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Patent 2333406 Summary

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Claims and Abstract availability

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(12) Patent Application: (11) CA 2333406
(54) English Title: METHOD OF DETECTING CANCEROUS LESIONS
(54) French Title: PROCEDE POUR DETECTER DES LESIONS CANCEREUSES
Status: Dead
Bibliographic Data
(51) International Patent Classification (IPC):
  • A61B 6/00 (2006.01)
(72) Inventors :
  • ANBAR, MICHAEL (United States of America)
(73) Owners :
  • ADVANCED BIOPHOTONICS INC. (United States of America)
(71) Applicants :
  • OMNICORDER TECHNOLOGIES, INC. (United States of America)
(74) Agent: MACRAE & CO.
(74) Associate agent:
(45) Issued:
(86) PCT Filing Date: 1999-04-22
(87) Open to Public Inspection: 2000-10-22
Examination requested: 2004-03-02
Availability of licence: N/A
(25) Language of filing: English

Patent Cooperation Treaty (PCT): Yes
(86) PCT Filing Number: PCT/US1999/008853
(87) International Publication Number: WO2001/012067
(85) National Entry: 2000-11-24

(30) Application Priority Data:
Application No. Country/Territory Date
09/085,627 United States of America 1998-05-27

Abstracts

English Abstract



A method for measuring the periodicity of changes
in blood perfusion over large regions of skin so as to
identify a locally impaired neuronal control, thereby
providing a quick and inexpensive screening test for
relatively shallow neoplastic lesions, such as breast
cancer, is described. The present method is predicated
on infrared imaging of the skin to detect changes in the
spectral structure and spatial distribution of
thermoregulatory frequencies (TRFs) over different areas
of the skin.


French Abstract

La présente invention concerne une technique permettant de mesurer la périodicité des modifications enregistrées dans une perfusion sanguine sur de grandes zones cutanées de façon à identifier une anomalie locale d'un contrôle neuronal, offrant ainsi un test d'analyse rapide et bon marché destiné à des lésions néoplastiques relativement peu profondes, telles que le cancer du sein. Cette technique est fondée sur l'imagerie par infrarouge de la peau de façon à détecter des modifications dans la structure spectrale et dans la distribution spatiale des fréquences thermorégulatrices (TRF) sur différentes zones cutanées.

Claims

Note: Claims are shown in the official language in which they were submitted.




-13-
What is claimed is:
1. A method of detecting cancerous tissue, comprising
the steps of:
a) recording a plurality of infrared images of a
predetermined area of skin;
b) converting each of the plurality of infrared
images into corresponding thermal images;
c) subdividing the predetermined area of skin
into a plurality of subareas;
d) calculating an average temperature value for
the plurality of thermal images of a selected one of the
subareas, wherein the average temperature value for the
selected subarea constitutes a time series for that
subarea;
e) repeating the procedure of steps a) to d) an
the remaining subareas of the predetermined area of the
skin; and
f) analyzing the average temperature value of the
time series of each of the subareas, wherein when a
spatial distribution of the average temperature value of
a cluster comprising at least six congruent subareas is
less than about 20% or more than 100% of the average
temperature of the plurality of subareas, that cluster
is determined to be cancerous.

2. The method of claim 1 including analyzing the
average temperature value of each of the plurality of
subareas to determine a contributing frequency of each
average temperature value and displaying a presentation
of the relative amplitude value of each frequency of the
subareas, wherein when a spatial distribution of the
amplitude of a given frequency of the cluster is less
than about 25% or more than 100% of the average
amplitude value of that frequency of the plurality of
subareas, that cluster is determined to be cancerous.



-14-
3. The method of claim 1 wherein when the spatial
distribution of the average temperature amplitude of the
cluster is less than about 10% of the average amplitude
of the plurality of subareas, that cluster is determined
to be cancerous.

