A Study on Segmentation of Leukocyte Image With Shannon's Entropy

2019 ◽  
pp. 1-27 ◽  
Author(s):  
N. Sri Madhava Raja ◽  
S. Arunmozhi ◽  
Hong Lin ◽  
Nilanjan Dey ◽  
V. Rajinikanth
Keyword(s):  
Blood Cell ◽  
Digital Images ◽  
Ground Truth ◽  
Cuckoo Search ◽  
Peripheral Blood Cell ◽  
Shannon’S Entropy ◽  
Post Processing ◽  
Shannon's Entropy ◽  
Statistical Measures ◽  
Ground Truth Image

In recent years, a considerable number of approaches have been proposed by the researchers to evaluate infectious diseases by examining the digital images of peripheral blood cell (PBC) recorded using microscopes. In this chapter, a semi-automated approach is proposed by integrating the Shannon's entropy (SE) thresholding and DRLS-based segmentation procedure to extract the stained blood cell from digital PBC pictures. This work implements a two-step practice with cuckoo search (CS) and SE-based pre-processing and DRLS-based post-processing procedure to examine the PBC pictures. During the experimentation, the PBC pictures are adopted from the database leukocyte images for segmentation and classification (LISC). The proposed approach is implemented by considering the RGB scale and gray scale version of the PBC pictures, and the performance of the proposed approach is confirmed by computing the picture similarity and statistical measures computed with the extracted stained blood cell with the ground truth image.

scholarly journals Segmentation and Evaluation of COVID-19 Lesion from CT scan Slices - A Study with Kapur/Otsu Function and Cuckoo Search Algorithm

2020 ◽  
Author(s):  
Suresh Chandra Satapathy ◽  
D. Jude Hemanth ◽  
Seifedine Kadry ◽  
Gunasekaran Manogaran ◽  
Naeem M S Hannon ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Processing System ◽  
Ground Truth ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Ground Truth Image ◽  
Chest X Ray ◽  
Infection Disease ◽  
Global Threat ◽  
Tomography Scan

Abstract Infection/disease in lung is one of the acute illnesses in humans. Pneumonia is one of the major lung diseases and each year; the death rate due to the untreated pneumonia is on rise globally. From December 2019; the pneumonia caused by the Coronavirus Disease (COVID-19) has emerged as a global threat due to its rapidity. The clinical level assessment of the COVID-19 is normally performed with the Computed-Tomography scan Slice (CTS) or the Chest X-ray. This research aims to propose an image processing system to examine the COVID-19 infection in CTS. This work implements Cuckoo-Search-Algorithm (CSA) monitored Kapur/Otsu image thresholding and a chosen image segmentation procedure to extract the pneumonia infection. After extracting the COVID-19 infection from the CTS, a relative assessment is then executed with the Ground-Truth-Image (GTI) offered by a radiologist and the essential performance measures are then computed to confirm the superiority of the proposed technique. This work also presents a comparative assessment among the segmentation procedures, such as Level-Set (LS) and Chan-Vese (CV) methods. The experimental outcome authenticates that, the results by Kapur and Otsu threshold are approximately similar when the LS is implemented and the CV with the Otsu presents better values of Jaccard, Dice and Accuracy compared to other methods presented in this research.

A Flying Gray Ball Multi-illuminant Image Dataset for Color Research

2020 ◽  
Vol 64 (5) ◽  
pp. 50411-1-50411-8
Author(s):  
Hoda Aghaei ◽  
Brian Funt
Keyword(s):  
Machine Vision ◽  
Spectral Reflectance ◽  
Color Constancy ◽  
Ground Truth ◽  
Practical Approach ◽  
Post Processing ◽  
Illumination Estimation ◽  
Processing Step ◽  
Image Dataset

Abstract For research in the field of illumination estimation and color constancy, there is a need for ground-truth measurement of the illumination color at many locations within multi-illuminant scenes. A practical approach to obtaining such ground-truth illumination data is presented here. The proposed method involves using a drone to carry a gray ball of known percent surface spectral reflectance throughout a scene while photographing it frequently during the flight using a calibrated camera. The captured images are then post-processed. In the post-processing step, machine vision techniques are used to detect the gray ball within each frame. The camera RGB of light reflected from the gray ball provides a measure of the illumination color at that location. In total, the dataset contains 30 scenes with 100 illumination measurements on average per scene. The dataset is available for download free of charge.

Minnesota Department of Transportation Case Studies for Coreless Asphalt Pavement Compaction Assessment

2020 ◽  
Vol 2674 (2) ◽  
pp. 291-301 ◽  
Author(s):  
Kyle Hoegh ◽  
Trevor Steiner ◽  
Eyoab Zegeye Teshale ◽  
Shongtao Dai
Keyword(s):  
Quality Assurance ◽  
Ground Truth ◽  
Calibration Method ◽  
Field Trials ◽  
Asphalt Pavements ◽  
Attractive Potential ◽  
Dielectric Measurements ◽  
Statistical Measures ◽  
Gyratory Compaction ◽  
Air Void

Available methods for assessing hot-mix-asphalt pavements are typically restricted to destructive methods such as coring that damage the pavement and are limited in coverage. Recently, density profiling systems (DPS) have become available with the capability of measuring asphalt compaction continuously, giving instantaneous measurements a few hundred feet behind the final roller of the freshly placed pavement. Further developments of the methods involved with DPS processing have allowed for coreless calibration by correlating dielectric measurements with asphalt specimens fabricated at variable air void contents using superpave gyratory compaction. These developments make DPS technology an attractive potential tool for quality control because of the real-time nature of the results, and quality assurance because of the ability to measure a more statistically significant amount of data as compared with current quality assurance methods such as coring. To test the viability of these recently developed methods for implementation, multiple projects were selected for field trials. Each field trial was used to assess the coreless calibration prediction by comparing with field cores where dielectric measurements were made. Ground truth core validation on each project showed the reasonableness of the coreless calibration method. The validated dielectric to air void prediction curves allowed for assessment of the tested pavements in relation to as-built characteristics, with the DPS providing the equivalent of approximately 100,000 cores per mile. Statistical measures were used to demonstrate how DPS can provide a comprehensive asphalt compaction evaluation that can be used to inform construction-related decisions and has potential as a future quality assurance tool.

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