AUTOMATIC ANALYSIS OF MICROSCOPY IMAGES USING THE DLGRAM01 CLOUD SERVICE

Исследование материалов методами микроскопии нередко включает стадию подсчета количества наблюдаемых объектов и определения их статистических параметров, для чего необходимо измерять сотни объектов. В работе описан облачный сервис DLgram01, который позволяет специалистам в области материаловедения, не имеющих навыков программирования, выполнять автоматизированную обработку изображений - определять количество и параметры (площадь, размер) изучаемых объектов. Сервис разработан с использованием новейших достижений в области глубокого машинного обучения, для обучения нейронной сети пользователю необходимо разметить несколько изучаемых объектов. Обучение нейронной сети производится автоматически за несколько минут. Важными особенностями сервиса DLgram01 является возможность корректировать результаты предсказания нейронной сети, а также получение детальной информации о всех распознанных объектах. Использование сервиса позволяет существенно сократить временные затраты на количественный анализ изображений, снизить влияние субъективного фактора, повысить точность анализа и его эргоемкость. The study of materials by microscopy often includes counting the number of observed objects and determining their statistical parameters, for which it is necessary to measure hundreds of objects. The created DLgram01 cloud service allows specialists in the field of materials science who do not have programming skills to perform automated image processing - to determine the number and parameters (area, size) of the objects under study. The service is developed using the latest achievements in the field of deep machine learning. To train a neural network, the user needs to label only several objects. The neural network is trained automatically in a few minutes. Important features of the DLgram01 service are the ability to adjust the results of neural network prediction, as well as obtaining detailed information about all recognized objects. Using the service allows to significantly decrease the time for quantitative image analysis, reduce the influence of the subjective factor, increase the accuracy of the analysis and its ergo-intensity.

Labkit: Labeling and Segmentation Toolkit for Big Image Data

We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data. It offers easy to use manual and automated image segmentation routines that can be rapidly applied to single- and multi-channel images as well as to timelapse movies in 2D or 3D. Labkit is specifically designed to work efficiently on big image data and enables users of consumer laptops to conveniently work with multiple-terabyte images. This efficiency is achieved by using ImgLib2 and BigDataViewer as the foundation of our software. Furthermore, memory efficient and fast random forest based pixel classification inspired by the Waikato Environment for Knowledge Analysis (Weka) is implemented. Optionally we harness the power of graphics processing units (GPU) to gain additional runtime performance. Labkit is easy to install on virtually all laptops and workstations. Additionally, Labkit is compatible with high performance computing (HPC) clusters for distributed processing of big image data. The ability to use pixel classifiers trained in Labkit via the ImageJ macro language enables our users to integrate this functionality as a processing step in automated image processing workflows. Last but not least, Labkit comes with rich online resources such as tutorials and examples that will help users to familiarize themselves with available features and how to best use \Labkit in a number of practical real-world use-cases.

A STUDY OF TOMATO FRUIT DISEASE DETECTION USING RGB COLOR THRESHOLDING AND K-MEANS CLUSTERING

The agriculture field plays vital role in development of smart India. To increase economic level the production of fruits, crops and vegetables can use CAD technique using image processing tools. Identifying diseases in fruits is an image processing’s big challenging task. This can done by continuous visual photos or videos monitoring system. The automated image processing research helps to control the pesticides on fruits and vegetables. In this paper we focus to detect the diseases of tomato at earlier stage. The proposed system shows how different algorithms such as color thresholding segmentation techniques and K-means clustering are used. In proposed system shows the K-means Clustering is better than RGB color based colorthresholder method for detecting tomato diseases in beginning stage.