4. The method of claim 1 wherein the infrared images
are converted to corresponding digital thermal images.

5. The method of claim 1 including providing the
infrared image of a field of 256 x 256 pixels.

6. The method of claim 1 including recording about 128
to 1024 thermal images.

7. The method of claim 6 wherein the thermal images
are recorded consecutively.

8. The method of claim 6 including recording the
thermal images in a period of about 10 to 60 seconds.

9. The method of claim 1 including subdividing the
predetermined area of skin into the plurality of
subareas of about 24 to 100 square millimeters of skin
based on a 256 x 256 pixel field.

10. The method of claim 1 including subdividing the
predetermined area of skin into a plurality of subareas
of about 4 to 16 pixels.

11. The method of claim 1 including analyzing the
computed average temperature value of the selected
subarea using a fast Fourier transform analysis.

12. The method of claim 1 including determining that
the predetermined area of skin is of cancerous tissue.



-15-
13. The method of claim 1 including determining that
the predetermined area of akin is of non-cancerous
tissue.

14. The method of claim 1 including determining that
the predetermined area of skin includes both
non-cancerous tissue and cancerous tissue.

15. The method of claim 1 including recording the
infrared images using either a HgCdTi or a GaAs
quantum-well infrared photodetector.

16. The method of claim 1 including providing the
presentation as a color-coded bitmap.

27. The method of claim 1 including subjecting the
predetermined are of skin to either a cooling or warming
air flow.

18. The method of claim 1 wherein the predetermined
area of skin is a human breast.

19. A method of detecting cancerous tissue, comprising
the steps of:
a) recording a plurality of infrared images of a
predetermined area of skin;
b) converting each of the plurality of infrared
images into corresponding thermal images;
c) subdividing the predetermined area of skin
into a plurality of subareas;
d) calculating an average temperature value and a
temperature standard deviation for the plurality of
thermal images of each of the subareas;
e) dividing the average temperature by the
standard deviation of each of the subareas to derive a
homogeneity of skin temperature (HST) value for each



-16-
subarea, wherein the HST value for each subarea
constitutes a time series for that subarea; and
f) analyzing the HST value of the time series of
each of the subareas, wherein when a spatial
distribution of the HST value of a cluster comprising at
least six congruent subareas is less than about 20% or
more than 100% of the average HST value of the plurality
of subareas, that cluster is determined to be cancerous.

20. The method of claim 19 including analyzing the HST
value of each of the plurality of subareas to determine
a contributing frequency of each HST value and
displaying a presentation of the relative amplitude
value of each frequency of the plurality of subareas,
wherein when a spatial distribution of the amplitude of
a given frequency of the cluster is less than about 20%
or more than 100% of the average HST value of the
plurality of subareas, that cluster is determined to be
cancerous.

21. The method of Claim 19 wherein the infrared images
are converted to corresponding digital thermal images.

22. The method of claim 19 including providing the
infrared image of a field of 256 x 256 pixels.

23. The method of claim 19 including recording about
128 to 1024 thermal images.

24. The method of claim 23 including recording the
thermal images in a period of about 10 to 60 seconds.

25. The method of claim 19 including subdividing the
predetermined area of skin into the plurality of
subareas of about 24 to 100 square millimeters of skin
based on a 256 x 256 pixel field.



-17-
26. The method of claim 19 including subdividing the
predetermined area of skin into a plurality of subareas
of about 4 to 16 pixels.

27. The method of claim 19 including analyzing the
computed average temperature value of the selected
subarea using a fast Fourier transform analysis.

