DESC:CROSS REFERENCE TO RELATED APPLICATION
This application is based on and derives the benefit of Indian Provisional Application 201841007545 as filed on 28th February, 2018, the contents of which are incorporated herein by reference
TECHNICAL FIELD
[001] Embodiments herein relate to image processing, and more particularly to methods and systems for performing a stencil guided image acquisition for an optimized clinical application.
BACKGROUND
[002] Currently, there is a proliferation of low-cost real-time medical imaging modalities such as ultrasound imaging. The medical imaging modalities are being increasingly used by personnel, who require guidance for capturing clinically prescribed diagnostic quality images. The guidance provided needs to understandable to the personnel who is capturing the data. Further, the guidance needs to be non-intrusive. The acquired image quality needs to be optimal for extracting required quantification parameters.
[003] Automated quantification of parameters can be performed in order to avoid subjectivity in diagnosis. Automated quantification can be performed using Artificial Intelligence (AI). The automated solutions (which perform automated quantification using AI) may require quality imaging data, which may not be available. In an example, certain parameters (to be quantified) may require that the prescribed anatomy has to be clearly depicted in the field-of-view, some of the parameters (to be quantified) may be appropriate only in views prescribed by clinical guidelines, and so on.
OBJECTS
[004] The principal object of the embodiments herein is to disclose methods and systems for performing a stencil guided image acquisition for an optimized clinical application.
[005] Another object of the embodiments herein is to overlay a reference stencil on a live image, which is being acquired through a medical imaging modality.
[006] Another object of the embodiments herein is to determine a figure of merit, wherein the figure of merit can signify a degree of match obtained by overlaying the reference stencil on the live image.
[007] Another object of the embodiments herein is to provide a feedback, based on the figure of merit, which can be used for determining specific quantification parameters that are appropriate for extraction from the live image and a means to extract the determined specific quantification parameters.
SUMMARY
[008] Accordingly, the embodiments provide methods and systems for performing a stencil guided image acquisition for an optimized clinical application. The embodiments include providing a reference stencil, which can be used as a guide by a clinician to determine parameters, which are appropriate for quantification, from live acquired images. The embodiments include overlaying a stencil on a screen displaying a live image. The live image displayed on the screen can be obtained using medical imaging modalities. The embodiments include determining a figure of merit based on a match between the reference stencil and the live image. The embodiments include providing a feedback to the clinician based on the figure of merit. The feedback can indicate appropriateness of specific quantification parameters for extraction from the live image and indicate methods to estimate the specific quantification parameters.
[009] These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following descriptions, while indicating embodiments and numerous specific details thereof, are given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.
BRIEF DESCRIPTION OF FIGURES
[0010] Embodiments herein are illustrated in the accompanying drawings, through out which like reference letters indicate corresponding parts in the various figures. The embodiments herein will be better understood from the following description with reference to the drawings, in which:
[0011] FIG. 1 depicts various units of an apparatus for performing a stencil guided image acquisition for an optimized clinical application, according to embodiments as disclosed herein;
[0012] FIG. 2 is a flowchart depicting a method for performing a stencil guided image acquisition for an optimized clinical application, according to embodiments as disclosed herein;
[0013] FIG. 3a depicts an example selected reference stencil, according to embodiments as disclosed herein;
[0014] FIGS. 3b and 3c depict an example variation in figure of merit obtained by overlaying the selected reference stencil on different example live images, according to embodiments as disclosed herein;
[0015] FIG. 4 depicts a stencil palette and corresponding user action, according to embodiments as disclosed herein; and
[0016] FIGS. 5a-5d depict example stencils, according to embodiments as disclosed herein.

DETAILED DESCRIPTION
[0017] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0018] Embodiments herein disclose methods and systems for performing a stencil guided image acquisition for an optimized clinical application. The embodiments include providing a reference stencil, which can be used as a guide by a clinician to determine parameters, which are appropriate for quantification, from live acquired images. The embodiments include overlaying a stencil on a screen displaying a live image. The live image displayed on the screen can be obtained using medical imaging modalities. The embodiments include determining a figure of merit based on a match between the reference stencil and the live image. The embodiments include providing a feedback to the clinician based on the figure of merit. The feedback can indicate appropriateness of specific quantification parameters for extraction from the live image and indicate methods to estimate the specific quantification parameters.
