Detection of upscale-crop and partial manipulation in surveillance video based on sensor pattern noise.

In many court cases, surveillance videos are used as significant court evidence. As these surveillance videos can easily be forged, it may cause serious social issues, such as convicting an innocent person. Nevertheless, there is little research being done on forgery of surveillance videos. This paper proposes a forensic technique to detect forgeries of surveillance video based on sensor pattern noise (SPN). We exploit the scaling invariance of the minimum average correlation energy Mellin radial harmonic (MACE-MRH) correlation filter to reliably unveil traces of upscaling in videos. By excluding the high-frequency components of the investigated video and adaptively choosing the size of the local search window, the proposed method effectively localizes partially manipulated regions. Empirical evidence from a large database of test videos, including RGB (Red, Green, Blue)/infrared video, dynamic-/static-scene video and compressed video, indicates the superior performance of the proposed method.

Surveillance of the infection prevention and control practices of healthcare workers by an infection control surveillance-working group and a team of infection control coordinators during the COVID-19 pandemic.

BACKGROUND: During the ongoing coronavirus disease (COVID-19) pandemic, hospitals have strengthened their guidelines on infection prevention and control (IPC), and a rigorous adherence to these guidelines is crucial. An infection control surveillance-working group (ICS-WG) and infection control coordinators (ICCs) team were created to monitor the IPC practices of the healthcare workers (HCWs) in a regional hospital in Korea. This study analyzed the surveillance results and aimed to identify what IPC practices needed improvement. METHODS: During phase 1 (March to April 2020), the ICS-WG performed random audits, recorded incidences of improper IPC practices, and provided advice to the violators. During phase 2 (April to July), the ICCs inspected the hospital units and proposed practical ideas about IPC. The surveillance and proposals targeted the following practices: patient screening, usage of personal protective equipment (PPE), hand and respiratory hygiene, equipment reprocessing, environmental cleaning, management of medical waste, and social distancing. RESULTS: In phase 1, of the 127 violations observed, most (32.3%) corresponded to hand and respiratory hygiene. In phase 2, the highest proportion of violation per category was observed in the management of medical waste (37.8%); among these, a higher proportion of violation (71.4%) was observed in the collection of medical waste. Of the 106 proposals made by the ICCs, the most addressed practice was patient screening (28.3%). No case of nosocomial infection was reported during the study period. CONCLUSION: Adherence to proper hand and respiratory hygiene was inadequate at the early stage of the COVID-19 pandemic. The results indicate that more attention and further training are needed for the management of medical waste, particularly medical waste collection, and that continuous upgrading of the strategies for patient screening is essential. These results will be useful in helping other healthcare facilities to establish their IPC strategies.

Estimation of color modification in digital images by CFA pattern change.

Extensive studies have been carried out for detecting image forgery such as copy-move, re-sampling, blurring, and contrast enhancement. Although color modification is a common forgery technique, there is no reported forensic method for detecting this type of manipulation. In this paper, we propose a novel algorithm for estimating color modification in images acquired from digital cameras when the images are modified. Most commercial digital cameras are equipped with a color filter array (CFA) for acquiring the color information of each pixel. As a result, the images acquired from such digital cameras include a trace from the CFA pattern. This pattern is composed of the basic red green blue (RGB) colors, and it is changed when color modification is carried out on the image. We designed an advanced intermediate value counting method for measuring the change in the CFA pattern and estimating the extent of color modification. The proposed method is verified experimentally by using 10,366 test images. The results confirmed the ability of the proposed method to estimate color modification with high accuracy.

Improved left ventricular mass quantification with partial voxel interpolation: in vivo and necropsy validation of a novel cardiac MRI segmentation algorithm.