28. The method of claim 19 including providing the
presentation as a color-coded bitmap.

29. The method of claim 19 including subjecting the
predetermined area of skin to either a cooling or
warming air flow.

30. A method of detecting cancerous tissue, comprising
the steps of:
a) recording a plurality of infrared images of a
predetermined area of skin;
b) converting each of the plurality of infrared
images into corresponding thermal images;
c) subdividing the predetermined area of skin
into a plurality of subareas;
d) calculating an average temperature value and a
temperature standard deviation for the plurality of
thermal images of a selected one of the subareas,
wherein the temperature standard deviation value for the
selected subarea constitutes a time series for that
subarea;
e) repeating the procedure of steps a) to d) on
the remaining subareas of the predetermined area of the
skin; and
analyzing the temperature standard deviation
value of the time series of each of the subareas,
wherein when a spatial distribution of the temperature
standard deviation value of a cluster comprising at
least six congruent subareas is less than about 20% or



-18-
mere than 100 of the temperature standard deviation of
the plurality of subareas, that cluster is determined to
be cancerous.

31. The method of claim 30 including analyzing the
temperature standard deviation value of each of the
plurality of subareas to determine a contributing
frequency of each temperature standard deviation value
and displaying a presentation of the relative amplitude
value of each frequency of the subareas, wherein when a
spatial distribution of the amplitude of a given
frequency of the cluster is less than about 25% or more
than look of the average amplitude value of that
frequency of the plurality of subareas, that cluster is
determined to be cancerous.

32. A method of detecting cancerous tissue, comprising
the steps of:
a) recording a plurality of infrared images of a
predetermined area of skin;
b) converting each of the plurality of infrared
images into corresponding thermal images;
c) subdividing the predetermined area of skin
into a plurality of subareas;
d) calculating an average temperature value and a
temperature for the plurality of thermal images of a
selected one of the subareas, wherein the average
temperature standard deviation value for the selected
subarea constitutes a time series for that subarea;
e) repeating the procedure of steps a) to d) on
the remaining subareas of the predetermined area of the
skin;
f) analyzing the average temperature value of the
time series of each of the subareas, wherein when a
spatial distribution of the average temperature value of
a cluster comprising at least six congruent subareas is
less than about 20% or more than 100% of the average



-19-
temperature of the plurality of subareas, that cluster
is determined to be cancerous, and if a cluster is
determined to be cancerous;
g) dividing the average temperature by the
standard deviation of each of the subareas to derive a
homogeneity of skin temperature (HST) value for each
subarea, wherein the HST value for each subarea
constitutes an HST time series for that subarea; and
h) analyzing the HST value of the time series of
each of the subareas, wherein when a spatial
distribution of the HST value of a cluster comprising at
least six congruent subareas is less than about 20% or
more than 100% of the average HST value of the plurality
of subareas, that cluster is determined to be cancerous.

Description

Note: Descriptions are shown in the official language in which they were submitted.



