EP2666147A1 - Détermination de l'efficacité d'un traitement - Google Patents
Détermination de l'efficacité d'un traitementInfo
- Publication number
- EP2666147A1 EP2666147A1 EP12700700.3A EP12700700A EP2666147A1 EP 2666147 A1 EP2666147 A1 EP 2666147A1 EP 12700700 A EP12700700 A EP 12700700A EP 2666147 A1 EP2666147 A1 EP 2666147A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- severity
- considered
- value
- patient
- received
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Classifications
-
- 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
- G06T7/0014—Biomedical image inspection using an image reference approach
- G06T7/0016—Biomedical image inspection using an image reference approach involving temporal comparison
-
- 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
-
- 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. Performed by visual evaluation, the quantification of treatment efficacy can be empirical and subject to a certain amount of subjectivity.
- hyper-spectral imaging In order to assist in the observation and quantification of the degree of skin or dermis involvement of a patient with a disease 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 able to determine a numerical index reflecting the effectiveness of the treatment of a disease.
- An object of the invention is a method for determining a quantization value of the deviation of the mean value of a distribution of the severity difference between an initial moment and a moment after the initial moment, the severity depending on the contrast 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 subsequent to the initial instant, at least one hyper-spectral image comprising at least one of 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, a weighting coefficient of each wavelength is determined,
- an optimized value of the value of the severity difference is determined according to the acquired hyper-spectral images comprising weighted wavelengths, 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 difference in severity being a function of the weighted wavelengths
- 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, depending on the wavelengths weighted by realizing the simple difference of the intensity average for the image of the healthy zone having received the treatment and the intensity average for the image of the sick area 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 the zones having received the treatment, concerning the patient considered and the moment of measurement considered, depending on the lengths weighted waves as being equal to the intensity average for the image of the diseased area having received the treatment, and the value of the severity of the disease can be determined for the patient considered at the moment of measurement considered, calculated for the images relating to the zones having received the vehicle, concerning the patient considered and the moment of measurement considered, according to the lengths of weighted waves as being equal to the intensity average for the image of the diseased area having 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 making the simple difference in the severity of the disease.
- disease for the patient considered at the time of measurement considered calculated for the images relating to the areas 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 the areas which have 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 considered by making the relative difference in the severity of the disease.
- 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 patients. images relating to the 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 hyperspectral image acquiring device connected to a processing means, the means for The process is connected to a data storage means and to 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 therapeutic 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 means a composition comprising the same excipients as those corresponding to the treatment but not including not 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 determination method comprises a step 2 of determining an average value of the pixels.
- the average value of the pixels, for a hyper-spectral image is a vector comprising the values for each wavelength. This vector is thus analogous to an average spectrum.
- the average intensity of the hyper-spectral image of a sick area receiving the treatment is noted ⁇ ⁇ ⁇ ⁇
- the average intensity of the hyper-spectral image of a healthy zone image receiving the treatment is noted ⁇ ⁇
- the average intensity of the hyper-spectral image of a hyper-spectral image of a sick area receiving the vehicle is noted ⁇ ⁇ ⁇ ⁇
- the average intensity of the hyper-spectral image of a picture of healthy zone receiving the vehicle is noted ⁇ ⁇ ⁇
- Np the total number of pixels per band of the image
- 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.
- a severity zones having received the vehicle t eV if the IMH values and ⁇ ⁇ are replaced by the values ⁇ ⁇ and ⁇ ⁇ ⁇ on medium intensity for areas having received the vehicle.
- 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 determination method applies equation 4 by replacing the values ⁇ and ⁇ with the values ⁇ ⁇ and ⁇ ⁇ in order to obtain the severity d t eA .
- ⁇ ⁇ the average intensity for a sound zone that has received the vehicle
- ⁇ ⁇ ⁇ 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 D j of the patient e at the measurement instant 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 e
- the determination process continues with a step 5 during which the contrast between the healthy areas and the diseased areas is maximized.
- a weighting favoring the contribution of the relevant spectral bands is carried out.