BACKGROUND: Cardiac magnetic resonance (CMR) typically quantifies LV mass (LVM) by means of manual planimetry (MP), but this approach is time-consuming and does not account for partial voxel components--myocardium admixed with blood in a single voxel. Automated segmentation (AS) can account for partial voxels, but this has not been used for LVM quantification. This study used automated CMR segmentation to test the influence of partial voxels on quantification of LVM. METHODS AND RESULTS: LVM was quantified by AS and MP in 126 consecutive patients and 10 laboratory animals undergoing CMR. AS yielded both partial voxel (AS(PV)) and full voxel (AS(FV)) measurements. Methods were independently compared with LVM quantified on echocardiography (echo) and an ex vivo standard of LVM at necropsy. AS quantified LVM in all patients, yielding a 12-fold decrease in processing time versus MP (0:21±0:04 versus 4:18±1:02 minutes; P<0.001). AS(FV) mass (136±35 g) was slightly lower than MP (139±35; Δ=3±9 g, P<0.001). Both methods yielded similar proportions of patients with LV remodeling (P=0.73) and hypertrophy (P=1.00). Regarding partial voxel segmentation, AS(PV) yielded higher LVM (159±38 g) than MP (Δ=20±10 g) and AS(FV) (Δ=23±6 g, both P<0.001), corresponding to relative increases of 14% and 17%. In multivariable analysis, magnitude of difference between AS(PV) and AS(FV) correlated with larger voxel size (partial r=0.37, P<0.001) even after controlling for LV chamber volume (r=0.28, P=0.002) and total LVM (r=0.19, P=0.03). Among patients, AS(PV) yielded better agreement with echo (Δ=20±25 g) than did AS(FV) (Δ=43±24 g) or MP (Δ=40±22 g, both P<0.001). Among laboratory animals, AS(PV) and ex vivo results were similar (Δ=1±3 g, P=0.3), whereas AS(FV) (6±3 g, P<0.001) and MP (4±5 g, P=0.02) yielded small but significant differences with LVM at necropsy. CONCLUSIONS: Automated segmentation of myocardial partial voxels yields a 14-17% increase in LVM versus full voxel segmentation, with increased differences correlated with lower spatial resolution. Partial voxel segmentation yields improved CMR agreement with echo and necropsy-verified LVM.

Automatic left ventricle segmentation using iterative thresholding and an active contour model with adaptation on short-axis cardiac MRI.

An automatic left ventricle (LV) segmentation algorithm is presented for quantification of cardiac output and myocardial mass in clinical practice. The LV endocardium is first segmented using region growth with iterative thresholding by detecting the effusion into the surrounding myocardium and tissues. Then the epicardium is extracted using the active contour model guided by the endocardial border and the myocardial signal information estimated by iterative thresholding. This iterative thresholding and active contour model with adaptation (ITHACA) algorithm was compared to manual tracing used in clinical practice and the commercial MASS Analysis software (General Electric) in 38 patients, with Institutional Review Board (IRB) approval. The ITHACA algorithm provided substantial improvement over the MASS software in defining myocardial borders. The ITHACA algorithm agreed well with manual tracing with a mean difference of blood volume and myocardial mass being 2.9 +/- 6.2 mL (mean +/- standard deviation) and -0.9 +/- 16.5 g, respectively. The difference was smaller than the difference between manual tracing and the MASS software (approximately -20.0 +/- 6.9 mL and -1.0 +/- 20.2 g, respectively). These experimental results support that the proposed ITHACA segmentation is accurate and useful for clinical practice.

Left ventricle segmentation using graph searching on intensity and gradient and a priori knowledge (lvGIGA) for short-axis cardiac magnetic resonance imaging.

PURPOSE: To develop and evaluate an automated left ventricle (LV) segmentation algorithm using Graph searching based on Intensity and Gradient information and A priori knowledge (lvGIGA). MATERIALS AND METHODS: The lvGIGA algorithm was implemented with coil sensitivity correction and polar coordinate transformation. Graph searching and expansion were applied for extracting myocardial endocardial and epicardial borders. LV blood and myocardium intensities were estimated for accurate partial volume calculation of blood volume and myocardial mass. Cardiac cine SSFP images were acquired from 38 patients. The lvGIGA algorithm was used to measure blood volume, myocardial mass, and ejection fraction, and compared with clinical manual tracing and the commercial MASS software. RESULTS: The success rate for segmenting both endocardial and epicardial borders was 95.6% slices for lvGIGA and 37.8% for MASS (excluding basal slices that required manual enclosure of ventricle blood). The lvGIGA segmentation result agreed well with manual tracing, within -2.9 +/- 4.4 mL, 2.1 +/- 2.2%, and -9.6 +/- 13.0 g, for blood volume, ejection fraction, and myocardial mass, respectively. CONCLUSION: The lvGIGA algorithm substantially improves the robustness of LV segmentation automation over the commercial MASS software, agrees well with clinical manual tracing, and may be a useful tool for clinical practice.