O1%24%O1 11:08 FAQ 613 234 3583 ilacRae & Co. ~ 005
_ . ~ ~~
. : _ W,
4
b~T80D OF DETECTION OF CANCEROUS
LESIONS HY TBEIR LFp'$CT ON ~ SPATIAL
DZSTRIBLiTION OF MODULATION OF TE'~dPERATURE
AND HOMOGENEITY OF TISSUE
CROSS-REFERENCE TO RELATED A..ppLICATION
The present application is a continuation-in-part
of application Serial No. 08/368,161, filed January 3,
1995.
BACKGROUND OF THE INVENTION
The present invention relates generally to cancer
detection and, more particularly, to~a cancer detection
method involving the measurement, recording and analysis
of temporal periodic perfusion changes associated with
dysfunction of neuronal control of the vasculature
surrounding cancerous lesions. While the present
invention has application to cancer detection throughout
the human body, it za particularly applicable to a
breast cancer screening teat involving the measurement
of temporal changes in perfusion over large areas of the
breasts to identify cancer. Specifically, cancer
detection is derived from a time series of infrared
images of an area o.f interest of a human breast. The
24 infrared images relate to the temporal periodic
perfusion changes and are converted by a computer to
time series. average temperature and standard deviation
of each of a plurality of subareas. The data is then
analyzed to identify clusters having abnormal
temperature dependent frequencies indicative of cancer.
SUMMARY OF THE INVENTION
Thermoregulatory frequencies of the processe$ that
control akin temperature over different areas of the
body are derived from the periodic changes in
temperature distribution over those skin areas. As
shown by Dr. Anbar in the European J Thermology 7:105-
118, 1997, under conditions of hyperperfusion, the
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homogeneity of the skin temperature distribution reaches
a maximum and the amplitude of its temporal modulation
is at a minimum. In other words, periodic changes in
the spatial homogeneity of akin temperature (HST) are
dictated by the processes that control the satuz~ation of
the cutaneous capillary bed in a particular area of the
akin. Accordingly, skin temperature and HST are
independent physiological hemodynamic parameters that
are determined by the structure of the cutaneous
vasculature and by its heat dissipatory activity.
However, unlike average temperature,~HST is affected
mainly by the behavior of the cutanevus capillaries and
to a much lesser extent by the blood flow in
subcutaneous vessels. As perfusion is enhanced, more
capillaries are recruited as blood conduits and HST
increases. The neuronal control of HST is, therefore,
different from that of skin temperature. Gonseqvently,
the spatial distribution of changes in skin temperature
and homogeneity, HST, of tissue derived from a time
series of infrared images of a skin area according to
the present invention is more informative than a
classical thermogram. HST is the reciprocal of the
spatial coefficient of variation of temperature in small.
( T~micro" ) areas of skin (< 100 mmz) : HST = average
temperature divided by the standard deviation of the
average temperature (HST is a dimensionless parameter).
The concept of HST has been fully described by,.Dr.
Michael Anbar in Biomedical Thermology, 13:173-187,
1994.
These and other objects of the present invention,
will become increasingly more apparent to those skilled
in the art by reference to the following description.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
Although skin temperature may vary over a wide
range, depending upon the environment and on the level
of metabolic activity, it is regulated under normal
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conditions. This regulation may be occasionally less
stringent, like during sleep but even then, some skin
regulation is retained. In the human body, skin
temperature maintenance is due, in substantial part to
S neuronal thermoregulation of vasoconstriction and
vasodilation of the vasculature, thereby causing a
characteristic mvdulativn of blvvd perfusion, unless the
neuronal thermoregulation is inhibited or taken over by
a non-neuronal thermoregulatory control, such as nitric
oxide (NO). Modulation of skin temperature is on the
order of 10 to 50 millidegrees Kelvin.
NO has been recognized as an ubiguitous
vasodilatory chemical messenger. Its main role appears
to be synchronisation of intercellular and inter organ
functions, because it diffuses freely in the
interstitial space. This has been discussed extensively
by Dr. ~lnbar in J. Pain Symptom Management 14:225-254,
1997. Under certain conditions, such as in breast
cancer, autocatalytic production of NO may occur, which
results in oscillatory vasodilation, independent of and
substantially different from the temperature
vacillations of perfusion caused by the neuronal
thermvregulatory system. NO can, therefore, inhibit
autonomic vasoconstrictive control in substantial
regions of the microvasculature and cause regional
