Assessment of myocardial function by speckle-tracking echocardiography

Myocardial deformation or strain is the universal property of contracting cardiac muscle. Deformation is defined in physics as relative change of length (and is therefore unitless and usually given as percentage) and in cardiac imaging it is thus algebraically negative for shortening or positive for thickening. There are several definitions of strain—Lagrangian strain refers to a fixed baseline distance and Eulerian (or natural) strain—to a dynamically changing reference length, representing a time integral of strain rate (which can be obtained by tissue Doppler). Measurements of strains are usually obtained by greyscale image quantification modality—speckle-tracking echocardiography (STE) which analyses myocardial motion by tracking and matching naturally occurring markers of myocardial texture, described as speckles. Echocardiographic speckles represent interference pattern of subtle myocardial scatters and can be followed from frame to frame by dedicated software to define the displacement of the myocardium within the interval between consecutive frames (inverse of frame rate).

Pseudo-colour with K-means Clustering Algorithm for Acute Ischemic Stroke Lesion Segmentation in Brain MRI

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.

An Evaluation of Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion and Manual Colour Grading

In the paper "Colour-to-Greyscale Image Conversion by Linear Anisotropic Diffusion of Perceptual Colour Metrics", Farup et al. presented an algorithm to convert colour images to greyscale. The algorithm produces greyscale reproductions that preserve detail derived from local colour differences in the original colour image. Such detail is extracted by using linear anisotropic diffusion to build a greyscale reproduction from a gradient of the original image that is in turn calculated using Riemannised colour metrics. The purpose of the current paper is to re-evaluate one of the psychometric experiments for these two methods (CIELAB L* and anisotropic Δ99) by using a flipping method to compare their resulting images instead of the side by side method used in the original evaluation. In addition to testing the two selected algorithms, a third greyscale reproduction was manually created (colour graded) using a colour correction software commonly used to process motion pictures. Results of the psychometric experiment found that when comparing images using the flipping method, there was a statistically significant difference between the anisotropic Δ99 and CIELAB L* conversions that favored the anisotropic method. The comparison between Δ99 conversion and the manually colour graded image also showed a statistically significant difference between them, in this case favoring the colour graded version.

On the Information Advantage of Sidescan Sonar Three-Frequency Colour over Greyscale Imagery

A prototype three-frequency (114, 256, and 410 kHz) colour sidescan sonar system, built by Kongsberg Underwater Mapping Ltd. (Great Yarmouth, UK), was previously described, and preliminary results presented, in Tamsett, McIlvenny, and Watts. The prototype system has subsequently been modified, and in 2017, new data were acquired in a resurvey of the Inner Sound of the Pentland Firth, North Scotland. An image texture characterisation and image classification exercise demonstrates considerably greater discrimination between different seabed classes in a three-frequency colour sonar image of the seabed, than in a multi-frequency colour image reduced to greyscale display, or in a single-frequency greyscale image, with readily twice the number of classes of seabed discriminated between, in the colour image. The information advantage of colour acoustic imagery over greyscale acoustic imagery is analogous to the information advantage of colour television images over black-and-white television images. A three-frequency colour sonar image contains a theoretical maximum of a factor of 3 times the information in a corresponding greyscale image, for independent seabed responses at the three frequencies. Estimates of the average information per pixel (information entropy) in the colour image, and in corresponding greyscale images, reveal an actual information advantage of colour sonar imagery over greyscale, to be in practice approximately a factor of 2.5, empirically confirming the greater information based utility of three-frequency colour sonar over greyscale sonar. Reference: Tamsett, D.; McIlvenny, J.; Watts, A. J. Mar. Sci. Eng. 2016, 4(26).

Density Based Traffic Controller with Emergency Override using CCTV Camera

The aim of this project is to provide alternative solution for the traffic signal system in clearing high density traffic jam. Now a day’s more number of vehicles is coming on to the road creating more traffic congestion at any junction. The traffic congestion is a severe problem when there arises high density at a particular junction. Especially when there is an emergency like ambulance, fire brigade stuck in the traffic they require priority to go first. In such cases it is necessary to override the normal signal timings automatically. To overcome this problem, this project uses CCTV cameras on each side of junction. It assigns longer green light with the help of the micro controller whenever it sensing the heavy density and whenever it finds emergency vehicles stuck in traffic like ambulance, fire brigade etc they require priority to go first. In this manner it overrides the standard signal timings there by it saves the waiting time of the vehicular. This project uses micro controller interfacing with CCTV aligned in the sight configuration across the load for detecting the density. Once the image is captured from the CCTV footage, it is converted into greyscale image. The greyscale image is passed through the median filter in order to reduce the noise present in it. Further Canny edge detection finds the intensity gradients of the images by suppressing all the other edges that weak and not connected to the strong edges. Then based on the canny image the density is calculated and turns the green light on at the heavy density road side of junction