Classifications

• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/48—Other medical applications
  • A61B5/4848—Monitoring or testing the effects of treatment, e.g. of medication
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T7/00—Image analysis
  • G06T7/0002—Inspection of images, e.g. flaw detection
  • G06T7/0012—Biomedical image inspection
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0075—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence by spectroscopy, i.e. measuring spectra, e.g. Raman spectroscopy, infrared absorption spectroscopy
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B5/00—Measuring for diagnostic purposes; Identification of persons
  • A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
  • A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
  • A61B2576/00—Medical imaging apparatus involving image processing or analysis
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/20—Special algorithmic details
  • G06T2207/20212—Image combination
  • G06T2207/20216—Image averaging
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
  • G06T2207/00—Indexing scheme for image analysis or image enhancement
  • G06T2207/30—Subject of image; Context of image processing
  • G06T2207/30004—Biomedical image processing
  • G06T2207/30088—Skin; Dermal

Definitions

• the technical field of the invention is the statistical classification systems, and more particularly the systems for the statistical classification of hyper-spectral images.
• the evolution of skin diseases is quantified by dermatologists over an entire treatment period.
• the degree of impairment is measured on each patient in a group. The measurement is performed clinically by a dermatologist.
• a statistical treatment of the measurements makes it possible to quantify the effectiveness of the treatment.
• an operating protocol based on the study over time of the symptoms related to a cutaneous disease expressed in a group of Ne patients is used.
• Each patient receives a treatment on a first affected skin area and a vehicle on a second skin area affected.
• the first skin area and the second skin area are selected to have similar area and disease involvement.
• one cheek receives treatment while the other cheek receives the vehicle, provided that both cheeks have the same attack by a skin disease.
• a dermatologist estimates the degree to which a patient is affected by the disease, zone by zone, patient by patient. As a result, the quantification of treatment efficacy can be empirical and subject to a certain amount of subjectivity.
• hyper-spectral imaging In order to improve the observation and quantification of the degree of disease attack while increasing the reproducibility of these steps, hyper-spectral imaging can be used. It is recalled that hyper-spectral imaging consists of acquiring several images at different wavelengths. Indeed, the materials and chemical elements react more or less differently when exposed to radiation of a given wavelength. By scanning the range of radiation, it is possible to differentiate materials involved in the composition of an object by their difference in interaction. This principle can be generalized to a landscape, or to a part of an object.
• the set of images from the photograph of the same scene at different wavelengths is called the hyper-spectral image or hyper-spectral cube.
• a hyper-spectral image therefore consists of a set of images, each pixel of which is characteristic of the intensity of the interaction of the scene observed at a particular wavelength.
• the acquisition of hyper-spectral images can be carried out according to several methods.
• spectral scan The method of acquiring hyper-spectral images called spectral scan consists of using a CCD type sensor, to make spatial images, and to apply different filters in front of the sensor in order to select a wavelength for each image .
• Different filter technologies make it possible to meet the needs of such imagers. For example, liquid crystal filters which isolate a wavelength by electrical stimulation of the crystals, or acousto-optic filters which select a wavelength by deforming a prism by means of an electric potential difference ( piezoelectric effect). These two filters have the advantage of not having moving parts which are often a source of fragility in optics.
• the method of acquiring hyper-spectral images aims to acquire or "image" simultaneously all wavelengths of the spectrum on a CCD type sensor.
• a prism is placed in front of the sensor. Then, to form the complete hyper-spectral cube, a spatial scan is performed line by line.
• the so-called time-scan hyper-spectral image acquisition method involves performing an interference measurement, and then reconstructing the spectrum by making a Fast Fourier Transform (FFT) on the interference measurement.
• FFT Fast Fourier Transform
• Interference is achieved through a Michelson-type system, which interferes with a ray with itself shifted temporally.
• the latest method of acquiring hyper-spectral images is to combine spectral and spatial scanning.
• the CCD sensor is partitioned into blocks. Each block therefore deals with the same region of space but with different wavelengths. Then, a spectral and spatial scan makes it possible to constitute a complete hyper-spectral image.