hyperperfusion. Subcutaneous and cutaneous
hyperperfusion are manifested as hyperthermia of the
overlying skin.
Thus, cancer associated breast hyperthermia results
from impaired neuronal thermoregulation caused by
excessive production of NO by breast cancer cells and in
macrophages that react to the neopJ~astic tissue. The
latter activity is an expression of the immune response
because NO generated by macrophages is a major factor in
killing of microorganisms or of mammalian cells
recognized as alien. The mechanism for this activity
is described fully in a 1994 publication of Dr. Michael
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- 4 -
Anbar, Cancer Letters 84:2 329, 1994. The rate of
production of NO is further enhanced by the presence of
ferritin, the level of which is significantly elevated
in breast cancerous tissue. Fe~+ released from ferritin
S is used to produce more NO synthase (NOS), an iron
carrying enzyme which produces NO from arginine, and
thus results in a further increase in the rate of
production of NO. Furthermore, NO has been shown to
release Fey'' from ferritin by forming an NO-ferritin
complex which consequently results in an autocatalytic
production of NO. Fey' also eliminates superoxide
radicals (HOZ) which are normally the major scavengers of
NO. Elimination of H02 maintains the local high level of
NO, and hyperperfusion of the tissues surrounding the
cancerous lesion. The ferritin dependent enhancement of
NO production seems to be specific to neoplastic cells
and is less likely to occur in other inflammatory
situations, including those induced by microoxganzsms.
Therefore, the freguencies of temperature and HST
oscillations observed over a cancerous breast differ
substantialJ.y from those observed over a non-cancerous
(normal) breast. The oscillations over a normal breast
are caused by the neuronal thermoregulatory processes,
which follow several characteristic bands of
frequencies. The cancerous area of the breast, on the
other hand, which loses its neuronal thermoregulatory
control due to the over-production of NO, is
characterized by the disappearance of the neuronal
osciilations and the appearance of oscillations due to
the autocatalysis of NO production, with their typical
frequency bands. The latter autocatalytic processes,
controlled by the temporary local depletion of one of
the precursors of NO, are utterly different in nature
from the neurological feedback processes manifested in
the neuronal frequency bands. Consequently, the
disappearance of neuronal frequencies over substantial
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parts of the cancerous breast is sufficient to identify
pathology. Furthermore, the appearance of the
autoeatalytic freguencies characteristic o~ NO
over-production is, by itself, sufficient to identify
pathology. The substitution of one set of frequeriCy
bands by the other is an even more strict cz~iterion of
pathology in a human breast. Hemodynamic modulation is
generally in the range of 0.8 to 2 Hz, whereas
modulation by the autonomic nervous system ranges
generally between 10 and 700 mHz.
It is the detection of these freguencies, whether
neuronal or non-neuronal, which is the basis o~ the
detection methods of the present invention.
Specifically, under conditions of extravascular NO
overproduction and its consequent hyperperfusion, skin
temperature and the homogeneity of skin temperature,
HST, reach values that oscillate at frequencies
dependent on the modulated autocatalytic rate of NO
production. The frequency bands v~ the modulation of
temperature and HST are, according to the present
detection methods, independent criteria of pathology.
It should be understood that while the present
detection methods are described herein with respect to a
human breast, it is not intended to be so limited.
Detection o~ cancerous lesion$ in the hands, arms,
chest, Legs, buttocks, back and literally any other body
part is possible with the present methods.
Thus, the screening techniques of the present
invention use the spatial distribution of the
characteristic oscillations or modulations in the
temporal behavior of blood perfusion caused by enhanced
NO production by cancerous cells and in macrophages and
amplified by ferritin to detect an immune response
induced by neoplastic disease. The temperature
vscillat~.on of blood perfusion associated with the
autocatalytic production of NO, as well as the
diminution or disappearance of the neuronal
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thermoregulatory frequencies, TRFs, are used as the
diagnostic parameters. The neuronal and autocatalytic
oscillations are measured by fast Fourier transform
(FFT) analysi$, an analysis method well known in the
art, of the temporal behavior of breast perfusion
(manifested in the tempvrax behavior of breast
temperature and of HST). The TRFs of HST are,
therefore, addztianal independent diagnostic parameters.
Accordingly, the present invention provides for the
accumuiation of hundreds of sequential thermal images