[0019] Referring now to the drawings, and more particularly to FIGS. 1 through 5c, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0020] FIG. 1 depicts various units of an apparatus 100 for performing a stencil guided image acquisition for an optimized clinical application, according to embodiments as disclosed herein. As depicted in FIG. 1, the apparatus 100 can be an image acquiring unit 101, a stencil management unit 102, a processing unit 103, and a display unit 104. The apparatus 100 can be used for acquiring and displaying medical images. The apparatus 100 can be a part of an ultrasound scanner, a Computed Tomography (CT) scanner, an Electroencephalogram (EEG) scanner, an Electrocardiogram (ECG) scanner, a Magnetic Resonance Imaging Scanner (MRI) scanner, and so on.
[0021] The image acquiring unit 101 can acquire live images of organs through a medical imaging modality. Examples of the medical imaging modality can be, but not limited to, an ultrasound scan, CT scan, ECG scan, EEG scan, MRI scan, and so on. In an example, when an ultrasound scan is being performed, the image acquiring unit 101 can acquire live ultrasound images and display the acquired images on the display unit 104.
[0022] The stencil can act as a reference to the live images, being acquired by the image acquiring unit 101. Examples of the stencils can be, but not limited to, at least one of images, graphics, animations, outlines, shapes, and so on. In an embodiment herein, the stencils can be obtained from a subject, who is being scanned currently, through medical imaging modalities and stored in a data storage location (such as a memory, database, file server, the Cloud, and so on) (not shown). In an embodiment herein, the stencils can be obtained from a subject, who was previously scanned, through medical imaging modalities and stored in a data storage location (such as a memory, database, file server, the Cloud, and so on) (not shown). In an embodiment herein, the stencils can be provided by an authorized person (such as service person, a technician, a clinician, and so on) and stored in a data storage location (such as a memory, database, file server, the Cloud, and so on) (not shown).
[0023] In an embodiment herein, the stencil can be selected manually by an authorized person (such as sonographer, a technician, a clinician, an operator, and so on). Embodiments herein refer to the person operating the image acquiring unit 101 as a ‘clinician’.
[0024] In an embodiment herein, the stencil can be selected automatically by the stencil management unit 102. The stencil management unit 102 can select the stencil based on parameters, such as the type of medical imaging modality, subject information (such as gender, age, weight, height, and so on), the part of the body of the subject being scanned, the data/information that is required from the acquired images, specific quantification parameters to be measured from the acquired images, and so on. The selected stencils can be pre-sized stencils based on age, gender, and so on, of the subject being scanned currently.
[0025] In an embodiment herein, the selected stencils can be drawn by the clinician. The medical imaging modality, through which the live images are acquired and the stencils are obtained, can be either same or different. In an example, if the live images are acquired through an ultrasound scan, the stencil can be obtained by performing an ultrasound scan or a different type of scan.
[0026] The stencil management unit 102 can adapt the stencils based on specific features that are detected in a live image by applying relative deformation to the stencils based on the specific features. The stencils need to be adapted, if age and gender of a subject, from which live images are acquired, is different from a subject from which the stencil has been obtained. The stencils of different persons can be adapted to a predefined size based on age, gender, observations from the live images, and so on. In an embodiment herein, the stencils can be adapted manually by scaling the stencils. In an embodiment herein, the stencils can be adapted automatically by the stencil management unit 102, based on parameters such as the source of the stencil, the patient information, observations from the live images, and so on.
[0027] The processing unit 103 can overlay the stencil on a live image. In an embodiment herein, the processing unit 103 can enable the clinician to overlay the stencil on a suitable location. In an embodiment herein, the processing unit 103 can overlay the stencil on a suitable location automatically on analyzing the live image (using a suitable means such as image analysis, video analysis, and so on) and determining the location for placing the stencil. In an embodiment herein, the stencil can be considered as a contour or binary mask.
[0028] The processing unit 103 can determine a figure of merit by overlaying an obtained stencil on the live image being acquired. The figure of merit can signify a match between the live image and the stencil. In an embodiment, the match can be an alignment between the live image and the stencil. The figure of merit can be determined using divergence based on statistical modeling of the live image and the stencil, mutual information, normalized cross-correlation, and so on.
