transformation. .. figure:: https://user-images.githubusercontent.com/20052362/139672710-e75d1f0e-e5f3-4365-8f83-b0d2316bfb10.png The Adaptive Equalization filter is based on the scikit-image implementation of the contrast limited adaptive histogram equalization (CLAHE) [1]_. Parameters ---------- img : PIL.Image.Image Input image (gray or RGB) nbins : int, optional Number of histogram bins. Default is 256. clip_limit : float, optional Clipping limit where higher value increases contrast. Default is 0.01. Returns ------- PIL.Image.Image Image with contrast enhanced by adaptive equalization. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import AdaptiveEqualization, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> adaptive_equalization = AdaptiveEqualization() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_clahe = adaptive_equalization(image_gray) References -------- .. [1] S.M. Pizer andet al. "Adaptive histogram equalization and its variations”, Comput Vis Graph Image Process 39.3 (1987). """ # noqa def __init__(self, n_bins: int = 256, clip_limit: float = 0.01) -> None: self.n_bins = n_bins self.clip_limit = clip_limit def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: adaptive_equ = F.adaptive_equalization(img, self.n_bins, self.clip_limit) return adaptive_equ
[docs]class LabToRgb(ImageFilter): """Lab to RGB color space conversion. Parameters ---------- img : np.array Input image in Lab space. illuminant : {"A", "B", "C", "D50", "D55", "D65", "D75", "E"}, optional The name of the illuminant (the function is NOT case sensitive). Default is "D65". observer : {"2", "10", "R"}, optional The aperture angle of the observer. Default is "2". Returns ------- PIL.Image.Image Image in RGB space. Example: >>> import numpy as np >>> from histolab.filters.image_filters import LabToRgb >>> arr_lab = np.load("tests/fixtures/arrays/diagnostic-slide-thumb-lab.npy") >>> lab_to_rgb = LabToRgb() >>> image_rgb = lab_to_rgb(arr_lab) """ def __init__(self, illuminant: str = "D65", observer: int = "2") -> None: self.illuminant = illuminant self.observer = observer def __call__(self, np_arr: np.ndarray) -> PIL.Image.Image: lab = F.lab_to_rgb(np_arr, self.illuminant, self.observer) return lab
[docs]class LocalEqualization(ImageFilter): """Filter gray image using local equalization. Local equalization method uses local histograms based on a disk structuring element. Parameters --------- img : PIL.Image.Image Grayscale input image disk_size : int, optional Radius of the disk structuring element used for the local histograms. Default is 50 Returns ------- PIL.Image.Image Grayscale image with contrast enhanced using local equalization. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import LocalEqualization >>> image_rgb = Image.open("tests/fixtures/pil-images-gs/diagnostic-slide-thumb-gs.png") >>> local_equ = LocalEqualization() >>> local_equ_image = local_equ(image_rgb) """ # noqa def __init__(self, disk_size: int = 50) -> None: self.disk_size = disk_size def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: local_equ = F.local_equalization(img, self.disk_size) return local_equ
[docs]class KmeansSegmentation(ImageFilter): """Segment an RGB image with K-means segmentation By using K-means segmentation (color/space proximity) each segment is colored based on the average color for that segment. .. figure:: https://user-images.githubusercontent.com/20052362/144853490-741deb95-cd84-47b0-8227-38e6091753d8.png :figwidth: 60 % :align: center Parameters --------- img : PIL.Image.Image Input image n_segments : int, optional The number of segments. Default is 800. compactness : float, optional Color proximity versus space proximity factor. Default is 10.0. Returns ------- PIL.Image.Image Image where each segment has been colored based on the average color for that segment. Raises ------ ValueError If ``img`` mode is RGBA. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import KmeansSegmentation >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> kmeans_segmentation = KmeansSegmentation() >>> kmeans_segmented_image = kmeans_segmentation(image_rgb) """ # noqa def __init__(self, n_segments: int = 800, compactness: float = 10.0) -> None: self.n_segments = n_segments self.compactness = compactness def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: kmeans_segmentation = F.kmeans_segmentation( img, self.n_segments, self.compactness ) return kmeans_segmentation
