# US 2018/0045654 A1 · Model-based methods and apparatus for classifying an interferent in specimens > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0045654 A1 - Document ID: US20180045654A1 - Status: Published application - Kind: A1 - Application number: 15/551,562 - Filed: 2016-02-16 - Published: 2018-02-15 - Earliest claimed: 2015-02-17 - Assignees: Siemens Healthcare Diagnostics Inc. - Applicants: Siemens Healthcare Diagnostics Inc. - Inventors: JinHyeong Park; Yao-Jen Chang; Wen Wu; Terrence Chen; Benjamin Pollack - Web page: https://patentgrep.com/patent/US20180045654A1 ## CPC - G01N 21/94: Investigating contamination, e.g. dust (G01N21/85 takes precedence) - G01J 3/462: {Computing operations in or between colour spaces; Colour management systems} - G01J 3/50: using electric radiation detectors - G01N 21/03: Cuvette constructions - G01N 21/25: Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands - G01N 21/27: using photo-electric detection (G01N21/31 takes precedence){; circuits for computing concentration (logarithmic circuits G06G7/24; photometric circuits in general G01J)} - G01N 35/00613: {Quality control} - G06F 18/2411: based on the proximity to a decision surface, e.g. support vector machines - G06T 7/0012: {Biomedical image inspection} - G06V 20/695: {Preprocessing, e.g. image segmentation} - G06V 20/698: {Matching; Classification} - G01N 2021/5923: {Determining zones of density; quantitating spots} - G01N 2021/5969: {Scanning of a tube, a cuvette, a volume of sample} - G01N 2201/12: Circuits of general importance; Signal processing - G06T 2207/10024: Color image - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - 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) - G01J: MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY (light sources F21, H01J, H01K, H05B; investigating properties of materials by optical means G01N) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract A model-based method of inspecting a specimen for presence of one or more interferent, such as Hemolysis, Icterus, and/or Lipemia (HI L) is provided. The method includes generating a pixelated image of the specimen in a first color space, determining color components (e.g., an a-value and a b-value) for pixels in the pixelated image, classifying of the pixels as being either liquid or non-liquid, defining one or more liquid regions based upon the pixels classified as liquid, and determining a presence of one or more interferent within the one or more liquid regions. The liquid classification is based upon a liquid classification model. Pixel classification may be based on a trained multiclass classifier. Interference level for the one or more interferent may be provided. Testing apparatus adapted to carry out the method are described, as are other aspects. ## Claims ### Claim 1 A method of determining a characteristic of a specimen contained within a sample container, comprising: generating a pixelated image of the specimen; determining color components for pixels in the pixelated image; classifying of pixels in the pixelated image as being liquid or non-liquid; defining one or more liquid regions based upon the classifying of the pixels; and determining a presence of one or more interferent within the one or more liquid regions. ### Claim 2 (depends on 1) The method of claim 1, comprising: determining an a-value and a b-value for the pixels in the pixelated image in L*a*b color space. ### Claim 3 (depends on 1) The method of claim 1, wherein the pixelated image is subjected to gamma correction prior to the classifying. ### Claim 4 (depends on 1) The method of claim 1, wherein the pixelated image is compiled from multiple images. ### Claim 5 (depends on 1) The method of claim 1, wherein the classifying of the pixels in the pixelated image as being liquid or non-liquid is based upon a liquid/non-liquid detector. ### Claim 6 (depends on 1) The method of claim 1, wherein the classifying of the pixels in the pixelated image as being liquid or non-liquid is based upon a liquid classification model generated from multiple training sets. ### Claim 7 (depends on 6) The method of claim 6, wherein the liquid classification model further comprises a support vector machine. ### Claim 8 (depends on 1) The method of claim 1, wherein the determining a presence of an interferent within the one or more liquid regions is based upon a multi-class classification model generated from multiple interferent training sets. ### Claim 9 (depends on 1) The method of claim 1, wherein the pixels in the pixelated image that have been classified as liquid is further classified by a pixel classifier as being either: normal, or by interferent type as being one of lipemia, hemolysis, or icterus. ### Claim 10 (depends on 9) The method of claim 9, wherein a classification as normal or by the interferent type is