[0029] Based on the figure of merit, the processing unit 103 can provide a feedback to the clinician about parameters that can be quantified from the live images that are being acquired. The parameters include, but not limited to, volume, area, major axis, minor axis, diameter, and so on of at least one object that is visualized in the live images. The processing unit 103 can also suggest methods that can be used by the clinician for quantifying the parameters. The methods can be either manual measurement or automatic quantification using Artificial Intelligence (AI).
[0030] The display unit 104 can display live images acquired using medical imaging modalities such as Ultrasound, ECG, EEG, CT, MRI, and so on. The display unit 104 can display the obtained stencils, stencils overlaid on live images, the figure of merit, the parameters that can be quantified, methods of quantifying the parameters, and so on.
[0031] The application of stencils allows determining the parameters which can be quantified using the live images. This may be used to overcome the requirements of having extensive imaging data required for automatic quantification of parameters.
[0032] FIG. 1 shows exemplary units of the apparatus 100, but it is to be understood that other embodiments are not limited thereon. In other embodiments, the apparatus 100 may include less or more number of units. Further, the labels or names of the units are used only for illustrative purpose and does not limit the scope of the invention. One or more units can be combined together to perform same or substantially similar function in the apparatus 100.
[0033] FIG. 2 is a flowchart 200 depicting a method for performing a stencil guided image acquisition for an optimized clinical application, according to embodiments as disclosed herein. At step 201, the method includes acquiring live images from a first subject through a medical imaging modality. For example, the medical imaging modality can be an ultrasound scan, MRI scan, CT scan, ECG scan, EEG scan, and so on.
[0034] At step 202, the method includes selecting stencils, which can act as a reference to the acquired live images. The stencils can be, but not limited to, at least one of images, graphics, animations, outlines, shapes, and so on.
[0035] In an embodiment herein, the stencil can be selected either manually by a clinician or automatically. The selection of stencils can be based on parameters such as the type of medical imaging modality, information associated with the first subject (such as gender, age, weight, height, and so on), the part of the body of the first subject being scanned, the data/information that is required from the acquired images, specific parameters (volume, area, major axis, minor axis, diameter, and so on, of objects depicted in the live images) to be quantified from the live acquired images, and so on.
[0036] The stencils can be obtained from the first subject and/or a second subject through medical imaging modalities. The stencils can be stored in a data storage location such as a memory, database, file server, the Cloud, and so on. The stencils can also be provided by an authorized person (such as service person, a technician, a clinician, and so on) and stored in a data storage location.
[0037] In an embodiment, the stencils can be images obtained by scanning the organs of the first subject and/or the second subject previously. The stencils can be pre-sized images designed based on information associated with the first subject such as gender, age, weight, height, and so on. The stencils can be drawn by a clinician acquiring the live images by performing medical imaging modalities on the first subject.
[0038] The imaging modality through which the live images and the stencils are obtained can either be same or different. In an example, if the live image is acquired by performing an ultrasound scan, the stencil can be an image obtained by performing an ultrasound scan or a different type of scan.
[0039] In an embodiment, if the age difference between the first subject and the second subject is significant and the gender is different, the method includes adapting the stencils. The adaptation can be performed based on specific features that are to be detected and/or quantified in the live images. The adaptation can be performed by applying relative deformation to the stencils based on the specific parameters that are to be detected and/or quantified from the acquired live images. The stencils can be adapted manually by scaling the stencils. The stencils of different persons can be adapted to a predefined size based on age, gender, observations from the live images, and so on. In an embodiment the stencils can be adapted automatically by the stencil management unit 102, based on parameters such as the source of the stencil, the patient information, observations from the live images, and so on.
[0040] At step 203, the method includes determining a figure of merit between the acquired live images and the selected stencils. The figure of merit can correspond to a degree of match between the acquired live images and the stencils. The figure of merit can be determined by overlaying the stencils on the live images. The stencil can be considered as a contour or binary mask, overlaying the live image.
[0041] In an embodiment, the stencil can be overlaid directly on the live image if the selected stencil is pre-sized designed based on age and gender of the first subject or if the stencil is drawn by the clinician.
[0042] In another embodiment, the overlaying the stencil can be automated, wherein the stencil is overlaid based on the at least one parameter (volume, area, major axis, minor axis, diameter, and so on, of objects depicted in the live images) to be quantified from the live image. In yet another embodiment, the stencil can be overlaid on a desired location on the live image. The embodiments can overlay the stencil automatically by analyzing the live image (using a suitable means such as image analysis, video analysis, and so on) and determining the location for placing the stencil.
