Probabilistic Image Analysis
A method for detecting at least one object of interest in at least one raw data x-ray image includes the steps of emitting an incident x-ray radiation beam through a scanning volume having an object therein, detecting x-ray signals transmitted through at least one of the scanning volume and the object, deriving the at least one raw data x-ray image from the detected x-ray signals, inputting the raw data x-ray image, expressed according to an attenuation scale, into a neural network, for each pixel in the raw data x-ray image, outputting from the neural network a probability value assigned to that pixel, and, classifying each pixel in the raw data x-ray image into a first classification if the probability value associated with the pixel exceeds a predetermined threshold probability value and in a second classification if the probability value associated with the pixel is below the predetermined threshold probability value.
1 . A method for detecting at least one object of interest in at least one raw data x-ray image, the method comprising the steps of:
emitting an incident x-ray radiation beam through a scanning volume having an object therein;
detecting x-ray signals transmitted through at least one of the scanning volume and the object;
deriving the at least one raw data x-ray image from the detected x-ray signals;
inputting the raw data x-ray image, expressed according to an attenuation scale, into a neural network;
for each pixel in the raw data x-ray image, outputting from the neural network a probability value assigned to that pixel; and,
classifying each pixel in the raw data x-ray image into a first classification if the probability value associated with the pixel exceeds a predetermined threshold probability value and in a second classification if the probability value associated with the pixel is below the predetermined threshold probability value.
2 . The method of claim 1 , wherein the step of inputting the raw data x-ray image expressed according to an attenuation scale further comprises the steps of:
determining a transmittance value for each pixel in the raw data x-ray image; and,
determining an attenuation value from each transmittance value.
3 . The method of claim 1 , wherein the outputting step outputs a probability map for each pixel in the raw data x-ray image.
4 . The method of claim 1 , wherein the classifying step is by way of semantic segmentation.
5 . The method of claim 1 , wherein the first classification indicates that the pixel is likely associated with a potential threat and the second classification indicates that the pixel is not likely to be associated with a potential threat.
6 . The method of claim 1 , wherein the neural network is a convolutional neural network.
7 . The method of claim 6 , wherein the convolutional neural network is a FC-Densenet.
8 . The method of claim 3 , wherein the method further comprises:
providing a colour-mapped image based on the probability map showing pixels classified in the first classification in a first colour scheme and pixels classified in the second classification in a second colour scheme.
9 . The method of claim 8 , wherein the first colour scheme and the second colour scheme at least one of flashes, shifts hue and shifts luma.
10 . The method of claim 1 , wherein the at least one raw data x-ray image includes a set of raw data dual-energy x-ray images.
11 . A system for detecting at least one object of interest in at least one raw data x-ray image, comprising:
an x-ray emitter for emitting an incident x-ray radiation beam through a scanning volume having an object therein;
at least one detector for detecting x-ray signals transmitted through at least one of the scanning volume and the object;
at least one processor for deriving at least one raw data x-ray image from the detected x-ray signal;
at least one processor configured to:
input the raw data x-ray image, expressed according to an attenuation scale, into a neural network;
output from the neural network a probability value assigned to each pixel in the raw data x-ray image; and,
classify each pixel in the raw data x-ray image into a first classification if the probability value associated with the pixel exceeds a predetermined threshold probability value and in a second classification if the probability value associated with the pixel is below the predetermined threshold probability value.
12 . The system of claim 11 , wherein to express the raw data x-ray image according to an attenuation scale, the at least one processor is further configured to:
determine a transmittance value for each pixel in the raw data x-ray image; and,
determine an attenuation value from each transmittance value.
13 . The system of claim 11 , wherein to output the probability value assigned to each pixel in the raw data x-ray image, the at least one processor is further configured to output a probability map for each pixel in the raw data x-ray image.
14 . The system of claim 11 , wherein the neural network is configured to classify each pixel in the raw data x-ray image by way of semantic segmentation.
15 . The system of claim 11 , wherein the first classification indicates that the pixel is likely associated with a potential threat and the second classification indicates that the pixel is not likely to be associated with a potential threat.
16 . The system of claim 11 , wherein the neural network is a convolutional neural network.
17 . The system of claim 16 , wherein the convolutional neural network is a FC-Densenet.
18 . The system of claim 13 wherein the at least one processor is further configured to provide a colour-mapped image showing pixels in the first classification in a first colour scheme and pixels in the second classification in a second colour scheme.
19 . The system of claim 18 , wherein the first colour scheme and the second colour scheme at least one of flashes, shifts hue and shifts luma.
20 . A method for determining a presence of an object of interest, the method comprising the steps of:
deriving a raw data image representative of at least a portion of an object;
inputting the raw data image, expressed according to an attenuation scale, into a neural network;
for each pixel in the raw data image, outputting from the neural network a probability value assigned to that pixel; and,
classifying each pixel in the raw data image into a first classification if the probability value associated with the pixel exceeds a predetermined threshold probability value and in a second classification if the probability value associated with the pixel is below the predetermined threshold probability value.