[docs]class RagThreshold(ImageFilter): """Combine similar K-means segmented regions based on threshold value. Segment an image with K-means, build region adjacency graph based on the segments, combine similar regions based on threshold value, and then output these resulting region segments. .. figure:: https://user-images.githubusercontent.com/20052362/144852689-6098a415-1714-4abf-a361-485761647349.png :figwidth: 60 % :align: center Parameters ---------- img : PIL.Image.Image Input image n_segments : int, optional The number of segments. Default is 800. compactness : float, optional Color proximity versus space proximity factor. Default is 10.0 threshold : int, optional Threshold value for combining regions. Default is 9. return_labels : bool, optional If True, returns a labeled array where the value denotes segment membership. Otherwise, returns a PIL image where each segment is colored by the average color in it. Default is False. Returns ------- PIL.Image.Image, if not ``return_labels`` Each segment has been colored based on the average color for that segment (and similar segments have been combined). np.ndarray, if ``return_labels`` Value denotes segment membership. Raises ------ ValueError If ``img`` mode is RGBA. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RagThreshold >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rag_threshold = RagThreshold() >>> rag_thresholded_array = rag_threshold(image_rgb) """ # noqa def __init__( self, n_segments: int = 800, compactness: float = 10.0, threshold: int = 9, return_labels: bool = False, ) -> None: self.n_segments = n_segments self.compactness = compactness self.threshold = threshold self.return_labels = return_labels def __call__( self, img: PIL.Image.Image, mask: np.ndarray = None, ) -> Union[PIL.Image.Image, np.ndarray]: return F.rag_threshold( img, n_segments=self.n_segments, compactness=self.compactness, threshold=self.threshold, mask=mask, return_labels=self.return_labels, )
[docs]class HysteresisThreshold(ImageFilter): r"""Apply two-level (hysteresis) threshold to an image. The hysteresis thresholding is a two-threshold method used to detect objects on an image, based on the assumption that points connected to an object are most likely objects themselves. In particular, pixels above a specified high threshold :math:`t_h`are considered as strong objects, pixels below a specified low threshold :math:`t_l` are labelled as non-objects, and pixels :math:`o\in[t_l, t_h]` are defined as weak objects; all the non-objects are removed, while the weak objects are kept only if connected to a strong one. The hysteresis thresholding can be applied to detect edges in an image. .. figure:: https://user-images.githubusercontent.com/31658006/116542328-d5f0ac00-a8ec-11eb-9f05-696ca0598fd4.png Parameters ---------- img : PIL.Image.Image Input image low : int, optional low threshold. Default is 50. high : int, optional high threshold. Default is 100 Returns ------- PIL.Image.Image Image with the hysteresis threshold applied Example: >>> from PIL import Image >>> from histolab.filters.image_filters import HysteresisThreshold, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> hyst_threshold = HysteresisThreshold(low=200, high=250) >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_thresholded = hyst_threshold(image_gray) """ # noqa def __init__(self, low: int = 50, high: int = 100) -> None: self.low = low self.high = high def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: return F.hysteresis_threshold(img, self.low, self.high)
[docs]class LocalOtsuThreshold(ImageFilter): """Mask image based on local Otsu threshold. Compute Otsu threshold for each pixel and return the image thresholded locally. Note that the input image must be 2D. Parameters ---------- img : PIL.Image.Image Input 2-dimensional image disk_size : float, optional Radius of the disk structuring element used to compute the Otsu threshold for each pixel. Default is 3.0 Returns ------- PIL.Image.Image Image thresholded with the Otsu algorithm computed locally Example: >>> from PIL import Image >>> from histolab.filters.image_filters import LocalOtsuThreshold, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> local_otsu = LocalOtsuThreshold() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_thresholded_locally = local_otsu(image_gray) """ # noqa def __init__(self, disk_size: float = 3.0) -> None: self.disk_size = disk_size def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.local_otsu_threshold(img, self.disk_size)
