Systems and methods for detecting anomaly in biological tissues
The usage of microwave imaging for biomedical (BMWI) applications is still challenging due to the imprecise reconstruction of the relative permittivity of tissues and the ill-posed inverse scattering problem. Anomaly detection in biological tissues demand in-vivo, non-invasive, and non-contact measurements. Considering and proving the anomaly as a point object in the microwave imaging is erroneous and results in false implications about the anomaly's presence, location, and characteristics. Present disclosure provides systems and methods for anomaly detection in biological tissues. An intensity map of target region is generated to detect tumor. Area around the tumor is processed to obtain a refined image. As the tumor is embedded in tissues of higher dielectric constant, image thus formed is larger in size than actual tumor. An iterative numerical computation method is implemented to estimate relative permittivity. Subsequently, the effective size of the anomaly is approximately estimated, which is close to their actual values.
1 . A processor implemented method, comprising:
receiving, at a Vector Network Analyzer (VNA) via one or more hardware processors, one or more reflected microwaves projected on a duty under test (DUT) kept at an initial distance, by using one or more antennas, wherein the DUT comprises a biological tissue;
measuring, by using the VNA via the one or more hardware processors, a first reflection coefficient from the one or more reflected microwaves;
converting, via the one or more hardware processors, the first reflection coefficient to a time series data and filtering the time series data to obtain a filtered time series data;
applying, a Delay-Multiply-and-Sum (DMAS) technique via the one or more hardware processors, on the filtered time series data to generate a radar return image at a distance from a frontend of the one or more antennas;
measuring, via the one or more hardware processors, a peak intensity from the radar return image;
obtaining, via the one or more hardware processors, an optimum distance by varying the distance at which the peak intensity is maximum;
computing, via the one or more hardware processors, a measured distance based on a comparison between the optimum distance and the initial distance;
measuring, by using the VNA via the one or more hardware processors, a second reflection coefficient specific to a first scenario and a third reflection coefficient specific to a second scenario;
iteratively performing, via the one or more hardware processors:
computing a permittivity for a plurality of frequencies based on a thickness of DUT, using the second reflection coefficient and the third reflection coefficient;
performing a first comparison of the permittivity with a first threshold; and
performing a second comparison of a degree of the DUT with a convergence criteria,
until a candidate effective permittivity for each of the plurality of frequencies is obtained based on the first comparison and the second comparison;
computing, via the one or more hardware processors, an effective permittivity based on the candidate effective permittivity computed for each of the plurality of frequencies; and
computing, via the one or more hardware processors, at least one of an actual depth and an actual diameter of an anomaly in the biological tissue based on the measured distance and the effective permittivity.
2 . The processor implemented method of claim 1 , wherein the filtering of the time series data to obtain the filtered time series data is based on a second threshold.
3 . The processor implemented method of claim 2 , wherein the second threshold is a pre-configured threshold or an empirically determined threshold.
4 . The processor implemented method of claim 1 , wherein the first scenario comprises measuring the second reflection coefficient without placing the DUT between the one or more antennas, and the second scenario comprises measuring the third reflection coefficient by placing the DUT between the one or more antennas.
5 . The processor implemented method of claim 1 , wherein the step of computing the permittivity is based on an iterative search method.
6 . A system, comprising:
a memory storing instructions;
one or more communication interfaces; and
one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
receive, at a Vector Network Analyzer (VNA), one or more reflected microwaves projected on a duty under test (DUT) kept at an initial distance, by using one or more antennas, wherein the DUT comprises a biological tissue;
measure, by using the VNA, a first reflection coefficient from the one or more reflected microwaves;
convert the first reflection coefficient to a time series data and filtering the time series data to obtain a filtered time series data;
apply a Delay-Multiply-and-Sum (DMAS) technique on the filtered time series data to generate a radar return image at a distance from a frontend of the one or more antennas;
measure a peak intensity from the radar return image;
obtain an optimum distance by varying the distance at which the peak intensity is maximum;
compute a measured distance based on a comparison between the optimum distance and the initial distance;
measure, by using the VNA, a second reflection coefficient specific to a first scenario and a third reflection coefficient specific to a second scenario;
iteratively perform:
computing a permittivity for a plurality of frequencies based on a thickness of DUT, using the second reflection coefficient and the third reflection coefficient;
performing a first comparison of the permittivity with a first threshold; and
performing a second comparison of a degree of the DUT with a convergence criteria,
until a candidate effective permittivity for each of the plurality of frequencies is obtained based on the first comparison and the second comparison;
compute an effective permittivity based on the candidate effective permittivity computed for each of the plurality of frequencies; and
compute at least one of an actual depth and an actual diameter of an anomaly in the biological tissue based on the measured distance and the effective permittivity.
7 . The system of claim 6 , wherein the filtered time series data is based on a second threshold.
8 . The system of claim 7 , wherein the second threshold is a pre-configured threshold or an empirically determined threshold.
9 . The system of claim 6 , wherein the first scenario comprises measuring the second reflection coefficient without placing the DUT between the one or more antennas, and the second scenario comprises measuring the third reflection coefficient by placing the DUT between the one or more antennas.
10 . The system of claim 6 , wherein the permittivity is computed based on an iterative search method.
11 . One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:
receiving, at a Vector Network Analyzer (VNA), one or more reflected microwaves projected on a duty under test (DUT) kept at an initial distance, by using one or more antennas, wherein the DUT comprises a biological tissue;
measuring, by using the VNA, a first reflection coefficient from the one or more reflected microwaves;
converting the first reflection coefficient to a time series data and filtering the time series data to obtain a filtered time series data;
applying, a Delay-Multiply-and-Sum (DMAS) technique, on the filtered time series data to generate a radar return image at a distance from a frontend of the one or more antennas;
measuring a peak intensity from the radar return image;
obtaining an optimum distance by varying the distance at which the peak intensity is maximum;
computing a measured distance based on a comparison between the optimum distance and the initial distance;
measuring, by using the VNA, a second reflection coefficient specific to a first scenario and a third reflection coefficient specific to a second scenario;
iteratively performing:
computing a permittivity for a plurality of frequencies based on a thickness of DUT, using the second reflection coefficient and the third reflection coefficient;
performing a first comparison of the permittivity with a first threshold; and
performing a second comparison of a degree of the DUT with a convergence criteria,
until a candidate effective permittivity for each of the plurality of frequencies is obtained based on the first comparison and the second comparison;
computing an effective permittivity based on the candidate effective permittivity computed for each of the plurality of frequencies; and
computing, at least one of an actual depth and an actual diameter of an anomaly in the biological tissue based on the measured distance and the effective permittivity.
12 . The one or more non-transitory machine-readable information storage mediums of claim 11 , wherein the filtering of the time series data to obtain the filtered time series data is based on a second threshold.
13 . The one or more non-transitory machine-readable information storage mediums of claim 12 , wherein the second threshold is a pre-configured threshold or an empirically determined threshold.
14 . The one or more non-transitory machine-readable information storage mediums of claim 11 , wherein the first scenario comprises measuring the second reflection coefficient without placing the DUT between the one or more antennas, and the second scenario comprises measuring the third reflection coefficient by placing the DUT between the one or more antennas.
15 . The one or more non-transitory machine-readable information storage mediums of claim 11 , wherein the step of computing the permittivity is based on an iterative search method.