Classification of indirect immunofluorescence images using thresholded local binary count features

Susaiyah, Allmin; Pathan, Suhail Parvaze; Swaminathan, Ramakrishnan

doi:10.1515/cdbme-2016-0106

Cited by 6 publications

(3 citation statements)

References 7 publications

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“…Saleem et al derived an approach using Situationaware BDI Reasoning for the early finding of the symptoms of COVID-19 using a smart watch [54]. Influence of pre-processing and utility of threshold and level set-based segmentation along with texture and key point analysis using fluorescence-based microscopic images and infrared-based thermograms in two separate studies [55][56][57][58]. Laplace Beltrami (LB) eigenvalue features are used to execute shape detection with MRI of the images discussed [59].…”

Section: Role Of Vesselnessmentioning

confidence: 99%

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Basha,

Albuquerque,

Chelloug

et al. 2024

CMES

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Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images into common lung infections, lung opacity (LO), COVID-19, and viral pneumonia (VP). The results demonstrate that the fusion of texture and vesselbased features provides an effective ML model for aiding diagnosis. The ML model validation using performance measures, including an accuracy of approximately 91.8% with an RF-based classifier, supports the usefulness of the feature set and classifier model in categorizing the four different pathologies. Furthermore, the study investigates the importance of the devised features in identifying the underlying pathology and incorporates histogrambased analysis. This analysis reveals varying natural pixel distributions in CXR images belonging to the normal, COVID-19, LO, and VP groups, motivating the incorporation of additional features such as mean, standard deviation, skewness, and percentile based on the filtered images. Notably, the study achieves a considerable improvement in categorizing COVID-19 from LO, with a true positive rate of 97%, further substantiating the effectiveness of the methodology implemented.

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Section: Role Of Vesselnessmentioning

confidence: 99%

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Basha,

Albuquerque,

Chelloug

et al. 2024

CMES

View full text Add to dashboard Cite

show abstract

“…In the past several automated algorithms have been developed for analyzing microscopic images, counting of cells and quanti cation of different cellular phenomena [9][10][11][12][13][14][15][16]. Segmentation and detection are the two major requirements for analyzing microscopic images.…”

Section: Introductionmentioning

confidence: 99%

Investigation of time dependent growth of HepG2 cancerous cells using deep learning and shape metrics

Ronickom

2023

Preprint

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Study of growth of Human hepatocellular carcinoma cells (HepG2) cells provide useful information for clinical study of megestrol acetate for the treatment of Hepatocellular carcinoma. In this study, we analyzed the growth of HepG2 liver cancerous cells using image processing methods. Initially, the HepG2 cells were cultured and microscopic images were captured in bright field mode at time of seeding (00 h) followed by 06 h and 18 h. We segmented the cells using Tsallis and deep learning methods and the average size of colonies were calculated using shape metrics. Finally, we correlated the cell density obtained using MTT assay with the average size of colonies calculated from the Tsallis and deep learning segmented images. Results show that deep learning methods were able to segment the cells more precisely than Tsallis method. The average colony area calculated from the deep learning segmented images increases with time and concentration. The cell growth and adhesion pattern measured by deep leaning method showed good consistency with spectroscopic observations. The process pipeline provides a new way to assess cell adhesion and proliferation with capabilities in measuring their occupied surface area. The framework documented can be a promising tool to automate cancerous cell growth by evaluating average colony size for studying normal and pathological conditions.

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“…Susaiyah et al implemented image segmentation by means of multilevel Otsu thresholding. This was validated through using Dice's coefficient; the Jaccard index and accuracy recommend the first amongst four levels in Otsu [ 36 ]. Parvaze and Ramakrishnan considered thirteen Positive and intermediate intensity level images with homogenous, Centro mere, and Nucleolus patterns for LS based segmentation to extract objects of interest [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia

Basha

Neto

Alshathri

et al. 2022

Computational Intelligence and Neuroscience

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Coronavirus disease (COVID-19) has created an unprecedented devastation and the loss of millions of lives globally. Contagious nature and fatalities invariably pose challenges to physicians and healthcare support systems. Clinical diagnostic evaluation using reverse transcription-polymerase chain reaction and other approaches are currently in use. The Chest X-ray (CXR) and CT images were effectively utilized in screening purposes that could provide relevant data on localized regions affected by the infection. A step towards automated screening and diagnosis using CXR and CT could be of considerable importance in these turbulent times. The main objective is to probe a simple threshold-based segmentation approach to identify possible infection regions in CXR images and investigate intensity-based, wavelet transform (WT)-based, and Laws based texture features with statistical measures. Further feature selection strategy using Random Forest (RF) then selected features used to create Machine Learning (ML) representation with Support Vector Machine (SVM) and a Random Forest (RF) to make different COVID-19 from viral pneumonia (VP). The results obtained clearly indicate that the intensity and WT-based features vary in the two pathologies that are better differentiated with the combined features trained using SVM and RF classifiers. Classifier performance measures like an Area Under the Curve (AUC) of 0.97 and by and large classification accuracy of 0.9 using the RF model clearly indicate that the methodology implemented is useful in characterizing COVID-19 and Viral Pneumonia.

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Classification of indirect immunofluorescence images using thresholded local binary count features

Cited by 6 publications

References 7 publications

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Robust Machine Learning Technique to Classify COVID-19 Using Fusion of Texture and Vesselness of X-Ray Images

Investigation of time dependent growth of HepG2 cancerous cells using deep learning and shape metrics

Multithreshold Segmentation and Machine Learning Based Approach to Differentiate COVID-19 from Viral Pneumonia

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