that are then subjected to FFT to extract the
frequencies and amplitudes of periodic changes at each
pixel of the image. To measure the spatial distribution
of modulation of temperature and HST, an infrared camera
is positioned to provide infrared flux images of a part
of the human body, for example a human breast. A
preferred camera is equipped with a 256 x 256 focal
plane array (FPA) GaAs quantum-well infrared
photodetectvr (QWIP). Such a camera can record
modulation of skin temperatuz~e and its homogeneity with
a precision greater than f 1 millidegrees C, i.e., less
than 1/10 of physiological modulation of temperature and
of homogeneity of human skin. Another camera that is
useful with the present invention i$ a HgCdTi infrared
photodetector.
While successful results can be achieved by
analyzing the temporal behavior of at least 7.28 thermal
images, it is most preferred to measure 1024 consecutive
thermal images over a period o~ 10 to 60 seconds. The
infrared images are transmitted to a CPU having an
analog/digital converter which processes the recorded
infrared flux information and outputs digital infxexed
flux data.
To determine whether the breast is normal or
cancerous, the CPU factors the digital data to
equivalent temperature readings for a designated spot
subarea or group of spots of the breast (each spot
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subarea corresponds to about 25 to 100 mm~ of skin based
on a 256 x 256 pixel field, as will be described
hereinafter), which are later to be analyzed for the
presence of lesions. A spot is typically about four to
sixteen pixels and can have the shape of a circle,
square or other shapes. The temperature values of the
pixels in each subarea of the image are averaged. The
variance of the average temperature is used to calculate
the HST of each subarea. The accumulated images are
then analyzed by FFT to extract the corresponding
frequencies of the average temperature and standard
deviation of average temperature. The FFT yield$ the
frequency spectra of each pixel together with the
relative amplitude o~ each TRF. The software then
1.5 tabulates, or displays as color-coded bitrnaps, the
spatial distribution of the TRFs within a given range of
relative ampzitudes over the image. The same procedure
is followed with the HST data.
When TRFs in a cluster of subaxeas are displayed
with amplitudes above a given threshold, a subset of
characteristic neuronal frequencies over areas of
breasts free from cancer~enhanced immune response is
identified. A cluster is defined as at least $ix
congruent subareas of four to sixteen pixels per
subarea. Such TRFs are significantly attenuated or
completely absent in areas overlying breasts with
neoplastic lesions. The latter areas are characterized
by non-neuronal thermoregulatory behavior which manifest
substantially different TRFs caused by the autocatalytic
production of NO. Also the latter areas are, therefore,
characterized by aberrant modulation of blood perfusion
and aberrant temperature oscillations. A hard copy in
the form of a colored bitmap of the imaged skin area is
! then generated to allow an expert to anatomically
identify the location of the aberrant area, or areas.
The computer algorithms that facilitate this
computation are as follows:
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A. Use of temperature values of individual pixels and
the computation of TRFs.
1. The computerized camera takes an image of the
infrared flux (256 x 256 pixels) of an area of
interest of the skin, such as of a human
breast, and converts it into a digital thermal
image where each pixel has a certain
temperature value. This process is repeated,
preferably thirty times a second until 1024
thermal images have been accumulated and
stored in the computer.
2. The infrared image is subdivided into subareas
of 4 to 16 pixels, each subarea corresponding
to about 25 to 100 square millimeters of skin,
depending on the optics of the camera and the
geometry of the measurement. For example, in
a 256 x 256 pixel field, each pixel. represents
about 2.4 mm2. Higher resolution camera will
have greater skin area detail.. The computer
then converts each infrared image into a
digital thermal image where each pixel has a
certain temperature value.
3. The computer calculates the average
temperature and the temperature standard
deviation of each of the subareas in each of
the 1024 images. The average temperature
values of each subarea constitutes a'single
time series that is theca subjected to ~'FT
analysts to extract the contributing
i frequencies of each image of the series and
' their relative amplitudes. The computer
stores the resulting FFT spectrum for the
calculated subarea related to the subdivided
area of the image_ The computez~ repeats the
same procedure far each of the subareas of the
image of the skin. Additionally, or in the
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altez-native, the temperature standard