Validation and tuning of in situ transcriptomics image processing workflows with crowdsourced annotations

Recent advancements in in situ methods, such as multiplexed in situ RNA hybridization and in situ RNA sequencing, have deepened our understanding of the way biological processes are spatially organized in tissues. Automated image processing and spot-calling algorithms for analyzing in situ transcriptomics images have many parameters which need to be tuned for optimal detection. Having ground truth datasets (images where there is very high confidence on the accuracy of the detected spots) is essential for evaluating these algorithms and tuning their parameters. We present a first-in-kind open-source toolkit and framework for in situ transcriptomics image analysis that incorporates crowdsourced annotations, alongside expert annotations, as a source of ground truth for the analysis of in situ transcriptomics images. The kit includes tools for preparing images for crowdsourcing annotation to optimize crowdsourced workers’ ability to annotate these images reliably, performing quality control (QC) on worker annotations, extracting candidate parameters for spot-calling algorithms from sample images, tuning parameters for spot-calling algorithms, and evaluating spot-calling algorithms and worker performance. These tools are wrapped in a modular pipeline with a flexible structure that allows users to take advantage of crowdsourced annotations from any source of their choice. We tested the pipeline using real and synthetic in situ transcriptomics images and annotations from the Amazon Mechanical Turk system obtained via Quanti.us. Using real images from in situ experiments and simulated images produced by one of the tools in the kit, we studied worker sensitivity to spot characteristics and established rules for annotation QC. We explored and demonstrated the use of ground truth generated in this way for validating spot-calling algorithms and tuning their parameters, and confirmed that consensus crowdsourced annotations are a viable substitute for expert-generated ground truth for these purposes.

How Many Bedrooms Do You Need? A Real-Estate Recommender System from Architectural Floor Plan Images

This paper introduces an automated image processing method to analyze an architectural floor plan database. The floor plan information, such as the measurement of the rooms, dimension lines, and even the location of each room, can be automatically produced. This assists the real-estate agents to maximise the chances of the closure of deals by providing explicit insights to the prospective purchasers. With a clear idea about the layout of the place, customers can quickly make an analytical decision. Besides, it reduces the specialized training cost and increases the efficiency in business actions by understanding the property types with the greatest demand. Succinctly, this paper utilizes both the traditional image processing and convolutional neural networks (CNNs) to detect the bedrooms by undergoing the segmentation and classification processes. A thorough experiment, analysis, and evaluation had been performed to verify the effectiveness of the proposed framework. As a result, a three-class bedroom classification accuracy of ∼ 90% was achieved when validating on more than 500 image samples that consist of the different room numbers. In addition, qualitative findings were presented to manifest visually the feasibility of the algorithm developed.

An open-source pipeline for analysing changes in microglial morphology

Changes in microglial morphology are powerful indicators of the inflammatory state of the brain. Here, we provide an open-source microglia morphology analysis pipeline that first cleans and registers images of microglia, before extracting 62 parameters describing microglial morphology. It then compares control and ‘inflammation’ training data and uses dimensionality reduction to generate a single metric of morphological change (an ‘inflammation index’). This index can then be calculated for test data to assess inflammation, as we demonstrate by investigating the effect of short-term high-fat diet consumption in heterozygous Cx3CR1-GFP mice, finding no significant effects of diet. Our pipeline represents the first open-source microglia morphology pipeline combining semi-automated image processing and dimensionality reduction. It uses free software (ImageJ and R) and can be applied to a wide variety of experimental paradigms. We anticipate it will enable others to more easily take advantage of the powerful insights microglial morphology analysis provides.

Effective Strain Image Sequence Selection by Using Semi-Automated Image Processing Technique

One fourth of the cancer detected in women worldwide is breast cancer which leads this as a major threat for women. There are many methods of detecting cancer among which ultra-sound strain imaging is one of the promising techniques. However, in strain sequence, not all the frames show clear tumor visibility. Consequently, in this paper we tested some well-defined algorithms to find only those frames where the tumor is comparatively clearly visible. We have used Mean Pixel Difference (MPD) and Gray- Level Co-occurrence Matrix (GLCM) to find the frames with better tumor visibility. We have tested our methods in several real-life cases and the results have been examined by a professional doctor. The MPD has an accuracy of 96.2% and the GLCM. Contrast has that of 55.55%. GUB JOURNAL OF SCIENCE AND ENGINEERING, Vol 7, Dec 2020 P 8-13