[0043] The figure of merit can signify a degree of match between the live images and the stencils. The degree of match can be determined using normalized cross correlation, divergence based on statistical modeling of the live images and the stencils, mutual information, quantitative distance between the live images and the stencils, similarity between the live images and the stencils, and so on.
[0044] In an embodiment, the degree of match can be based on alignment between the live image and the stencil. The figure of merit can be determined using divergence based on statistical modeling of the live image and the stencil, mutual information, normalized cross-correlation, and so on.
[0045] At step 204, the method includes determining the parameters to be quantified from the live images based on the figure of merit. The parameters include, but not limited to, volume, area, major axis, minor axis, diameter, and so on of at least one object that is visualized in the live images. Based on the figure of merit, the embodiments include determining whether the parameters can be quantified manually or using AI.
[0046] The various actions in the flowchart 200 may be performed in the order presented, in a different order, or simultaneously. Further, in some embodiments, some actions listed in FIG. 2 may be omitted.
[0047] FIG. 3a depicts an example selected reference stencil. FIGS. 3b and 3c depict an example variation in figure of merit obtained by overlaying the selected reference stencil on different example live images. The figure of merit, as depicted in FIG. 3b, can indicate that the degree of match between the stencil and the live image (FIG. 3b) is less. The figure of merit, as depicted in FIG. 3c, can indicate that the degree of match between the stencil and the live image (FIG. 3c) is more.
[0048] FIG. 4 depicts a stencil palette and corresponding user action.
[0049] FIGS. 5a-5c depict an example images of an acquired live image, a stencil, and an image with the stencil overlaid on the live image. Consider that the medical imaging modality through which the live image is obtained is an ultrasound scan. As depicted in FIG. 5a, the live image is a representative image of a shoulder of a subject. The live image can be obtained clinical evaluation of rotator cuff injury. The stencil, as depicted in FIG. 5b, can be selected for overlaying on the live image. As depicted in FIG. 5c, the selected stencil is overlaid on the live image, obtained through ultrasound scan, to determine the parameters that can be quantified for diagnostic requirements.
[0050] Embodiments can be used to provide the image acquisition guidance with nominal changes to an existing system. Embodiments can simplify the workflow of image based quantification. Embodiments can be used to ensure that appropriate images are acquired, which are optimal for obtaining targeted parameters for quantification.
[0051] The embodiments disclosed herein can be implemented through at least one software program running on at least one hardware device and performing network management functions to control the network elements. The network elements shown in FIG. 1 include blocks which can be at least one of a hardware device, or a combination of hardware device and software module.
[0052] The embodiments disclosed herein describe methods and systems for performing a stencil guided image acquisition for an optimized clinical application. Therefore, it is understood that the scope of the protection is extended to such a program and in addition to a computer readable means having a message therein, such computer readable storage means contain program code means for implementation of one or more steps of the method, when the program runs on a server or mobile device or any suitable programmable device. The method is implemented in a preferred embodiment through or together with a software program written in e.g. Very high speed integrated circuit Hardware Description Language (VHDL) another programming language, or implemented by one or more VHDL or several software modules being executed on at least one hardware device. The hardware device can be any kind of portable device that can be programmed. The device may also include means which could be e.g. hardware means like e.g. an ASIC, or a combination of hardware and software means, e.g. an ASIC and an FPGA, or at least one microprocessor and at least one memory with software modules located therein. The method embodiments described herein could be implemented partly in hardware and partly in software. Alternatively, the invention may be implemented on different hardware devices, e.g. using a plurality of CPUs.
[0053] The foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the embodiments as described herein.
,CLAIMS:STATEMENT OF CLAIMS
I/We claim:
1. A method for performing a stencil guided image acquisition for an optimized clinical application, the method comprising:
overlaying, by a processing unit (103), a selected stencil on a live image acquired from a first subject through a medical imaging modality;
determining, by the processing unit (103), a figure of merit, wherein the figure of merit corresponds to a degree of match between the live image and the stencil; and
determining, by the processing unit (103), at least one parameter to be quantified from the live image and means for quantifying the at least one parameter based on the figure of merit.