# ----------- Branching functions (grayscale/invert input)------------------- # invert --> grayscale ..> hysteresis
[docs]class HysteresisThresholdMask(ImageFilter): """Mask an image using hysteresis threshold Compute the Hysteresis threshold on the complement of a grayscale image, and return boolean mask based on pixels above this threshold. Parameters ---------- img : PIL.Image.Image Input image. low : int, optional low threshold. Default is 50. high : int, optional high threshold. Default is 100. Returns ------- np.ndarray Boolean NumPy array where True represents a pixel above hysteresis threshold. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import HysteresisThresholdMask, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> hyst_threshold_mask = HysteresisThresholdMask() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_thresholded_array = hyst_threshold_mask(image_gray) """ # noqa def __init__(self, low: int = 50, high: int = 100) -> None: self.low = low self.high = high def __call__(self, img: PIL.Image.Image) -> np.ndarray: hysteresis_threshold_mask = F.hysteresis_threshold_mask( img, self.low, self.high ) return hysteresis_threshold_mask
[docs]class OtsuThreshold(ImageFilter): """Mask image based on pixel above Otsu threshold. Compute Otsu threshold on image as a NumPy array and return boolean mask based on pixels above this threshold. The Otsu algorithm is a standard method to automatically compute the optimal threshold value to separate image background from the foreground [7]_. In this filter, the pixels below the Otsu threshold are considered as foreground. Note that Otsu threshold is expected to work correctly only for grayscale images. .. figure:: https://user-images.githubusercontent.com/31658006/116542034-76929c00-a8ec-11eb-98ca-9e0d283cdcbb.png Parameters ---------- img : PIL.Image.Image Input image. relate : operator, optional Operator to be used to compute the mask from the threshold. Default is operator.lt Returns ------- np.ndarray Boolean NumPy array where True represents a pixel above Otsu threshold. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import OtsuThreshold, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> otsu_threshold = OtsuThreshold() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_thresholded_array = otsu_threshold(image_gray) Reference --------- .. [7] N Otsu. “A threshold selection method from gray-level histograms”. IEEE Trans SystMan Cybern Syst 9.1 (1979) """ # noqa def __init__(self, relate: Callable[..., Any] = operator.lt): self.relate = relate def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.otsu_threshold(img, self.relate)
[docs]class FilterEntropy(ImageFilter): """Filter image based on entropy (complexity). Entropy measures complexity in an image: the greater the entropy the more heterogeneous structures are found is the image, while slide backgrounds are usually less complex. This method filters out pixels of grayscale images based on the local entropy. In details: (i) the entropy is computed on a neighborhood defined by a squared all-ones matrix of size n (by default n=9); (ii) pixels with entropy greater than a specified threshold t (by default t=5) are replaced with 1, 0 otherwise. This entropy filter can be used to detect highly hematoxylin-stained regions, which represent dense accumulation of nuclei (complex structures). Note that input must be 2D. .. figure:: https://user-images.githubusercontent.com/31658006/116543013-a1312480-a8ed-11eb-8f75-b25164286994.png Parameters ---------- img : PIL.Image.Image input 2-dimensional image neighborhood : int, optional Neighborhood size (defines height and width of 2D array of 1's). Default is 9. threshold : float, optional Threshold value. Default is 5.0 Returns ------- np.ndarray NumPy boolean array where True represent a measure of complexity. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import FilterEntropy, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> entropy_filter = FilterEntropy() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_thresholded_array = entropy_filter(image_gray) """ # noqa def __init__(self, neighborhood: int = 9, threshold: float = 5.0) -> None: self.neighborhood = neighborhood self.threshold = threshold def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.filter_entropy(img, self.neighborhood, self.threshold)