deviation value of each subarea can be used to


calculate a time series that is subjected to


FFT analysis tv extract the contributing


frequencies of each image of the series and


their relative amplitudes.


4. The computer selects the FFT spectra of


each of the subareas and displays colored


bitmaps of the relative amplitudes


derived from the FFT analysis in any


exhibited range of frequencies to thereby


identify clu$ters of subareas having


frequencies with abnormal amplitudes.


5. Tf procedure #4 does not identify definitely


aberrant clusters, the computer determines


that the test is negative for cancer and the


patient is normal. A message to this effect


is then output by the computer. Otherwise,


the computer proceeds with procedure #6.


6. The computer examines all the pixels i.n the


aberrant clusters identified in procedure #4


tv identify frequencies with amplitudes that


are Characteristic of cancer, i.e., at least


six congruent ~eubareas wherein a spatial


distribution of the amplitude value in the


congruent subareae is less than about 10%, and


preferably less than about 20%, yr more than


100% of the average amplitudes of the


temperature values of all of the surrounding


subareas.


7. If procedure #4 identifies a definitely


aberrant cluster, while procedure #6 turns out


negative, the computer prints out a color


image v~ the breast. If procedure #6 yield a


confirmation, the computer prints out another


color image with the aberrant clusters.


i
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B. Use of HST values and the computation of HST
TR.Fs .
1. The computerized camera takes on image of the


infrared flux (256 x 256 pixels) of an area of


interest of the skin, such as a human breast,


and Converts it into a thermal image where


each pixel has a certain temperature value_


This process is respected, preferably thirty


times a second until 1024 thermal images have


been accumulated and stored in the computer.


2. The infrared image is subdivided into subareas


of 4 to 16 pixels, each subarea corresponding


to about 25 to 100 square millimeters of skin,


based on the 256 x 256 optics resolution of


the camera. The computer then converts each


infrared image into a digital thermal image


where each pixel has a certain temperature


value.


3. The computer caJ.culates the average


temperature (AVT) value and temperature


standard deviation (SD) of each subarea. The


computer then calculates the HST value for


each subarea: HST = AVT/SD. The HST values


are analysed by FFT to extract the


contributing frequency of each subarea to


yield HST TRFs, and the relative amplitude Qf


each frequency.


4. The computer selects the FFT spectra.-'of each


of the subareas and displays colored bitmape


3~ of the relative amplitude derived from the FFT


analysis in any exhibited range of frequencies


to thereby identify clusters of subareas with


frequencies having abnormal amplitudes.


s. If procedure #4 d4es not identify definitely


aberrant clusters, the computer determines


that the test is negative for cancer and the


patient is normal. A message to this effect


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is then output by the computer. Otherwise,
the computer proceeds with procedure #6.
6. The computer examines all the subareas in the
aberrant clusters identified in procedure #4
to identify frequencies that are
characteristic of cancer, i.e., at least six
congruent subareas wherein a spatial
distribution of the amplitude value in the
congruent aubareas is less than about 10%, and
preferably less than about 20%, yr more than
100% of the amplitudes of the HST values of
all of the surrounding subareas.
7. If procedure #4 identifies a definitely
aberrant cluster, while procedure #6 tuz~ne out
negative, the computer prints out a color
image of the breast. If procedure #6 yields a
confirmation, the computer prints out another
color image with the aberrant clusters.
8. A match between the findings of procedure A
and B increases the diagnostic certainty of
the detection method of the present invention.
According to a further aspect of the present
invention, the difference between normal and cancerous
breasts is accentuated by a thermal challenge (cooling
or warming) of the breasts. Such a thermal challenge
affects only the neuronal thermoregulatory system and
therefore affects anly TRFs in areas that are not
vasodilated by excessive extravascular NO production.
The computer is programmed to look for the frequency
bands of the neuronal and the NO controlled TRFs in
every subset of pixels (e.g., 4 to 16 pixels) of the FFT
processed image. If the computer does not find any
clusters with neuronal TRFs having exceptionally low
amplitude (except in the periphery of the image which
does not depict skin), and no clusters are found to have
the NO controlled autacatalytic TRFs with a significant
CA 02333406 2000-11-24

