# US 2017/0287133 A1 · Method and system for generating multiparametric nosological images

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- Number: US 2017/0287133 A1
- Document ID: US20170287133A1
- Status: Published application
- Kind: A1
- Application number: 15/508,079
- Filed: 2015-07-28
- Published: 2017-10-05
- Earliest claimed: 2014-09-05
- Assignees: Universidad Politecnica de Valencia
- Applicants: Universidad PolitéCnica De Valencia
- Inventors: Miguel ESPARZA MANZANO; Elies FUSTER GARCIA; Juan Miguel GARCIA GOMEZ; Javier JUAN ALBARRACIN; Jose Vicente MANJON HERRERA; Monserrat ROBLES VIEJO; Carlos SAEZ SILVESTRE
- Web page: https://patentgrep.com/patent/US20170287133A1

## CPC

- G06V 10/255: {Detecting or recognising potential candidate objects based on visual cues, e.g. shapes}
- G06F 18/2413: based on distances to training or reference patterns
- G06T 5/70: Denoising; Smoothing
- G06T 7/0012: {Biomedical image inspection}
- G06T 11/00: Two-dimensional [2D] image generation
- G06T 19/00: Manipulating three-dimensional [3D] models or images for computer graphics
- G06V 10/764: using classification, e.g. of video objects
- G06T 2207/10088: Magnetic resonance imaging [MRI]
- G06T 2207/30096: Tumor; Lesion
- G06V 2201/031: of internal organs
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

The method of generating multi-parametric nosological images including the steps of obtaining a stack of images; improving such images; reducing the images to a new space of equivalent representation; classifying in an unsupervised manner the units of the images obtained according to a pre-established number of classes without biological interpretation; automatically assigning multi-parametric profiles to the obtained classes; and generating a nosological image through the obtained classes. The system includes an imaging unit; an image processing unit for processing the obtained images and generating therefrom a multi-parametric nosological image; a connection for transmitting the images obtained by the imaging unit to the image processing unit; and a display for displaying the generated multi-parametric nosological image.

## Claims

### Claim 1

A method of generating multi-parametric nosological images, comprising the steps of:
a) obtaining a stack of medical images;
b) processing the stack of medical images, through the steps of:
b1) subjecting each image to a noise filtering;
b2) registering each image to a reference space;
b3) eliminating from at least some of the images areas corresponding to peripheral tissues of a region of interest;
b4) correcting inhomogeneities in at least some of the images;
c) reducing the stack of images to a new representation space of smaller dimension, which maintains or increases discriminative properties of original data in the stack of images;
d) classifying in an unsupervised manner units of the stack of images obtained in step c) according to a pre-established number of classes and without biological interpretation of the units of the stack of images obtained in step c);
e) automatically assigning multi-parametric profiles to the classes obtained in step d), through the steps of:
e1) registering in a non-linear manner a reference template of a region of interest to the stack of images;
e2) correcting probability maps of healthy tissues of the reference template in pathological areas;
e3) identifying which classes obtained in step d) represent non-pathological tissues by using the probability maps of healthy tissues corrected in step e2);
e4) eliminating peripheral and minimal classes from the set of classes obtained in step d) to produce a remaining set of classes; and
e5) merging in the pathological areas the remaining set of classes from step d) through agglomerative hierarchical classification using distances between probability density functions; and
f) generating a nosological image through the classes obtained in steps e3) and e5).

### Claim 2 (depends on 1)

The method according to claim 1, wherein after step b4 the method further comprises:
b5) improving a resolution in the stack of images through a super-resolution step.

### Claim 3 (depends on 2)

The method according to claim 2, wherein after step b5 the method further comprises:
b′) normalizing an intensity of the images in the stack for obtaining the same intensity ranges between different studies.

### Claim 4 (depends on 3)

The method according to claim 3, wherein after step b′) the method further comprises:
b″) extracting features from the stack of images, each feature being a new image added to the stack of images.

### Claim 5 (depends on 4)

The method according to claim 4, wherein step b″) comprises any one of quantifying image biomarkers from functional images, extracting features through operations on the functional images, extracting first-order or second-order texture features from the functional images, or any combination thereof.

### Claim 6 (depends on 1)

The method according to claim 1, wherein the stack of images are selected from the group constituted by 2D and 3D images, so that 2D and 3D multi-parametric nosological images are obtained as result, respectively.

### Claim 7 (depends on 1)

The method according to claim 1, wherein a structured classification is performed in step d).

### Claim 8 (depends on 1)

A multi-parametric nosological imaging system, comprising:
an image device for obtaining a stack of images of a patient;
an image processor for processing the stack of images obtained by the image device and generating therefrom a multi-parametric nosological image, by performing the steps of the method according to claim 1;
a connection between the image device and the image processor, which permits transmitting the images obtained by the image device to the image processor; and
a display for displaying to the user the multi-parametric nosological image generated by the image processor.

### Claim 9 (depends on 2)

The method according to claim 2, wherein the images are selected from the group constituted by 2D and 3D images, so that 2D and 3D multi-parametric nosological images are obtained as result, respectively.

### Claim 10 (depends on 3)

The method according to claim 3, wherein the images are selected from the group constituted by 2D and 3D images, so that 2D and 3D multi-parametric nosological images are obtained as result, respectively.

### Claim 11 (depends on 4)

The method according to claim 4, wherein the images are selected from the group constituted by 2D and 3D images, so that 2D and 3D multi-parametric nosological images are obtained as result, respectively.

### Claim 12 (depends on 5)

The method according to claim 5, wherein the images are selected from the group constituted by 2D and 3D images, so that 2D and 3D multi-parametric nosological images are obtained as result, respectively.

### Claim 13 (depends on 2)

The method according to claim 2, wherein a structured classification is performed in step d).

### Claim 14 (depends on 3)

The method according to claim 3, wherein a structured classification is performed in step d).

### Claim 15 (depends on 4)

The method according to claim 4, wherein a structured classification is performed in step d).

### Claim 16 (depends on 5)

The method according to claim 5, wherein a structured classification is performed in step d).

### Claim 17 (depends on 6)

The method according to claim 6, wherein a structured classification is performed in step d).

### Claim 18 (depends on 9)

The method according to claim 9, wherein a structured classification is performed in step d).

### Claim 19 (depends on 10)

The method according to claim 10, wherein a structured classification is performed in step d).

### Claim 20 (depends on 11)

The method according to claim 11, wherein a structured classification is performed in step d).