Separate modal analysis for tumor detection with a digital image elasto tomography (DIET) breast cancer screening system

Kashif, Amer Sohail; Lotz, Thomas; Heeren, Adrianus M.W.; Chase, J. Geoffrey

doi:10.1118/1.4826168

Cited by 20 publications

(18 citation statements)

References 30 publications

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“…Two sample t test was used to compare both groups, where null hypothesis was that both groups were from same distribution with unequal variance and equal means that shows area without inclusion. While if first group was different in normal distribution with lesser mean value shows region with inclusion [10].…”

Section: Applying Modal Analysismentioning

confidence: 90%

“…Applying randomly fiducial markers left some of the unmarked regions which cause non-availability of motion data in that region. This creates blank or empty segments and effects accuracy of system [10]. This paper introduces Scale Invariant Feature Transform (SIFT) algorithm to extract keypoints on unmarked surface of breast.…”

Section: A the Diet Systemmentioning

confidence: 99%

“…Elevation angle is denoted by with reference of chest wall as 0º and 90º is at bust point. Whereas is defined as rotational angle with top of breast is at reference of 0º, located at 12 o'clock when observed from front [10]. Evaluate bode plot of frequency response with respect to sinusoidal actuations of actuator for each segment.…”

Section: Applying Modal Analysismentioning

confidence: 99%

“…Statistical analysis was implemented with 18 segments in direction and 7 segments in direction. Regions with statistical difference in natural frequency as compared to other regions show the presence of inclusion, while healthy phantom show no statistical difference in natural frequency [13]. To analyze and identify the significant change, statistical hypothesis testing and two sample t-test was applied.…”

Section: Applying Modal Analysismentioning

confidence: 99%

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Separate Modal Analysis using Scale Invariant Feature Transform (SIFT) with Digital Image Elasto Tomography (DIET) for breast cancer screening test

Hussain

Rehman

Nazir

et al. 2015

2015 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

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Digital Image Elasto Tomography (DIET) is noninvasive approach for detection and screening test of breast cancer at an early stage. In DIET mechanical vibrations are applied on the surface of breast after applying fiducial markers and images are taken to capture surface motion using five digital cameras. Applying Scale Invariant Feature Transform (SIFT) on the unmarked surface of the breast to produce reference points for tracking surface motion. Using Separate Modal Analysis frequency and amplitude response of oscillations are evaluated using markers. SIFT features are used to detect change in stiffness which refers to presence and/or location of tumor. This increases motion data and reduces noise for detection of cancerous tumours.

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Section: Applying Modal Analysismentioning

confidence: 90%

Section: A the Diet Systemmentioning

confidence: 99%

Section: Applying Modal Analysismentioning

confidence: 99%

Section: Applying Modal Analysismentioning

confidence: 99%

See 2 more Smart Citations

Separate Modal Analysis using Scale Invariant Feature Transform (SIFT) with Digital Image Elasto Tomography (DIET) for breast cancer screening test

Hussain

Rehman

Nazir

et al. 2015

2015 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS)

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show abstract

“…Umut proposed a quantification method for percussion [16], vibrating the livers of animals with an impulse hammer to compare tissue properties according to their frequency responses. Kashif [17] analyzed the vibrated breast images to detect changes in stiffness. They reported that with respect to frequency, elastic moduli (storage elastic modulus, loss modulus) had different mechanical properties between normal and diseased tissues.…”

Section: Related Workmentioning

confidence: 99%

Enhanced frequency analysis on a vibrated tumor with a compression cylinder

et al. 2019

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Breast cancer diagnosis has been mostly accomplished through imaging. These methods have great advantages in being able to detect the presence and location of breast cancer. However, it is difficult to distinguish between a benign and malignant tumor located in a deep position because both tumor types look similar. In this paper, tissue including the tumor from skin was vibrated using a compression cylinder, to analyze the frequency difference for distinguishing tissue type. Before distinguishing between a benign and malignant tumor, it is necessary to validate that the difference between normal tissue and tumor can be distinguished. The objective of the study is to validate the feasibility to emphasize the frequency differences in a 10.0 mm or greater deep tumors during vibration by pushing a cylinder towards the deep tumor. A phantom model and finite element analysis model were constructed to simulate the breast. In the experiment, air was injected into the phantom and the displacement was measured. The frequency response for distinction of tissue types was analyzed and it was found that the displacement difference rate was over 50% at a frequency of 130 Hz when the cylinder was pushed into the sample as opposed to when not pushed in. Changes in displacement were measured according to the distance between the tumor and vibration point using finite element analysis. When the measurement and vibration points were on the center of the tumor, the difference in the resonance point was at its largest (5.5 Hz). Results show that the position of a tumor could be easily and rapidly detected by vibrations from a cylinder pushed into the diagnostic site. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Modeling viscous damping in actuated breast tissue to provide diagnostic insight for breast cancer: A proof‐of‐concept analysis

et al. 2021

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Purpose This study develops a viscous damping model (VDM) based on Rayleigh Damping (RD) with potential use in low cost, non‐invasive breast cancer diagnostics using Digital Image Elasto Tomography (DIET). Methods A clinical trial involving 13 subjects, each with a tumor in one breast, resulted in 13 cancerous and 13 healthy breasts. Displacement data following actuator induced steady state vibration in the breast tissue were captured using the DIET system. Over 14 000 reference points on the breast surface were split into four segments and viscous damping constant calculated for each reference point. The VDM was fit to median‐filtered data for each breast segment and VDM coefficients compared within each breast. One model coefficient, relating to stiffness, was hypothesized to differ in breast segments containing a tumor. Comparison of “b” coefficients in different breast segments using percentage tolerances provided an unbiased, generalizable diagnostic method. Bootstrapping with replacement was used to upsample the data and create smooth receiver operator characteristic (ROC) curves. A total of 12 breast segmentation configurations were used to demonstrate the robustness of the method. Results Fitting the VDM to median‐filtered data gave consistent results for one VDM coefficient (“a”) across all breasts. The second VDM coefficient (“b”) showed diagnostic potential with breast segments having consistent coefficients in healthy breasts. In cancerous breasts “b” coefficients were found to be statistically different in segments containing and adjacent to the tumor compared with the segment furthest from the tumor with p < 0.02 using the Student t‐Test. Large discrepancies in “b” coefficients were found to be indicative of a tumor with a 14.5% tolerance resulting in sensitivity and specificity of 76.9%. The optimal breast configuration resulted in an area under the ROC curve (AUC) of 0.81 with sensitivity and specificity at 77% and 72%, respectively. Conclusion This VDM method enables a computationally simple diagnostic technique using DIET for comfortable breast screening for women of all ages. Regular screening potential allows for tolerance alteration based on age, prior subject‐specific results, and other risk factors to manage false positives, reducing psychological harm while optimizing early detection for successful treatment and decreased mortality.

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Separate modal analysis for tumor detection with a digital image elasto tomography (DIET) breast cancer screening system

Cited by 20 publications

References 30 publications

Separate Modal Analysis using Scale Invariant Feature Transform (SIFT) with Digital Image Elasto Tomography (DIET) for breast cancer screening test

Separate Modal Analysis using Scale Invariant Feature Transform (SIFT) with Digital Image Elasto Tomography (DIET) for breast cancer screening test

Enhanced frequency analysis on a vibrated tumor with a compression cylinder

Modeling viscous damping in actuated breast tissue to provide diagnostic insight for breast cancer: A proof‐of‐concept analysis

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