01!24%O1 11:11 Fad 813 234 3583 MacRae & Co. 1~J016
- 12 -
amplitude, the findings of the test are declared as
negative (i.e., normal). This finding is then confirmed
by computing and analyzing the HST TRFs. If the
computer finds certain clusters with exceptionally low
neuronal TRF$ and especially if those clusters exhibit
the NO controlled autocatalytic TRFs, the test findings
are classified as pathological. This finding is then
confirmed by analyzing the HST data, as described for
the uncooled or unwarmed breast. Cooling or warming of
the breasts (by a mild flow of forced air? attains
maximal sensitivity and specificity. .Such additional
testing is administered as a confirmatory test only to
patients who show a positive result on the uncooled
test.
Although the invention has been described zn detail
for the purpose of illustration, it is to be understood
that such detail is solely for that purpose and that
variations can be made therein by those skilled in the
art without departing from the spirit and scope of the
invention except as it may be limited by the claims.
CA 02333406 2000-11-24

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Administrative Status

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Administrative Status

Title Date
Forecasted Issue Date Unavailable
(86) PCT Filing Date 1999-04-22
(87) PCT Publication Date 2000-10-22
(85) National Entry 2000-11-24
Examination Requested 2004-03-02
Dead Application 2009-04-22

Abandonment History

Abandonment Date Reason Reinstatement Date
2006-04-24 FAILURE TO PAY APPLICATION MAINTENANCE FEE 2007-04-19
2008-04-22 FAILURE TO PAY APPLICATION MAINTENANCE FEE

Payment History

Fee Type Anniversary Year Due Date Amount Paid Paid Date
Application Fee $150.00 2000-11-24
Maintenance Fee - Application - New Act 2 2001-04-23 $50.00 2001-03-14
Registration of a document - section 124 $100.00 2001-03-16
Maintenance Fee - Application - New Act 3 2002-04-22 $50.00 2002-02-14
Maintenance Fee - Application - New Act 4 2003-04-22 $100.00 2003-03-07
Request for Examination $800.00 2004-03-02
Maintenance Fee - Application - New Act 5 2004-04-22 $200.00 2004-03-24
Maintenance Fee - Application - New Act 6 2005-04-22 $200.00 2005-03-09
Reinstatement: Failure to Pay Application Maintenance Fees $200.00 2007-04-19
Maintenance Fee - Application - New Act 7 2006-04-24 $200.00 2007-04-19
Maintenance Fee - Application - New Act 8 2007-04-23 $200.00 2007-04-19
Registration of a document - section 124 $100.00 2007-05-24
Owners on Record

Note: Records showing the ownership history in alphabetical order.

Current Owners on Record
ADVANCED BIOPHOTONICS INC.
Past Owners on Record
ANBAR, MICHAEL
OMNICORDER TECHNOLOGIES, INC.
Past Owners that do not appear in the "Owners on Record" listing will appear in other documentation within the application.
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Document
Description 
Date
(yyyy-mm-dd) 
Number of pages   Size of Image (KB) 
Cover Page 2001-04-20 1 22
Abstract 2000-11-24 1 18
Description 2000-11-24 12 582
Claims 2000-11-24 7 272
Description 2004-05-25 12 576
Claims 2007-12-07 4 145
Fees 2002-09-18 3 101
Correspondence 2001-03-05 1 24
Assignment 2000-11-24 3 95
PCT 2000-11-24 11 495
Prosecution-Amendment 2000-11-24 1 20
PCT 2001-01-15 1 47
Assignment 2001-03-16 6 297
PCT 2001-01-12 1 63
PCT 2001-02-22 1 31
PCT 2001-02-20 3 147
PCT 2001-08-01 1 57
Prosecution-Amendment 2004-03-02 1 28
Prosecution-Amendment 2004-05-25 3 85
Prosecution-Amendment 2007-06-07 3 95
Fees 2007-04-19 1 32
Assignment 2007-05-24 3 94
Prosecution-Amendment 2007-12-07 7 255