Ultrasonographic Tissue Perfusion in Peri-implant Health and Disease

Color flow ultrasonography has played a crucial role in medicine for its ability to assess dynamic tissue perfusion and blood flow variations as an indicator of a pathologic condition. While this feature of ultrasound is routinely employed in various medical fields, its intraoral application for the assessment of tissue perfusion at diseased versus healthy dental implants has never been explored. We tested the hypothesis that quantified tissue perfusion of power Doppler ultrasonography correlates with the clinically assessed inflammation of dental implants. Specifically, we designed a discordant-matched case-control study in which patients with nonadjacent dental implants with different clinical diagnoses (healthy, peri-implant mucositis, or peri-implantitis) were scanned and analyzed with real-time ultrasonography. Forty-two posterior implants in 21 patients were included. Ultrasound scans were obtained at the implant regions of midbuccal, mesial/distal (averaged as interproximal), and transverse to compute the velocity- and power-weighted color pixel density from color velocity (CV) and color power (CP), respectively. Linear mixed effect models were then used to assess the relationship between the clinical diagnoses and ultrasound CV and CP. Overall, the results strongly suggested that ultrasound’s quantified CV and CP directly correlate with the clinical diagnosis of dental implants at health, peri-implant mucositis, and peri-implantitis. This study showed for the first time that ultrasound color flow can be applicable in the diagnosis of peri-implant disease and can act as a valuable tool for evaluating the degree of clinical inflammation at implant sites.

Representing hierarchies of visual regard in eye-tracking analysis

In the following study, the author developed a method for representing data from eye-tracking recordings. The study proposed a form of graphical analysis that illustrates hierarchical densities of visual regard without obscuring the original pictorial stimulus. Across three different case studies, subjects’ fixation patterns were used to propagate Voronoi generating points. Integrating both fixation locations and their respective dwell times, randomized Gaussian distribution provided a technique to augment Voronoi generating seeds and enhance graphical resolution. Color pixel values were then used to fill in resultant Voronoi cells, in relation to color values provided by the original stimulus. The study revealed a form of analysis that allowed for effective differentiation of viewing behaviors between different subjects, in which emphasis was placed on a subject's attentional distribution rather than on graphic icons.

Color-complexity enabled exhaustive color-dots identification and spatial patterns testing in images

Our computational developments and analyses on experimental images are designed to evaluate the effectiveness of chemical spraying via unmanned aerial vehicle (UAV). Our evaluations are in accord with the two perspectives of color-complexity: color variety within a color system and color distributional geometry on an image. First, by working within RGB and HSV color systems, we develop a new color-identification algorithm relying on highly associative relations among three color-coordinates to lead us to exhaustively identify all targeted color-pixels. A color-dot is then identified as one isolated network of connected color-pixel. All identified color-dots vary in shapes and sizes within each image. Such a pixel-based computing algorithm is shown robustly and efficiently accommodating heterogeneity due to shaded regions and lighting conditions. Secondly, all color-dots with varying sizes are categorized into three categories. Since the number of small color-dot is rather large, we spatially divide the entire image into a 2D lattice of rectangular. As such, each rectangle becomes a collective of color-dots of various sizes and is classified with respect to its color-dots intensity. We progressively construct a series of minimum spanning trees (MST) as multiscale 2D distributional spatial geometries in a decreasing-intensity fashion. We extract the distributions of distances among connected rectangle-nodes in the observed MST and simulated MSTs generated under the spatial uniformness assumption. We devise a new algorithm for testing 2D spatial uniformness based on a Hierarchical clustering tree upon all involving MSTs. This new tree-based p-value evaluation has the capacity to become exact.

Retinal Image Processing on Early Glaucoma Detection

Glaucoma is a habitual eye disorder which harms eye’s second cranial nerve. There are millions of second cranial nerves. The main function of these types of nerves is to sending captured visual information from retina to the brain. The escalate pressure in the human eye leads to Glaucoma. This heavy pressure is known as intraocular pressure. This heavy pressure leads to damage eye's optic nerve head and retina continuously further it tends to vision loss. In this paper there are two datasets including both normal person and affected person's eye color images. The principal aim of this project is to compare the color of the eye with these two datasets. A special camera which is attached to less power microscope is called fundus camera or retinal camera. The images captured by this type of fundus camera is called fundus picture[1]. It is a high dimensional laser image. MATLAB software tool is used to fulfill the feature extraction of these fundus images. A color pixel in the affected area of the person is measured to check whether a person is Glaucomatous or not. If the final result is positive then it is Glaucoma.

DETEKSI DINI KEBAKARAN HUTAN DAN LAHAN MEMANFAATKAN EKSTRAKSI EXIF PADA INFORMASI GAMBAR BERBASIS PENGOLAHAN CITRA

Forest fire detection system is one of important tools in preventing and mitigating forest and land fires. In Indonesia, the detection of forest and land fires relies on hotspot information captured from satellites. However, the location obtained by the satellite has a horizontal error of 2 km from the ground check data. Therefore, these information are less relevant to the actual location. In this research, an android app is proposed to extract Exchangeable Image Format (EXIF) photo metadata. The metadata has image information such as latitude and longitude, to obtain the location of forest fires reported by the application user. In addition, this research implemented one of the image processing methods to classify fire and smoke in images of fires. Color filtering method is used based on the color space of Red Green Blue (RGB), Hue Saturation Value (HSV) and YCbCr. This classification process aims to ease the burden on the admin in confirming user reports. The results of the fire and smoke classification process are described using a confusion matrix. This matrix produces an accuracy rate of 75%, a precision of 80% and a recall of 80% for a fire classification and an accuracy of 70%, a precision of 92% and a recall of 87% for smoke classification. There are 25% and 30% of misclassified data of fire and smoke. This is because the color filtering method classifies each color pixel from the image, therefore many pixels that are not classified as fire or smoke images are classified because there are other objects that have a range of colors to classify fire and smoke

Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in this study. The method consists of three stages. The first stage is to generate N colors based on 3D histogram computation, the second is to obtain the initial palette by selecting K colors from the N colors based on an artificial bee colony algorithm, and the third is to obtain the quantized images using the accelerated K-means algorithm. In order to reduce the computation time, the sampling process is employed. The closest color in the palette for each sampled color pixel in the color image is efficiently determined by the mean-distance-ordered partial codebook search algorithm. The experimental results show that the proposed method can generate high-quality quantized images with less time consumption.