A survey on image preprocessing techniques for diverse fields of medical imagery

Vasuki, P.; Kanimozhi, J.; Devi, Mrs.B Sita

doi:10.1109/iceice.2017.8192443

Cited by 28 publications

(14 citation statements)

References 2 publications

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“…The quality of the image depends on the skills of the person making slides and the one who is capturing images using a microscope [ 44 ]. Hence, the following steps were taken to enhance the quality and variability of the dataset.…”

Section: Methodsmentioning

confidence: 99%

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Dhindsa

Bhatia

Agrawal

et al. 2021

Entropy

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The accurate classification of microbes is critical in today’s context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).

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Section: Methodsmentioning

confidence: 99%

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Dhindsa

Bhatia

Agrawal

et al. 2021

Entropy

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“…Here, the fire detection process is performed via four main steps: (a) Image acquisition: here, images are captured from an installed camera at a regular interval. (b) Image preprocessing: captured images then might be preprocessed via different techniques (e.g., contrast adjustment, intensity adjustment, and color conversion) to enhance their quality or to reduce their computational complexity [82]. (c) Image similarity measurement: here, two captured images are compared to each other to decide how close they are.…”

Section: Table 5 Distribution Of Papers By Intrusion Detection Methodsmentioning

confidence: 99%

Systematic Survey on Smart Home Safety and Security Systems Using the Arduino Platform

Sarhan

2020

IEEE Access

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Smart home safety and security systems have gained much importance over the last few years owing to their notable impact in reducing and preventing losses in resources and human life caused by unwanted situations that could occur while homeowners are far away from their homes. To date, there is a lack of an in-depth literature analysis that could help researchers and developers better understand these systems and their applications in different contexts. It is therefore crucial that research evidence published in this area is presented. In this study, 63 research papers that examined smart home safety and security systems using the Arduino platform from popular literature databases were thoroughly surveyed to extract useful data. Then, the extracted data were analyzed to answer many research questions concerning state-ofthe-art applications of these systems, their architectures, their enabling technologies, their components, etc. In addition, several challenges that these systems currently face and how future research could enable better implementation and use of these systems were discussed. INDEX TERMS Arduino, smart homes, safety systems, security systems, sensors and actuators, architectures, enabling technologies.

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“…In dental images enhancement, the prime objective is to give more visibility to the core information and neglect or nullify the secondary features or information to improve the quality of visual perception [8], [9]. Both the objective of the perception by the human vision system (HVS) and computer vision system (CVS) shall meet the respective goal of diagnosis by humans and computers.…”

Section: Introductionmentioning

confidence: 99%

Evaluating spatial and frequency domain enhancement techniques on dental images to assist dental implant therapy

Shashikala¹,

Thangadurai

2021

IJECE

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<span lang="EN-US">Dental imaging provides the patient's anatomical details for the dental implant based on the maxillofacial structure and the two-dimensional geometric projection, helping clinical experts decide whether the implant surgery is suitable for a particular patient. Dental images often suffer from problems associated with random noise and low contrast factors, which need effective preprocessing operations. However, each enhancement technique comes with some advantages and limitations. Therefore, choosing a suitable image enhancement method always a difficult task. In this paper, a universal framework is proposed that integrates the functionality of various enhancement mechanisms so that dentists can select a suitable method of their own choice to improve the quality of dental image for the implant procedure. The proposed framework evaluates the effectiveness of both frequency domain enhancement and spatial domain enhancement techniques on dental images. The selection of the best enhancement method further depends on the output image perceptibility responses, peak signal-to-noise ratio (PSNR), and sharpness. The proposed framework offers a flexible and scalable approach to the dental expert to perform enhancement of a dental image according to visual image features and different enhancement requirements.</span>

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A survey on image preprocessing techniques for diverse fields of medical imagery

Cited by 28 publications

References 2 publications

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification

Systematic Survey on Smart Home Safety and Security Systems Using the Arduino Platform

Evaluating spatial and frequency domain enhancement techniques on dental images to assist dental implant therapy

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