- ⁇ mean values
- the determination method seeks to optimize the severity value D j , which results in a modification of the weighting of the spectral bands of the hyper-spectral image. Optimization can be represented by the following equation:
- Nt the total number of images acquired for each patient
- ⁇ the difference between the initial time tO and the time t:
- the value of the severity O e t corresponding to the optimized value ⁇ is again determined for each measurement instant and for each patient, and used for the subsequent steps of the method.
- 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 W 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 moment of measurement and the moment 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.
- the lower the value of the quantization of the deviation the greater the difference between the average values of the two distributions between the instant t and the instant t 0 .
- This test is applied to severities e O t obtained after optimizing the gap between healthy and diseased area area, the X (t) then being the average value of all patients treated severities O e 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 the severities 1 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.
- severity 1 is calculated by excluding the same values removed from the set of values considered to achieve 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 O 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 6 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 the time t 0 and the time t.
- This quantization value will be compared firstly to the null hypothesis to determine the presence of an effect, then compared to values Z t t0 (t) of other treatments to compare them. effects, or compared with Z tt0 (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 contrast-dependent severity 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.
Abstract
Description
Claims
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR1150458A FR2970801B1 (fr) | 2011-01-20 | 2011-01-20 | Procede de determination de l'efficacite d'un traitement et systeme de traitement d'image associe |
PCT/EP2012/050862 WO2012098225A1 (fr) | 2011-01-20 | 2012-01-20 | Determination de l'efficacite d ' un traitement |
Publications (1)
Publication Number | Publication Date |
---|---|
EP2666147A1 true EP2666147A1 (fr) | 2013-11-27 |
Family
ID=44247947
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP12700700.3A Withdrawn EP2666147A1 (fr) | 2011-01-20 | 2012-01-20 | Détermination de l'efficacité d'un traitement |
Country Status (5)
Country | Link |
---|---|
US (1) | US20140016842A1 (fr) |
EP (1) | EP2666147A1 (fr) |
CA (1) | CA2824937A1 (fr) |
FR (1) | FR2970801B1 (fr) |
WO (1) | WO2012098225A1 (fr) |
Families Citing this family (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
ES2684643T3 (es) * | 2014-01-10 | 2018-10-03 | Pictometry International Corp. | Sistema y procedimiento de evaluación de estructura mediante aeronave no tripulada |
TWI585711B (zh) * | 2016-05-24 | 2017-06-01 | 泰金寶電通股份有限公司 | 獲得保養信息的方法、分享保養信息的方法及其電子裝置 |
Family Cites Families (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US7321791B2 (en) * | 2003-09-23 | 2008-01-22 | Cambridge Research And Instrumentation, Inc. | Spectral imaging of deep tissue |
US8224425B2 (en) * | 2005-04-04 | 2012-07-17 | Hypermed Imaging, Inc. | Hyperspectral imaging in diabetes and peripheral vascular disease |
US7689016B2 (en) * | 2005-05-27 | 2010-03-30 | Stoecker & Associates, A Subsidiary Of The Dermatology Center, Llc | Automatic detection of critical dermoscopy features for malignant melanoma diagnosis |
-
2011
- 2011-01-20 FR FR1150458A patent/FR2970801B1/fr not_active Expired - Fee Related
-
2012
- 2012-01-20 WO PCT/EP2012/050862 patent/WO2012098225A1/fr active Application Filing
- 2012-01-20 US US13/980,853 patent/US20140016842A1/en not_active Abandoned
- 2012-01-20 CA CA2824937A patent/CA2824937A1/fr not_active Abandoned
- 2012-01-20 EP EP12700700.3A patent/EP2666147A1/fr not_active Withdrawn
Non-Patent Citations (1)
Title |
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See references of WO2012098225A1 * |
Also Published As
Publication number | Publication date |
---|---|
WO2012098225A1 (fr) | 2012-07-26 |
FR2970801A1 (fr) | 2012-07-27 |
FR2970801B1 (fr) | 2013-08-09 |
CA2824937A1 (fr) | 2012-07-26 |
US20140016842A1 (en) | 2014-01-16 |
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Inventor name: PRIGENT, SYLVAIN, MERIADEC Inventor name: ZERUBIA, JOSIANE Inventor name: DESCOMBES, XAVIER Inventor name: ZUGAJ, DIDIER |
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