[docs]class CannyEdges(ImageFilter): r"""Filter image based on Canny edge algorithm. The Canny edge detector has been used to generate a version of the image that highlights edges within tissue fragments by detecting changes in pixel intensity [2]_ [3]_. The algorithm includes five steps: (i) smoothing the image (i.e. remove the noise); (ii) computing the gradient's magnitude :math:`M_\nabla` and direction :math:`\theta_\nabla`; (iii) keeping the direction :math:`\theta_\nabla` with greatest intensity :math:`M_\nabla` for each pixel; (iv) thinning the edges by suppressing non-maximal pixels; (v) applying the hysteresis thresholding algorithm for the final edge detection. Note that input image must be 2D. .. figure:: https://user-images.githubusercontent.com/20052362/144851746-3332560c-bd61-4b5a-baa3-6a8a528c0db0.png Parameters ---------- img : PIL.Image.Image Input 2-dimensional image sigma : float, optional Width (std dev) of Gaussian. Default is 1.0. low_threshold : float, optional Low hysteresis threshold value. Default is 0.0. high_threshold : float, optional High hysteresis threshold value. Default is 25.0. Returns ------- np.ndarray Boolean NumPy array representing Canny edge map. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import CannyEdges, RgbToGrayscale >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> canny_edges_detection = CannyEdges() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_thresholded_array = canny_edges_detection(image_gray) References ---------- .. [2] A Kumar and M Prateek. “Localization of Nuclei in Breast Cancer Using Whole SlideImaging System Supported by Morphological Features and Shape Formulas”. CancerManag Res 12 (2020) .. [3] M Mũnoz-Aguirre and et al. “PyHIST: A Histological Image Segmentation Tool”. PLOS Comput Biol 16.10 (2020) """ # noqa def __init__( self, sigma: float = 1.0, low_threshold: float = 0.0, high_threshold: float = 25.0, ) -> None: self.sigma = sigma self.low_threshold = low_threshold self.high_threshold = high_threshold def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.canny_edges(img, self.sigma, self.low_threshold, self.high_threshold)
[docs]class Grays(ImageFilter): """Filter out gray pixels in RGB image. Gray pixels are those pixels where the red, green, and blue channel values are similar, i.e. under a specified tolerance. Parameters ---------- img : PIL.Image.Image Input image tolerance : int, optional if difference between values is below this threshold, values are considered similar and thus filtered out. Default is 15. Returns ------- PIL.Image.Image Mask image where the grays values are masked out Example: >>> from PIL import Image >>> from histolab.filters.image_filters import Grays >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> grays_filter = Grays(tolerance=5) >>> filtered_mask = grays_filter(image_rgb) """ # noqa def __init__(self, tolerance: int = 15) -> None: self.tolerance = tolerance def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: return F.grays(img, self.tolerance)
[docs]class GreenChannelFilter(ImageFilter): """Mask pixels in an RGB image with G-channel greater than a specified threshold. Create a binary mask where pixels with the green channel value above a specified threshold (by default 200) are set to 0. This filtering method can be used to detect tissue in H&E-stained images, considering that the green dye is poorly used in the tissue-related stains, i.e. eosin (pink) and hematoxylin (purple). To avoid over-masking the image, the overmask_thresh parameter defines the maximum percentage of tissue that can be masked by the green channel filter (by default 90%). This method alone may be sufficient to segment tissue on H&E-stained images. .. figure:: https://user-images.githubusercontent.com/31658006/116541610-f53b0980-a8eb-11eb-939d-944ebc7c87ee.png Parameters ---------- img : PIL.Image.Image Input RGB image green_thresh : int, optional Green channel threshold value (0 to 255). Default is 200. If value is greater than green_thresh, mask out pixel. avoid_overmask : bool, optional If True, avoid masking above the overmask_thresh percentage. Default is True. overmask_thresh : float, optional If avoid_overmask is True, avoid masking above this percentage value. Default is 90. Returns ------- np.ndarray Boolean mask where pixels above a particular green channel threshold have been masked out. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import GreenChannelFilter >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> g_channel_filter = GreenChannelFilter(avoid_overmask=True, overmask_thresh=90) >>> image_thresholded_array = g_channel_filter(image_rgb) """ # noqa def __init__( self, green_thresh: int = 200, avoid_overmask: bool = True, overmask_thresh: float = 90.0, ) -> None: self.green_thresh = green_thresh self.avoid_overmask = avoid_overmask self.overmask_thresh = overmask_thresh def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.green_channel_filter( img, self.green_thresh, self.avoid_overmask, self.overmask_thresh )
[docs]class RedFilter(ImageFilter): """Mask reddish colors in an RGB image. Create a mask to filter out reddish colors, where the mask is based on a pixel being above a red channel threshold value, below a green channel threshold value, and below a blue channel threshold value. Parameters ---------- img : PIl.Image.Image Input RGB image red_lower_thresh : int Red channel lower threshold value. green_upper_thresh : int Green channel upper threshold value. blue_upper_thresh : int Blue channel upper threshold value. Returns ------- np.ndarray Boolean NumPy array representing the mask. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RedFilter >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-red-pen.png") >>> red_filter = RedFilter(10, 30, 25) >>> mask_filtered = red_filter(image_rgb) """ def __init__(self, red_thresh: int, green_thresh: int, blue_thresh: int) -> None: self.red_thresh = red_thresh self.green_thresh = green_thresh self.blue_thresh = blue_thresh def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.red_filter(img, self.red_thresh, self.green_thresh, self.blue_thresh)
[docs]class RedPenFilter(ImageFilter): """Filter out red pen marks on diagnostic slides. The resulting mask is a composition of red filters with different thresholds for the RGB channels. Parameters ---------- img : PIL.Image.Image Input RGB image. Returns ------- PIL.Image.Image Image the green red marks filtered out. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RedPenFilter >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-red-pen.png") >>> red_pen_filter = RedPenFilter() >>> image_no_red = red_pen_filter(image_rgb) """ def __call__(self, img: PIL.Image.Image): return F.red_pen_filter(img)
[docs]class GreenFilter(ImageFilter): """Filter out greenish colors in an RGB image. The mask is based on a pixel being above a red channel threshold value, below a green channel threshold value, and below a blue channel threshold value. Note that for the green ink, the green and blue channels tend to track together, so for blue channel we use a lower threshold rather than an upper threshold value. Parameters ---------- img : PIL.image.Image RGB input image. red_thresh : int Red channel upper threshold value. green_thresh : int Green channel lower threshold value. blue_thresh : int Blue channel lower threshold value. Returns ------- np.ndarray Boolean NumPy array representing the mask. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import GreenFilter >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-red-pen.png") >>> green_filter = GreenFilter(230, 10, 105) >>> mask_filtered = green_filter(image_rgb) """ def __init__(self, red_thresh, green_thresh, blue_thresh): self.red_thresh = red_thresh self.green_thresh = green_thresh self.blue_thresh = blue_thresh def __call__(self, img): return F.green_filter(img, self.red_thresh, self.green_thresh, self.blue_thresh)