• hyper-spectral imaging allows the acquisition of images including information related to the wavelength. The intensity of each pixel as a function of the wavelength is recorded. The application of classification methods to these images distinguishes healthy areas from affected areas.
• the classification of hyper-spectral images is a particularly active area.
• Pigment cell res, vol. 17, pp. 618-626, 2004 teach that the L * component or the ITA index calculated with the L * and b * components is used to describe pigmentation.
• An object of the invention is to generate images having a maximum contrast between images of an area affected by a disease and images of an area spared by the disease.
• Another object of the invention is to determine a numerical index reflecting the effectiveness of the treatment of a disease.
• Another object of the invention is an image processing system capable of determining a numerical index reflecting the efficiency of the treatment of a disease.
• An object of the invention is a method of determining a quantization value of the deviation of the mean value of a di flibution of the severity difference between an initial time and a time after the initial time, the contrast-dependent severity between areas receiving treatment and areas receiving a vehicle.
• the method includes steps in which:
• At least one patient acquires, for at least one patient, at an initial time and at least one time after the initial time, at least one hyper-spectral image comprising at least one selected area among a sick area receiving the treatment, an area receiving the treatment, a sick area receiving the vehicle and a healthy area receiving the vehicle,
• a representative component is determined that maximizes the gap between the healthy zone and the pathological zone
• the averages are determined on all the images, absolute values of the linear combinations of the spectral bands each corresponding to the representative component of an image, a representative component corrected according to the average of the components is determined for each image.
• a value of the severity difference is determined as a function of the corrected representative components of the acquired hyper-spectral images, for each patient and for each measurement instant, and
• the invention has the advantage of providing a unique numerical index to characterize the efficiency of the treatment between two measurement instants automatically and from the only hyper-spectral images of a set of patients, the hyper-spectral images being classified between images of a healthy zone and images of a pathological zone.
• the value of the severity of the disease can be determined for a patient at a time of measurement, calculated for the images relating to the areas having received the treatment, concerning the patient considered and the moment of measurement considered, realizing the simple difference between the mean value of intensity for the image of the healthy zone having received the treatment and the average intensity for the image of the sick area having received the treatment , and
• a value of the difference in severity of the disease can be determined between the areas having received the treatment and the zones having received the vehicle for the patient considered at the time of measurement by making the simple difference in the severity of the disease. for the patient considered at the time of measurement considered, calculated for the images relating to the zones having received the treatment and the value of the severity of the disease for the patient considered at the time of measurement considered, calculated for the images relating to areas that received the vehicle.
• a value of the difference in severity of the disease can be determined between the areas having received the treatment and the areas having received the vehicle for the patient considered at the time of measurement by realizing the relative difference in the severity of the disease. for the patient considered at the time of measurement considered, calculated for the images relating to the zones having received the treatment and the value of the severity of the disease for the patient considered at the time of measurement considered, calculated for the images relating to areas that received the vehicle.
• the vehicle can be a placebo.
• the vehicle may be another treatment.
• Another object of the invention is an image processing system for determining the severity of a disease comprising a hyper-spectral image acquisition device connected to a processing means, the processing means being connected a data storage means and a human machine interaction device, the processing means being adapted to apply the method defined above.
• Another object of the invention is the application of the method described above to the determination of the effectiveness of a dermatological treatment.
• FIG. 1 illustrates the determination method according to the invention
• the determination method starts with the acquisition 1 of at least one hyper-spectral image comprising a zone among a sick area receiving the treatment, a healthy zone receiving the treatment, a sick area receiving the vehicle and a healthy area receiving the vehicle .
• These hyper-spectral images are acquired at each moment of measurement and for each patient. In the remainder of the description, it will be considered that four images each comprising an area of interest have been acquired.
• vehicle By vehicle is meant a composition comprising the same excipients as those corresponding to the treatment but not including the active ingredients acting on the causes of the disease.
• the vehicle may also be a placebo or a control solution.
• the vehicle may be another treatment, that is to say a composition comprising active ingredients and excipients different from the composition of the first treatment.
