# US 12,181,422 B2 · Probabilistic image analysis

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- Number: US 12,181,422 B2
- Document ID: US12181422B2
- Status: Granted
- Kind: B2
- Application number: 17/642,381
- Filed: 2020-09-15
- Published: 2022-10-13
- Granted: 2024-12-31
- Earliest claimed: 2019-09-16
- Assignees: Rapiscan Holdings, Inc.
- Applicants: Rapiscan Holdings, Inc.
- Inventors: James Manalad; Philippe Desjeans-Gauthier; Simon Archambault; William Awad; Francois Brillon
- Web page: https://patentgrep.com/patent/US12181422B2

## CPC

- G01N 23/04: and forming images of the material
- A61B 6/4241: {using energy resolving detectors, e.g. photon counting}
- A61B 6/482: {involving multiple energy imaging}
- G06F 18/24137: {Distances to cluster centroïds}
- G06N 3/045: Combinations of networks
- G06N 3/0464: Convolutional networks [CNN, ConvNet]
- G06N 3/084: Backpropagation, e.g. using gradient descent
- G06N 3/09: Supervised learning
- G06V 10/56: relating to colour
- G06V 10/82: using neural networks
- A61B 6/5205: {involving processing of raw data to produce diagnostic data}
- G01N 23/083: the radiation being X-rays
- G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q)
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

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.

## Claims

### Claim 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, wherein the probability value is indicative of whether the pixel is likely associated, or not likely associated, with a potential threat; and,
classifying each pixel in the raw data x-ray image into at least one of a first classification or second classification based on whether the probability value associated with the pixel exceeds, or does not exceed, a predetermined threshold probability value.

### Claim 2 (depends on 1)

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.

### Claim 3 (depends on 1)

The method of claim 1, wherein the outputting step outputs a probability map for each pixel in the raw data x-ray image.

### Claim 4 (depends on 3)

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.

### Claim 5 (depends on 4)

The method of claim 4, wherein the first colour scheme and the second colour scheme at least one of flashes, shifts hue and shifts luma.

### Claim 6 (depends on 1)

The method of claim 1, wherein the classifying step is by way of semantic segmentation.

### Claim 7 (depends on 1)

The method of claim 1, wherein the neural network is a convolutional neural network.

### Claim 8 (depends on 7)

The method of claim 7, wherein the convolutional neural network is a FC-Densenet.

### Claim 9 (depends on 1)

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.

### Claim 10

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, wherein the probability value is indicative of whether the pixel is likely associated, or not likely associated, with a potential threat; and,
classify each pixel in the raw data x-ray image into at least one of a first classification or second classification based on whether the probability value associated with the pixel exceeds, or does not exceed, a predetermined threshold probability value.

### Claim 11 (depends on 10)

The system of claim 10, 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.

### Claim 12 (depends on 10)

The system of claim 10, 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.

### Claim 13 (depends on 12)

The system of claim 12 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.

### Claim 14 (depends on 13)

The system of claim 13, wherein the first colour scheme and the second colour scheme at least one of flashes, shifts hue and shifts luma.

### Claim 15 (depends on 10)

The system of claim 10, wherein the neural network is configured to classify each pixel in the raw data x-ray image by way of semantic segmentation.

### Claim 16 (depends on 10)

The system of claim 10, wherein the neural network is a convolutional neural network.

### Claim 17 (depends on 16)

The system of claim 16, wherein the convolutional neural network is a FC-Densenet.