[docs]class GreenPenFilter(ImageFilter): """Filter out green pen marks from a diagnostic slide. The resulting mask is a composition of green filters with different thresholds for the RGB channels. .. figure:: https://user-images.githubusercontent.com/31658006/116548722-f290e200-a8f4-11eb-9780-0ce5844295dd.png Parameters --------- img : PIL.Image.Image Input RGB image Returns ------- PIL.Image.Image Image the green pen marks filtered out. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import GreenPenFilter >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-green-pen.png") >>> green_pen_filter = GreenPenFilter() >>> image_no_green = green_pen_filter(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: return F.green_pen_filter(img)
[docs]class BlueFilter(ImageFilter): """Filter out blueish colors in an RGB image. Create a mask to filter out blueish colors, where the mask is based on a pixel being above a red channel threshold value, above a green channel threshold value, and below a blue channel threshold value. Parameters ---------- img : PIl.Image.Image Input RGB image red_thresh : int Red channel lower threshold value. green_thresh : int Green channel lower threshold value. blue_thresh : int Blue channel upper threshold value. Returns ------- np.ndarray Boolean NumPy array representing the mask. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import BlueFilter >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/wsi-blue-pen.png") >>> blue_filter = BlueFilter(30, 20, 105) >>> mask_filtered = blue_filter(image_rgb) """ # noqa def __init__(self, red_thresh: int, green_thresh: int, blue_thresh: int): self.red_thresh = red_thresh self.green_thresh = green_thresh self.blue_thresh = blue_thresh def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.blue_filter(img, self.red_thresh, self.green_thresh, self.blue_thresh)
[docs]class BluePenFilter(ImageFilter): """Filter out blue pen marks from a diagnostic slide. The resulting mask is a composition of green filters with different thresholds for the RGB channels. Parameters --------- img : PIL.Image.Image Input RGB image Returns ------- np.ndarray NumPy array representing the mask with the blue pen marks filtered out. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import BluePenFilter >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/wsi-blue-pen.png") >>> blue_pen_filter = BluePenFilter() >>> image_no_blue = blue_pen_filter(image_rgb) """ # noqa def __call__(self, img: PIL.Image.Image) -> PIL.Image.Image: return F.blue_pen_filter(img)
[docs]class YenThreshold(ImageFilter): r"""Mask image based on pixel above Yen threshold. Compute Yen threshold on image and return boolean mask based on pixels below this threshold. The Yen method [8]_ is a multi-level image thresholding approach to separate objects from the background. It automatically computes the threshold that maximize the entropic correlation EC for a given gray level s defined as: .. math:: EC(s) = -\ln{(G(s)\cdot G'(s))} + 2\ln(P(s)\cdot (1-P(s)) where :math:`\displaystyle{G(s)=\sum_{i=0}^{s-1}p_i^2}`, :math:`\displaystyle{G'(s)=\sum_{i=s}^{m-1}p_i^2}`, m is the number of gray levels in the image, :math:`p_i` is the probability of the gray level i and :math:`\displaystyle{P(s)=\sum_{i=0}^{s-1}p_i}` is the total probability up to gray level (s-1). In this filter, pixels below the computed threshold are considered as foreground. .. figure:: https://user-images.githubusercontent.com/31658006/116542194-ab065800-a8ec-11eb-9fea-24dd97de8226.png Parameters ---------- img : PIL.Image.Image Input image. relate : operator, optional Operator to be used to compute the mask from the threshold. Default is operator.lt Returns ------- np.ndarray Boolean NumPy array where True represents a pixel below Yen's threshold. Example: >>> from PIL import Image >>> from histolab.filters.image_filters import RgbToGrayscale, YenThreshold >>> image_rgb = Image.open("tests/fixtures/pil-images-rgb/tcga-lung-rgb.png") >>> rgb_to_grayscale = RgbToGrayscale() >>> yen_threshold = YenThreshold() >>> image_gray = rgb_to_grayscale(image_rgb) >>> image_thresholded_array = yen_threshold(image_gray) References ---------- .. [8] J.C. Yen and et al.“A new criterion for automatic multilevel thresholding”. IEEE Trans Image Process 4.3 (1995) """ # noqa def __init__(self, relate: Callable[..., Any] = operator.lt): self.relate = relate def __call__(self, img: PIL.Image.Image) -> np.ndarray: return F.yen_threshold(img, self.relate)