based upon a summation or weighting for each of the pixels classified in each class of normal, lipemia, hemolysis, or icterus. ### Claim 11 (depends on 1) The method of claim 1, wherein the determining a presence of an interferent within the liquid region comprises: determining an interferent type within the one or more liquid regions based upon a multi-class classification model; and determining an interferent level of the interferent type based upon a regression model. ### Claim 12 (depends on 11) The method of claim 11, wherein the regression model is trained for each interferent type based upon multiple training specimens exhibiting diverse interference levels. ### Claim 13 (depends on 1) The method of claim 1, comprising analyzing the pixelated image to determine a physical dimensional characteristic of the sample container. ### Claim 14 (depends on 1) The method of claim 1, comprising analyzing the pixelated image to determine a color of a cap on the sample container. ### Claim 15 A testing apparatus adapted to determine a characteristic of a specimen in a sample container, comprising: one or more image capture devices configured to capture one or more images of the specimen and at least a portion of the sample container and at least a portion of a label affixed to the sample container, if present, from which a pixelated image is generated; a liquid/non-liquid detector, based upon a liquid classification model, operable to classify the pixels in the pixelated image as being either liquid or non-liquid and define one or more liquid regions; a pixel classifier, based on a multi-class classification model, operable to determine for the pixels in the liquid region a classification as normal, hemolysis, icterus, or lipemia; and an interferent type detector operable to detect, based on a result of the pixel classifier, one or more interferent type within the one or more liquid regions. ### Claim 16 (depends on 15) The apparatus of claim 15, wherein the liquid classification model further comprises a support vector machine. ### Claim 17 (depends on 15) The apparatus of claim 15, wherein the liquid classification model is generated from multiple training sets. ### Claim 18 (depends on 15) The apparatus of claim 15, comprising an interferent level detector operable to determine an interferent level for the one or more interferent type detected. ### Claim 19 (depends on 18) The apparatus of claim 18, wherein the one or more interferent type is at least one selected from a group consisting of: lipemia; hemolysis; and icterus. ### Claim 20 (depends on 18) The apparatus of claim 18, wherein the interferent level detector comprises a regression model that is trained for each interferent type based upon multiple training specimens exhibiting diverse interference levels. ### Claim 21 A method of determining a characteristic of a specimen contained within a sample container, comprising: generating a pixelated image of the specimen in the sample container at a quality control station in a first color space; converting the pixelated image from the first color space to a second color space; determining color components for pixels in the pixelated image in the second color space; classifying of the pixels in the pixelated image as being liquid or non-liquid based upon a liquid classification model; defining one or more liquid regions based upon pixels that are classified as liquid pixels; classifying the liquid pixels as being one of normal, hemolysis, icterus or lipemia based upon a multi-class classification model; determining that the one or more liquid regions is either normal or includes one or more types of interferent based upon a number of the liquid pixels in each of normal, hemolysis, icterus or lipemia; and determining an interferent level for at least some of the one or more types of interferent based upon a regression model. ### Claim 22 A testing apparatus adapted to determine a characteristic of a specimen in a sample container, comprising: a plurality of image capture devices arranged around the specimen and configured to capture multiple images of the specimen and at least a portion of the sample container and at least a portion of a label affixed to the sample container, if present, from multiple viewpoints, from which a pixelated image is generated; a liquid/non-liquid detector, based upon a liquid classification model, operable to classify pixels in the pixelated image as being either liquid or non-liquid and define one or more liquid regions; a pixel classifier, based on a multi-class classification model, operable to determine for the pixels in the liquid region a classification as normal, hemolysis, icterus, or lipemia; and an interferent type detector operable to detect, based on a result of the pixel classifier, one or more interferent type within the one or more liquid regions.