• the described method is based on the application of a treatment and a vehicle, it is possible to apply the method to compare a zone receiving a first treatment with a zone receiving a second treatment.
• the four hyper-spectral images are processed by an independent component analysis method ("Independent Component Analysis” in English, identified by the acronym "ICA").
• ICA Independent Component Analysis
• Each image is transformed by the ICA process into an image of the same size and comprising as many independent components that the original image included wavelengths.
• Each of the independent components is a linear combination of the wavelengths of the original image.
• a representative component is determined that maximizes the difference between healthy and pathological zones.
• linear combination For each image, the linear combination of the spectral bands corresponding to the representative component is stored.
• linear combination is meant the weighting coefficients of each spectral band. It may require variations in coefficients from one patient to another.
• the average of the solute values of the weighting coefficients involved in the linear combination of the representative component of each patient is averaged. The average of the ab solute values could also be achieved by precarrying outlier or extreme values. Average coefficients are thus obtained.
• a representative component corrected as a function of the average of the representative components is determined, that is to say as a function of the average coefficients.
• the determination method comprises a step 2 of determining an average pixel value of the corrected representative component M, for each image.
• the average intensity on a corrected representative component M of a sick area receiving treatment is noted ⁇ ⁇ ⁇ ⁇
• the mean intensity on a corrected representative component M of a healthy area receiving the treatment is noted ⁇ ⁇
• the average intensity on a corrected representative component M of a sick area receiving the vehicle is noted ⁇ ⁇ ⁇ ⁇
• the average intensity on a corrected representative component M of a healthy zone receiving the vehicle is noted ⁇ ⁇ ⁇
• Np the total number of pixels per band of the image
• I (m, M) the intensity of the pixel m of the corrected representative component M.
• the determination method determines a single value for quantifying a patient's disease from the four intensity averages determined in the previous step.
• the unique value obtained is the severity O e t .
• a first normalization is applied between the sick zone and the healthy zone, during a step 3 of the method followed by a second normalization in another step 4 of the method.
• the first standardization can be done by a simple difference.
• the first normalization can be achieved by a relative difference.
• ⁇ ⁇ 11 the average intensity for a healthy zone
• ⁇ ⁇ the average intensity for a sick area.
• the first normalization may correspond to the average measurement for the sick area
• the normalization equation 2 is preferred. These values are determined at each moment of measurement and for each patient.
• the determination method applies equation 2 by replacing the values ⁇ and ⁇ by the values ⁇ ⁇ and ⁇ ⁇ ⁇ in order to obtain the severity d t eA .
• the determination method applies equation 3 by replacing the values ⁇ and ⁇ by the values ⁇ ⁇ and ⁇ ⁇ in order to obtain the severity d t eA .
• the severity d ⁇ by replacing the values ⁇ ⁇ and ⁇ ⁇ ⁇ of equation 6 by the values ⁇ ⁇ and ⁇ ⁇ ⁇ ⁇
• the determination method applies equation 4 by replacing the values ⁇ and ⁇ with the values ⁇ ⁇ and ⁇ ⁇ in order to obtain the severity d t eA .
• ⁇ ⁇ ⁇ the mean intensity for a sick area that received the vehicle
• ⁇ ⁇ the average intensity for a healthy zone that has received the treatment
• ⁇ ⁇ the mean intensity for a diseased area that received the treatment.
• a second normalization is applied between the area receiving the treatment and the area receiving the vehicle.
• the second normalization makes it possible to determine the severity O e t of the patient e at the time of measurement t resulting from the comparison of the treatment and the vehicle. From the severities t eA areas having received treatment and severities t eV areas having received the vehicle, one can determine a severity O e t for e patient to the measurement time t.
• d t eA the severity of the disease for the patient e at the time t of measurement, calculated for the images relating to the zones having received the treatment
• d t eV the severity of the disease to the patient e at time t measure, calculated for images relating to areas having received the vehicle.
• the determination method applies equation 8 to determine the severity O e t for the patient e at the time of measurement t.
• equation 9 is applied to determine the severity D j for the patient e at the time of measurement t.
• the distribution of severities e O t between patients is not yet considered.
