Feature Extraction of Gram-Negative Bacteria Texture Using Grey Level Co-Occurrence Matrix and Scale-Invariant Feature Transform (original) (raw)

Counting of Micro-organisms for Medical Diagnosis Using Image Processing Method

Recognizing and counting the total number of microorganisms in a particular sample is one of the main task that is performed in biological research and diagnosis field. Usually the procedure for counting the total number of microorganisms is done manually which makes this task more tedious, lengthy as well as error prone. To overcome these drawbacks various algorithms have been presented in this paper that aims to automatically count the total number of microorganisms present in a sample using image processing methods. To achieve this goal object recognition method is used which recognize the microorganisms by their shape. Thresholding technique is also applied to the image containing microorganisms for counting. In last thresholding using morphological operations with object recognition method has been applied which overcome all the drawbacks and count total number of microorganisms automatically.

Image Processing and SVM Application for Microscopic Counting and Size Measuring of Staphylococci

2014

Microscopic count and size measurement of bacteria are necessary for microbiological work. Staphylococci are one of the most frequently found bacteria in food industry and hospitals. However, an expert is needed for such count. He needs to perform at least 10 visual counts, which cause eye strain and are time consuming. This research proposes the developed software, namely Micros-Staph, to aid in the microscopic count of these bacteria as well as the size measurement of the individual cell using extracted features from image processing techniques. These features are used to train Support Vector Machine (SVM) to differentiate the bacterial colony from noises in the images. Therefore, the number of colony can be easily counted. The cell size is obtained by comparing the number of pixels on the stage micrometer with the cell bounding box at the smallest area found after angle rotation. The experimental results show that the extracted features can be used to count the number of colony e...

Texture based feature extraction methods for content based medical image retrieval systems

Bio-medical materials and engineering, 2014

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

Detection of Escherichia Coli Bacteria by Using Image Processing Techniques

International Journal of Biology and Biomedical Engineering, 2022

Recently, image processing has proven itself as a fast and reliable technique in research in medicine and biology. Bacterial colony separation is an important and time-consuming process in studies in the field of microbiology. Bacteria counting is usually carried out by the naked eye or even by Coulter counter machines, which are based on the rather expensive electric field measurement method. In this study, image-based enumeration of Escherichia Coli over the colony morphology in the petri dish was investigated. In the experimental study, 4 different bacteria from the Enterobacteriaceae family were planted on petri dishes containing Eosin-methylene blue agar (Merck, Darmstadt, Germany). Escherichia Coli colony characteristics were determined by digitizing planted bacterial petri images. For the study, counting was done with the interface developed in MATLAB R2013a. After the classification criteria were determined, the method was tested on new petri dishes and successful results we...

Counting Bacteria Colonies Based on Image Processing Methods

2019

Counting of microbial colonies is crucial due to the applications of medical microbiology to search and detect the causes of diseases. While different tasks performed, the counting process of bacteria colonies is provided either by the searcher manually or by a common software, nowadays. The manual counting of bacteria colonies is tiresome, eye-straining, and time consuming for the searcher where common softwares require high troublesome with having high error rates. The aim of this study is detecting and counting bacteria colonies without having these limitations in today's non-practical applications. Therefore, an image-processing based bacteria colony counter designed in MATLAB. In the medical plasma laboratory of the Izmir Katip Celebi University three different types of hospital-acquired infection cause bacterias, which are Escherichia coli, Pseudomonas aeruginosa, and Enterococcus faecalis, cultured and examined properly, then, using the Circular Hough Transform (CHT) in MATLAB the detection and counting of bacteria colonies provided. To be able to obtain more practical usage, a Graphical User Interface (GUI) designed.

Identification and classification of cocci bacterial cells in digital microscopic images

International Journal of Computational Biology and Drug Design, 2011

In cytology, automating the feature extraction process yields an objective, quantitative, detailed and reproducible computation of cell morphofunctional characteristics and allows the analysis of a large quantity of images. The objective of the present study is to develop an automatic tool to identify and classify the different types of cocci bacterial cells in digital microscopic cell images. Geometric features are used to identify the arrangement of cocci bacterial cells, namely cocci, diplococci, streptococci, tetrad, sarcinae and staphylococci using 3σ , K-NN and Neural network classifiers. The current methods rely on the subjective reading of profiles by a human expert based on the various manual staining methods. In this paper, we propose a method for cocci bacterial cell classification by segmenting digital bacterial cell images and extracting geometric and statistical features for cell classification. The experimental results are compared with the manual results obtained by microbiology expert and other methods in the literature.

