Hyperspectral image construction of biological tissue for blood hemoglobin analysis using a smartphone

Abstract

A bloodless system for numerically generated hyperspectral imaging data for measuring biochemical compositions is disclosed which includes an optical imaging device adapted to acquire an RGB image from an area of interest, a processor adapted to receive a hyperspectral dataset representing an a priori hyperspectral data of the area of interest of a population to which the subject belongs, receive RGB response for each one of RGB channels of the optical imaging device, pair the corresponding RGB data with the hyperspectral data, obtain a transformation matrix adapted to convert a subject-specific RGB image dataset into a subject-specific hyperspectral dataset for the optical imaging device, receive a subject- specific RGB dataset, generate a subject- specific hyperspectral dataset using the transformation matrix, and compute a blood hemoglobin level of the subject from the generated subject-specific hyperspectral dataset.

1 . A bloodless system for numerically generated hyperspectral imaging data for measuring biochemical compositions, comprising:
 an optical imaging device adapted to acquire an RGB image from an area of interest, thereby generating a subject-specific RGB dataset representing the area; and   a processor adapted to: 
 receive a hyperspectral dataset representing an a priori hyperspectral data of the area of interest of a population to which the subject belongs, 
 receive RGB response for each one of RGB channels of the optical imaging device, 
 pair the corresponding RGB data with the hyperspectral data, 
 obtain a transformation matrix adapted to convert the subject-specific RGB image dataset into a subject-specific hyperspectral dataset for the optical imaging device, 
 receive a subject-specific RGB dataset, 
 generate a subject-specific hyperspectral dataset using the transformation matrix, and 
 compute a blood hemoglobin level of the subject from the generated subject-specific hyperspectral dataset.     
     
     
         2 . The system of  claim 1 , wherein the paired pixels from the RGB image is associated with a 3×1 RGB value matrix. 
     
     
         3 . The system of  claim 2 , wherein the paired pixels from the hyperspectral dataset is associated with an N×1 spectrum, where N represents discretized spectra between a lower bound and an upper bound. 
     
     
         4 . The system of  claim 3 , wherein the lower and upper bounds are 400 nm and 800 nm, respectively. 
     
     
         5 . The system of  claim 1 , wherein the transformation matrix is a form of inverse of the RGB response matrix of the RGB sensor that converts an RGB to a spectrum. 
     
     
         6 . The system of  claim 5 , wherein the inverse of the transformation matrix is determined numerically by using the paired RGB and hyperspectral data of the population. 
     
     
         7 . The system of  claim 1 , wherein the biochemical compositions include blood hemoglobin. 
     
     
         8 . The system of  claim 1 , wherein the area of interest includes the inner surface of a subject's inner eyelid. 
     
     
         9 . The system of  claim 1 , wherein the biochemical compositions are determined using spectral analysis. 
     
     
         10 . The system of  claim 9 , wherein the spectral analysis includes a partial least square regression statistical modeling technique to first build a model from a training set of a first hyperspectral dataset vs. the biochemical composition and then apply the model to a second dataset from the generated hyperspectral image dataset. 
     
     
         11 . A method for a bloodless numerically generated hyperspectral imaging data for measuring biochemical compositions, comprising:
 obtaining an RGB image from an area of interest, thereby generating a subject-specific RGB dataset representing the area of interest;   receiving a hyperspectral dataset representing an a priori hyperspectral data of the area of interest for a population to which the subject belongs;   receiving an RGB response for each one of RGB channels of the optical imaging device, pairing the corresponding RGB data with the hyperspectral data;   obtaining a transformation matrix adapted to convert the subject-specific RGB image dataset into a subject-specific hyperspectral dataset for the optical imaging device;   generating a subject-specific hyperspectral dataset using the transformation matrix; and   computing a blood hemoglobin level of the subject from the generated subject-specific hyperspectral dataset.   
     
     
         12 . The method of  claim 1 , wherein the paired pixels from the RGB image is associated with a 3×1 RGB value matrix. 
     
     
         13 . The method of  claim 12 , wherein the paired pixels from the hyperspectral dataset is associated with an N×1 spectrum, where N represents discretized spectra between a lower bound and an upper bound. 
     
     
         14 . The method of  claim 13 , wherein the lower and upper bounds are 400 nm and 800 nm, respectively. 
     
     
         15 . The method of  claim 11 , wherein the transformation matrix is a form of inverse of the RGB response matrix of the RGB sensor that converts an RGB to a spectrum. 
     
     
         16 . The method of  claim 15 , wherein the inverse of the transformation matrix is determined numerically by using the paired RGB and hyperspectral data of the population. 
     
     
         17 . The method of  claim 11 , wherein the biochemical compositions include blood hemoglobin. 
     
     
         18 . The method of  claim 11 , wherein the area of interest includes the inner surface of a subject's inner eyelid. 
     
     
         19 . The method of  claim 11 , wherein the biochemical compositions are determined using spectral analysis. 
     
     
         20 . The method of  claim 19 , wherein the spectral analysis includes a partial least square regression statistical modeling technique to first build a model from a training set of a first hyperspectral dataset vs. the biochemical composition and then apply the model to a second dataset from the generated hyperspectral image dataset. 
     
     
         21 . A method for a bloodless numerically generated hyperspectral imaging data for measuring biochemical compositions, comprising:
 obtaining an RGB image from an area of interest, thereby generating a subject-specific RGB dataset representing the area of interest;   receiving a hyperspectral dataset representing an a priori hyperspectral data of the area of interest for a population to which the subject belongs;   receiving an RGB response for each one of RGB channels of the optical imaging device, pairing the corresponding RGB data with the hyperspectral data;   converting the subject-specific RGB image dataset into a subject-specific hyperspectral dataset for the optical imaging device; and   computing a blood hemoglobin level of the subject from the generated subject-specific hyperspectral dataset.