• a statistical analysis is necessary.
• the inventors have ingeniously applied a t-test method to the data characterizing the difference between the treated patient zone and the sick area receiving the vehicle.
• the t-test is applied to the severity index O e t .
• Z Wo the quantification of the deviation of the mean value between two distributions
• X (t) the mean value of X
• ⁇ ( ⁇ ) the standard deviation of X
• N e the number of patients in the group
• t the time of measurement and the time of the reference measurement.
• the null hypothesis is that the average value of the distribution does not evolve between the time to and the time t.
• This test is applied to severities e O t obtained after optimizing the gap between healthy and diseased area area, X (t) then being the average value of all patients treated O e severities t at time t, and ⁇ ( ⁇ ) then being the standard deviation of the distribution of severities O e t at time t.
• the average value of severities O e t can be calculated in several ways known to the skilled person, such as a simple average, on average, a statistical average. In addition, it is possible to remove extreme values, or outliers. In the latter two cases, the standard deviation of the distribution of severities O e t is calculated by separating the same values removed from all the values considered to perform the average.
• the distribution of severities e O t between patients is not yet considered.
• a statistical analysis is necessary.
• the inventors have ingeniously applied a t-test method matched to the data characterizing the difference between the treated patient zone and the sick area receiving the vehicle.
• the paired t-test is applied to the severity index W e t.
• the method is called Paired Student Test, or paired t-test.
• the null hypothesis is that the average value of the distribution does not evolve between time t 0 and time t.
• This test is applied to severities e O t obtained after optimizing the gap between healthy and diseased area area, X (t) then being the average value of all patients considered the difference D , -D, between time t and time t 0 , and ⁇ ( ⁇ ) then being the standard deviation of the distribution of the difference D, -D, between time t and time t 0 .
• the value of the difference -Df between the instant t and the instant t 0 can be calculated in several ways known to those skilled in the art, such as for example a simple average, a relative average, a statistical average. In addition, it is possible to remove extreme values, or outliers. In the latter two cases, the standard deviation of the distribution of severities -D, is calculated by discarding the same values removed from the set of values considered to achieve the mean.
• the determination method thus comprises a step 5 of determining a value Z t t0 (t) of the quantization of the deviation of the mean value of the severity distribution O e t between time to and time t.
• This value quantification is firstly compared to the null hypothesis for the presence of an effect, and compared to values Z t t0 (t) other treatments to compare the effects, or compared to Z t t0 (t) at other times to determine the evolution over time.
• the difference between a value of the Student's statistic associated with Z t t0 (t) and the value 0.05 of the null hypothesis is significant, it means that the treated area is evolving more and more distinctly from the nonzero zone. treated. The treatment is then considered effective.
• the Student statistic is obtained by reading the Student's Law table. For a value of Z on the abscissa, there is a probability on the y-axis.
• the value Z makes it possible to characterize the effectiveness of the treatment over the total duration of treatment.
• the value Z also makes it possible to compare the effectiveness of a treatment with the effectiveness of another treatment of the same duration.
• the hyper-spectral image processing system 10 comprises a hyper-spectral image acquisition device 11 connected to a processing means 12, itself connected to a data storage means 13 and to a data storage device. human machine interaction 14.
• the acquisition device is capable of producing hyper-spectral images of zones (15, 16) of a patient 17.
• the zones whose image is acquired are a healthy zone 15 and a sick zone 16.
• the images are also taken from fractions of these areas that have received treatment or a vehicle.
• the acquisition is repeated for several subjects and at different measurement times.
• the data obtained is transmitted to the processing means 12 which processes them in real time or redirects them to the data storage means 13 for delayed processing.
• the processing means 12 applies the steps of the method for determining a quantization value of the deviation of the mean value of a distribution of the severity difference between an initial instant and a moment after the initial moment, the severity depending on the contrast between areas receiving treatment and areas receiving a vehicle.
• the results of the processing are displayed via the human-machine interaction device 14.
• the result can be displayed on one screen, transmitted to another system for further processing, or transmitted by a means of communication. remote electronic communication to or from multiple users.

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