# US 2022/0044409 A1 · Imaging analyzer

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- Number: US 2022/0044409 A1
- Document ID: US20220044409A1
- Status: Published application
- Kind: A1
- Application number: 17/415,393
- Filed: 2019-02-14
- Published: 2022-02-10
- Earliest claimed: 2019-02-14
- Assignees: Shimadzu Corporation
- Applicants: Shimadzu Corporation
- Inventors: Shinichi YAMAGUCHI
- Web page: https://patentgrep.com/patent/US20220044409A1

## CPC

- G06T 7/11: Region-based segmentation
- G06V 10/762: using clustering, e.g. of similar faces in social networks
- G06F 18/23: Clustering techniques
- G06F 18/24: Classification techniques
- G06V 10/14: Optical characteristics of the device performing the acquisition or on the illumination arrangements
- G06V 10/25: Determination of region of interest [ROI] or a volume of interest [VOI]
- G06V 10/26: Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V 10/764: using classification, e.g. of video objects
- H01J 49/0004: {Imaging particle spectrometry}
- G06T 2207/10028: Range image; Depth image; 3D point clouds
- G06T 2207/20104: Interactive definition of region of interest [ROI]
- G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- H01J: ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS (spark-gaps H01T; electric arc lamps H05B31/00; particle accelerators H05H)

## Abstract

An imaging mass spectrometry unit (1) executes predetermined analysis on each of a plurality of micro areas set in a two-dimensional measurement region on a sample or a three-dimensional measurement region in a sample to acquire spectrum data. A clustering execution section (21) classifies spectrum data for a plurality of measurement points obtained for a reference sample into any of a plurality of clusters. A clustering model information storage section (22) stores a clustering model obtained by clustering processing. A segmentation execution section (23) classifies respective spectral data for a plurality of measurement points obtained for a sample other than a reference sample using a clustering model, and a spatial distribution image creation section (24) creates a segmentation image obtained by partitioning a two-dimensional or three-dimensional image into a plurality of small regions on the basis of a result of the classification.

## Claims

### Claim 1

An imaging analyzer capable of acquiring spectral data by executing predetermined analysis on each of a plurality of micro areas set in a two-dimensional measurement region on a sample or a three-dimensional measurement region in a sample, the imaging analyzer comprising:
a clustering execution section configured to execute clustering processing of classifying spectral data for a plurality of measurement points obtained for one sample into any of a plurality of clusters;
a model storage section configured to store a model of clustering obtained by clustering processing by the clustering execution section; and
a segmentation execution section configured to classify spectral data for a plurality of measurement points obtained for an optional sample using a clustering model stored in the model storage section, and to create a segmentation image obtained by partitioning a two-dimensional or three-dimensional image into a plurality of small regions on the basis of a result of the classification.

### Claim 2

An imaging analyzer capable of acquiring spectral data by executing predetermined analysis on each of a plurality of micro areas set in a two-dimensional measurement region on a sample or a three-dimensional measurement region in a sample, the imaging analyzer comprising:
a clustering execution section configured to execute clustering processing of classifying spectral data for a plurality of measurement points obtained for one sample into any of a plurality of clusters;
a segmentation image creation section configured to create a segmentation image obtained by partitioning the entire two-dimensional or three-dimensional measurement region or a part of the entire two-dimensional or three-dimensional measurement region into a plurality of small regions on the basis of a result of the clustering processing; and
a region-of-interest designation accepting section that displays the segmentation image on a display unit and recognizes one or a plurality of partitioned small regions in the segmentation image as a region of interest according to an operation of an input unit by a user.

### Claim 3 (depends on 1)

The imaging analyzer according to claim 1, wherein the measurement region is a two-dimensional measurement region on a sample.

### Claim 4 (depends on 1)

The imaging analyzer according to claim 1, wherein the measurement region is a three-dimensional measurement region in a sample.

### Claim 5 (depends on 1)

The imaging analyzer according to claim 1, wherein the predetermined analysis is mass spectrometry, and the spectrum data is mass spectrum data.

### Claim 6 (depends on 2)

The imaging analyzer according to claim 2, wherein the measurement region is a two-dimensional measurement region on a sample.

### Claim 7 (depends on 2)

The imaging analyzer according to claim 2, wherein the measurement region is a three-dimensional measurement region in a sample.

### Claim 8 (depends on 2)

The imaging analyzer according to claim 2, wherein the predetermined analysis is mass spectrometry, and the spectrum data is mass spectrum data.