Image Processing Approach for Feature Extraction and Classification of Pneumonia Infected areas of Lungs

International Conference on Engineering & Technology (ICET-22), 2023

Lung diseases are responsible for many deaths and it is considered as one of the most common type of medical condition around the world [1]. In many occasions infections and genetics are identified as the main factors for lung diseases. Among many lung diseases Pneumonia is responsible for majority among those deaths [1]. From many years Chest radiograph (CXR) is aided by highly trained specialist in diagnosing these medical conditions. However, reviewing these images is a complicated task because, in pneumonia doctors need to consider number of other lung conditions such as fluid overload (pulmonary edema), bleeding and volume loss (atelectasis or collapse) Furthermore, with the limited number of available specialists, time constrain play a major role in this medical image reviewing process. Therefore, availability of an automated supporting system for this kind of tasks would be helpful for experts to quickly diagnose medical states. With the recent development in image processing and computer vision techniques, these techniques can be enabling in diagnosing medical images in a more efficient manner. This study proposes a system to achieve this purpose by providing more accurate results to find out pneumonia using CXR.

Image analysis framework for infection monitoring

IEEE transactions on bio-medical engineering, 2012

We present a novel framework for automatic extraction of the progress of an infection from time-series medical images, with application to pneumonia monitoring. In each image of a series, the lungs, which are the body components of interest in our study, are detected and delineated by a modified active shape model-based algorithm that is constrained by binary approximation masks. This algorithm offers resistance in the presence of infection manifestations that may distort the typical appearance of the body components of interest. The relative extent of the infection manifestations is assessed by supervised classification of samples acquired from the respective image regions. The samples are represented by multiple dissimilarity features fused according to a novel entropy-based weighted voting scheme offering nonparametric operation and robustness to outliers. The output of the proposed framework is a time series of structured data quantifying the relative extent of infection manifestations at the body components of interest over time. The results obtained indicate an improved performance over relevant state-of-the-art methods. The overall accuracy quantified by the area under receiver operating characteristic reaches 90.0 ± 2.1%. The effectiveness of the proposed framework to pneumonia monitoring, the generality, and the adaptivity of its methods open perspectives for application to other medical imaging domains.

Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia

Purpose: Medical imaging is one of the main methods of diagnosing COVID-19, along with real-time reverse transcription-polymerase chain reaction (RT-PCR) tests. The purpose of the study was to analyse the texture parameters of chest X-rays (CXR) of patients suspected of having COVID-19. Material and methods: Texture parameters of the CXRs of 70 patients with symptoms typical of COVID-19 infection were analysed using LIFEx software. The regions of interest (ROIs) included each lung separately, for which 57 parameters were tested. The control group consisted of 30 healthy, age-matched patients with no pathological findings in CXRs. Results: According to the ROC analysis, 13 of the tested parameters differentiate the radiological image of lungs with COVID-19 features from the image of healthy lungs: GLRLM_LRHGE (AUC 0.91); DISCRETIZED_Q3 (AUC 0.90); GLZLM_HGZE (AUC 0.90); GLRLM_HGRE (AUC 0.89); DISCRETIZED_mean (AUC 0.89); DISCRETIZED_Q2 (AUC 0.61); GLRLM_SRHGE (AUC 0.87); GLZLM_LZHGE (AUC 0.87); GLZLM_SZHGE (AUC 0.84); DISCRE-TIZED_Q1 (AUC 0.81); NGLDM_Coarseness (AUC 0.70); DISCRETIZED_std (AUC 0.64); CONVENTIONAL_Q2 (AUC 0.61). Conclusions: Selected texture parameters of radiological CXRs make it possible to distinguish COVID-19 features from healthy ones.

Disease Detection by Using Image Processing

2017

The analysis of malaria is based on novel Annular Ring Ratio Method which is already implemented, tested and validated in MATLAB. The method detects the blood components such as the Red Blood Cells (RBCs), White Blood Cells (WBCs), and identifies the parasites in the infected RBCs. Dengue fever is a viral disease and it is a major issue in many developing countries, including India. The main objective is to detect and count platelet to diagnose Dengue Haemorrhagic Fever this reduces labor intensive, time and cost. There is a solution in hands of digital image processing to face this challenge. Segmentation techniques and morphological operation are applied to investigate the number of platelets which is used to diagnose dengue using the microscopic image of blood smear. The platelet count is estimated using various Segmentation techniques and morphological operations and with the help of the platelets count dengue fever infection is detected. One of the morphological operations call...