2. The method, as claimed in claim 1, wherein the medical imaging modality is one of an ultrasound scan, a Computed Tomography (CT) scan, an Electroencephalogram (EEG) scan, an Electrocardiogram (ECG) scan, and a Magnetic Resonance Imaging Scanner (MRI) scan.
3. The method, as claimed in claim 1, wherein the stencil is one of image, graphic, animation, outline and shape, wherein the stencil acts as a reference to the live image.
4. The method, as claimed in claim 1, wherein the stencil is obtained by one of: a pre-sized stencil designed based on information associated with the first subject, drawn by a clinician acquiring the live image, and through a medical imaging modality performed on one of the first subject and a second subject.
5. The method, as claimed in claim 4, wherein the stencil is selected based on at least one of: the at least one parameter to be quantified from the live image, type of the medical imaging modality through which the live image is acquired, and type of the medical imaging modality through which the stencil is obtained.
6. The method, as claimed in claim 4, wherein the method further comprises adapting the stencil based on at least one of: the at least one parameter to be extracted, source of the stencil, information associated with the first subject, observation from the live image, variation in one of age and gender between the first subject and the second subject, and manual scaling of the stencil.
7. The method, as claimed in claim 1, wherein the stencil is overlaid on the live image by one of: overlaying the stencil on a desired location on the live image by analyzing the live image, overlaying the stencil based on the at least one parameter to be extracted, and overlaying the stencil directly on the live image if the stencil is one of the pre-sized stencil and drawn by the clinician.
8. The method, as claimed in claim 1, wherein the degree of match is determined by at least one of: normalized cross correlation, divergence based on statistical modeling of the live image and the stencil, mutual information, quantitative distance between the live image and the stencil, and similarity between the live image and the stencil.
9. The method, as claimed in claim 1, wherein the at least one parameter is quantified by one of: manually and artificial intelligence.
10. A method for performing a stencil guided image acquisition for an optimized clinical application, the method comprising:
overlaying, by a processing unit (103), a selected stencil on a live image acquired from a first subject through a medical imaging modality.
11. The method, as claimed in claim 10, wherein the method further comprises determining at least one parameter to be quantified from the live image, based on a degree of match obtained by overlaying the stencil on the live image.
12. The method, as claimed in claim 10, wherein the medical imaging modality is one of an ultrasound scan, a Computed Tomography (CT) scan, an Electroencephalogram (EEG) scan, an Electrocardiogram (ECG) scan, and a Magnetic Resonance Imaging Scanner (MRI) scan.
13. The method, as claimed in claim 10, wherein the stencil is one of image, graphic, animation, outline and shape, wherein the stencil acts as reference to the live image.
14. The method, as claimed in claim 10, wherein the stencil is obtained by one of: a pre-sized stencil designed based on information associated with the first subject, drawn by a clinician acquiring the live image, and through a medical imaging modality performed on one of the first subject and a second subject.
15. The method, as claimed in claim 14, wherein the stencil is selected based on at least one of: the at least one parameter to be quantified from the live image, type of the medical imaging modality through which the live image is acquired, and type of the medical imaging modality through which the stencil is obtained.
16. The method, as claimed in claim 11 and claim 14, wherein the method further comprises adapting the stencil based on at least one of: the at least one parameter to be extracted, source of the stencil, information associated with the first subject, observation from the live image, variation in one of age and gender of the first subject and the second subject, and manual scaling of the stencil.
17. The method, as claimed in claim 10 and claim 11, stencil is overlaid on the live image by one of: overlaying the stencil on a desired location on the live image by analyzing the live image, overlaying the stencil based on the at least one parameter to be extracted, and overlaying the stencil directly on the live image if the stencil is one of the pre-sized stencil and drawn by the clinician.
18. The method, as claimed in claim 11, wherein the degree of match is determined by at least one of: normalized cross correlation, divergence based on statistical modeling of the live image and the stencil, mutual information, quantitative distance between the live image and the stencil, and similarity between the live image and the stencil.
19. An apparatus (100) for performing a stencil guided image acquisition for an optimized clinical application, the apparatus (100) configured to:
overlay, by a processing unit (103), a selected stencil on a live image acquired from a first subject through a medical imaging modality;
determine, by the processing unit (103), a figure of merit, wherein the figure of merit corresponds to a degree of match between the live image and the stencil; and
determine, by the processing unit (103), at least one parameter to be quantified from the live image and means for quantifying the at least one parameter based on the figure of merit.
