Vital signs such as pulse rate and breathing rate are currently measured using contact probes. But, non-contact methods for measuring vital signs are desirable both in hospital settings (e.g. in NICU) and for ubiquitous in-situ health tracking (e.g. on mobile phone and computers with webcams). Recently, camera-based non-contact vital sign monitoring have been shown to be feasible. However, camera-based vital sign monitoring is challenging for people with darker skin tone, under low lighting conditions, and/or during movement of an individual in front of the camera. In this paper, we propose distancePPG, a new camera-based vital sign estimation algorithm which addresses these challenges. DistancePPG proposes a new method of combining skin-color change signals from different tracked regions of the face using a weighted average, where the weights depend on the blood perfusion and incident light intensity in the region, to improve the signal-to-noise ratio (SNR) of camera-based estimate. One of our key contributions is a new automatic method for determining the weights based only on the video recording of the subject. The gains in SNR of camera-based PPG estimated using distancePPG translate into reduction of the error in vital sign estimation, and thus expand the scope of camera-based vital sign monitoring to potentially challenging scenarios. Further, a dataset will be released, comprising of synchronized video recordings of face and pulse oximeter based ground truth recordings from the earlobe for people with different skin tones, under different lighting conditions and for various motion scenarios.

Blood carries oxygen and nutrients to the trillions of cells in our body to sustain vital life processes. Lack of blood perfusion can cause irreversible cell damage. therefore, blood perfusion measurement has widespread clinical applications. in this paper, we develop pulsecam -a new camera-based, motionrobust, and highly sensitive blood perfusion imaging modality with 1 mm spatial resolution and 1 frame-per-second temporal resolution. Existing camera-only blood perfusion imaging modality suffers from two core challenges: (i) motion artifact, and (ii) small signal recovery in the presence of large surface reflection and measurement noise. PulseCam addresses these challenges by robustly combining the video recording from the camera with a pulse waveform measured using a conventional pulse oximeter to obtain reliable blood perfusion maps in the presence of motion artifacts and outliers in the video recordings. For video stabilization, we adopt a novel brightness-invariant optical flow algorithm that helps us reduce error in blood perfusion estimate below 10% in different motion scenarios compared to 20-30% error when using current approaches. PulseCam can detect subtle changes in blood perfusion below the skin with at least two times better sensitivity, three times better response time, and is significantly cheaper compared to infrared thermography. PulseCam can also detect venous or partial blood flow occlusion that is difficult to identify using existing modalities such as the perfusion index measured using a pulse oximeter. With the help of a pilot clinical study, we also demonstrate that pulsecam is robust and reliable in an operationally challenging surgery room setting. We anticipate that pulsecam will be used both at the bedside as well as a point-of-care blood perfusion imaging device to visualize and analyze blood perfusion in an easy-to-use and cost-effective manner. open Scientific RepoRtS | (2020) 10:4825 | https://doi.comparing pulsecam with an infrared thermal camera. To compare PulseCam and infrared thermography, we perform incremental vascular occlusion experiment on 9 participants (5 male and 4 female) of varying skin tones (Fitzpatrick Scale I to VI) and record their palm videos using both a color camera as well as Scientific RepoRtS | (2020) 10:4825 | https://doi.

In this paper, we study the reversible electroporation process on the normal and cancerous cervical cell.The 2D contour of the cervical cells is extracted using image processing techniques from the Pap smear image. The conductivity change in the cancer cell model has been used to differentiate the effects of the high-frequency electric field on normal and cancerous cells. The cells modulate themselves when this high-frequency pulse is applied based on the Debye relaxation relation. In order to computationally visualize the effects of the electroporation on the cell membrane Smoluchowski equation calculates the number of pores generated and Maxwell equations are used to determine the Transmembrane potential generated on the membrane of the cervical cell. The results produced demonstrates that this mathematical model perfectly describes the numerical tool to study the normal cells and cancerous cells under the electric field. The electric field is provided with the help of a realistic pulse generator which is designed on the principle of Marx circuit and avalanche transistor-based operations to produce a Gaussian pulse. The paper here use strength duration curve to differentiate the electric field and time in nanoseconds required to electroporate normal and cancerous cells.

Background: This paper attempts to pinpoint different techniques for Pectoral Muscle (PM) segmentation, Microcalcification (MC) detection and classification in digital mammograms. The segmentation of PM and detection of MC and its classification are mostly based on image processing and data mining techniques. </P><P> Discussion: The review centered on major techniques in image processing and data mining that is employed for PM segmentation, MC detection and classification in digital mammograms. Breast cancer is one of the significant causes of death among women aged above 40. Mammography is considered the most successful means for prompt and timely detection of breast cancers. One notable visual indication of the malignant growth is the appearance of Masses, Architectural Distortions, and Microcalcification Clusters (MCCs). There are some disadvantages and hurdles for mankind viewers, and it is hard for radiologists to supply both precise and steady assessment for a large number of mammograms created in extensive screening. Computer Aided Detection has been employed to help radiologists in detecting MC and MCCs. The automatic recognition of malignant MCCs could be very helpful for diagnostic purpose. In this paper, we summarize the methods of automatic detection and classification of MCs in digitized mammograms. Pectoral muscle segmentation techniques are also summarized. Conclusion: The techniques used for segmentation of PM, MC detection and classification in a digitized mammogram are reviewed.

Measuring blood perfusion is important in medical care as an indicator of injury and disease. However, currently available devices to measure blood perfusion like laser Doppler flowmetry are bulky, expensive, and cumbersome to use. An alternative low-cost and portable camera-based blood perfusion measurement system has recently been proposed, but such camera-only system produces noisy low-resolution blood perfusion maps. In this paper, we propose a new multi-sensor modality, named PulseCam, for measuring blood perfusion by combining a traditional pulse oximeter with a video camera in a unique way to provide low noise and high-resolution blood perfusion maps. Our proposed multi-sensor modality improves per pixel signal to noise ratio of measured perfusion map by up to 3 dB and improves the spatial resolution by 2 - 3 times compared to best known camera-only methods. Blood perfusion measured in the palm using our PulseCam setup during a post-occlusive reactive hyperemia (PORH) test replicates standard PORH response curve measured using laser Doppler flowmetry device but with much lower cost and a portable setup making it suitable for further development as a clinical device.