20. The apparatus (100), as claimed in claim 19, wherein the medical imaging modality is one of an ultrasound scan, a Computed Tomography (CT) scan, an Electroencephalogram (EEG) scan, an Electrocardiogram (ECG) scan, and a Magnetic Resonance Imaging Scanner (MRI) scan.
21. The apparatus (100), as claimed in claim 19, wherein the stencil is one of image, graphic, animation, outline and shape, wherein the stencil acts as a reference to the live image.
22. The apparatus (100), as claimed in claim 19, wherein the stencil is obtained by one of: a pre-sized stencil designed based on information associated with the first subject, drawn by a clinician acquiring the live image, and through a medical imaging modality performed on one of the first subject and a second subject.
23. The apparatus (100), as claimed in claim 22, wherein the stencil is selected based on at least one of: the at least one parameter to be quantified from the live image, type of the medical imaging modality through which the live image is acquired, and type of the medical imaging modality through which the stencil is obtained.
24. The apparatus (100), as claimed in claim 22, wherein the apparatus (100) is further configured to adapt the stencil based on at least one of: the at least one parameter to be extracted, source of the stencil, information associated with the first subject, observation from the live image, variation in one of age and gender between the first subject and the second subject, and manual scaling of the stencil.
25. The apparatus (100), as claimed in claim 19, wherein the stencil is overlaid on the live image by one of: overlaying the stencil on a desired location on the live image by analyzing the live image, overlaying the stencil based on the at least one parameter to be extracted, and overlaying the stencil directly on the live image if the stencil is one of the pre-sized stencil and drawn by the clinician.
26. The apparatus (100), as claimed in claim 19, wherein the degree of match is determined by at least one of: normalized cross correlation, divergence based on statistical modeling of the live image and the stencil, mutual information, quantitative distance between the live image and the stencil, and similarity between the live image and the stencil.
27. The apparatus (100), as claimed in claim 1, wherein the at least one parameter is quantified by one of: manually and artificial intelligence.
28. An apparatus (100) for performing a stencil guided image acquisition for an optimized clinical application, the apparatus (100) configured to:
overlay, by a processing unit (103), a selected stencil on a live image acquired from a first subject through a medical imaging modality.
29. The apparatus (100), as claimed in claim 28, wherein the apparatus (100) is further configured to determine at least one parameter to be quantified from the live image, based on a degree of match obtained by overlaying the stencil on the live image.
30. The apparatus (100), as claimed in claim 28, wherein the medical imaging modality is one of an ultrasound scan, a Computed Tomography (CT) scan, an Electroencephalogram (EEG) scan, an Electrocardiogram (ECG) scan, and a Magnetic Resonance Imaging Scanner (MRI) scan.
31. The apparatus (100), as claimed in claim 28, wherein the stencil is one of image, graphic, animation, outline and shape, wherein the stencil acts as reference to the live image.
32. The apparatus (100), as claimed in claim 28, wherein the stencil is obtained by one of: a pre-sized stencil designed based on information associated with the first subject, drawn by a clinician acquiring the live image, and through a medical imaging modality performed on one of the first subject and a second subject.
33. The apparatus (100), as claimed in claim 32, wherein the stencil is selected based on at least one of: the at least one parameter to be quantified from the live image, type of the medical imaging modality through which the live image is acquired, and type of the medical imaging modality through which the stencil is obtained.
34. The apparatus (100), as claimed in claim 29 and claim 32, wherein the apparatus (100) is further configured to adapt the stencil based on at least one of: the at least one parameter to be extracted, source of the stencil, information associated with the first subject, observation from the live image, variation in one of age and gender of the first subject and the second subject, and manual scaling of the stencil.
35. The apparatus (100), as claimed in claim 28 and claim 29, stencil is overlaid on the live image by one of: overlaying the stencil on a desired location on the live image by analyzing the live image, overlaying the stencil based on the at least one parameter to be extracted, and overlaying the stencil directly on the live image if the stencil is one of the pre-sized stencil and drawn by the clinician.

36. The apparatus (100), as claimed in claim 29, wherein the degree of match is determined by at least one of: normalized cross correlation, divergence based on statistical modeling of the live image and the stencil, mutual information, quantitative distance between the live image and the stencil, and similarity between the live image and the stencil.