Automatic System for Plasmodium Species Identification from Microscopic Images of Blood-Smear Samples

Ghosh, Pinaki; Bhattacharjee, Debotosh; Nasipuri, Mita

doi:10.1007/s41666-017-0009-2

Cited by 11 publications

(4 citation statements)

References 45 publications

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“…The constriction contrast between gray matter and white matter expect a distinction within the lipid substance. The X-map identified ischemic injuries, and it did so more delicately than mimicked standard CT [12,13].…”

Section: Types Of Medical Imagesmentioning

confidence: 95%

A Comprehensive Review on Medical Imaging Technologies to Detect Brain Stroke

Halder¹,

Biswas²,

Samanta³

et al. 2023

ITM Web Conf.

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Brain stroke discovery remains one of the foremost critical investigation zones in restorative imaging. A brain stroke can be classified into two primary categories Ischemic and Haemorrhagic. Imaging technology detects strokes early. When it comes to imaging, the American College of Radiology persistently overhauls its rules for imaging pathways through the ACR Fittingness Criteria. There are a few prevalent restorative imaging methods for brain stroke locations like X-ray, computed tomography, positron outflow tomography, attractive reverberation imaging, and utilitarian attractive reverberation imaging. MRI and fMRI, using radio waves and a magnet, provide a detailed image of the brain, detecting a wide variety of brain tissues and blood vessel anomalies, including those in regions undetectable by CT (Computed tomography) scans. Advanced technology is making medical imaging crucial in emergencies, with faster scans and better stroke detection. Therefore, MRI and/or fMRI remain the best imaging technologies for detecting strokes.

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Section: Types Of Medical Imagesmentioning

confidence: 95%

A Comprehensive Review on Medical Imaging Technologies to Detect Brain Stroke

Halder¹,

Biswas²,

Samanta³

et al. 2023

ITM Web Conf.

View full text Add to dashboard Cite

show abstract

“…The initial region of interest extraction is a process where background regions are eliminated from further computation. In the conventional region of interest, segmentation is a process where a pixel is considered either foreground or background [20,21,22]. There is no provision to categorized suspected regions into another class, so to cope up with this issue, the image is transformed into neutrosophic domain.…”

Section: Detailed Design Of Caries Lesion Identification From Dental mentioning

confidence: 99%

“…If the neutrosophic set is denoted as N then N ϵ U. An element x from U is noted with respect to N as x(T, I, F) and belongs to N by the following way [16,20].…”

Section: Neutrosophic Logicmentioning

confidence: 99%

Neutrosophic Set-Based Caries Lesion Detection Method to Avoid Perception Error

Datta

Chaki

Modak³

2020

SN COMPUT. SCI.

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Dental caries is an infectious oral disease. The monitoring of caries region boundary, in regular intervals, is important for treatment purpose. To detect dental caries lesion, most of the time dentists use X-ray images. Due to human brain perception, sometimes it is difficult to detect the caries lesion accurately by observing the X-ray image manually. In this work, a framework has been proposed to detect caries lesion automatically within the optimum time. Almost all caries detection methods from the radiographic images apply iterative methods upon the entire image to separate initially suspected regions. Then, further processing is performed on the separated regions. This method reduced huge computation efforts by avoiding applying iterative methods upon the entire input image. This method transforms the input X-ray image into its equivalent neutrosophic domain to obtain initially suspected region. We have used a custom feature named 'weight' for neutrosophication. This feature is calculated by fusing other features differently. Once the initial region is detected, it is examined further to test whether there exists any iso-center rings like the catchment basin. This is the most important property of caries lesions. After the suspected region is confirmed as caries lesion, then the caries boundary is detected using active contour technique. The advantage of this system is that it avoids repetitive iterations at the time of suspected region selection using neutrosophication. Repetitive iterations upon the entire picture dimension are a time-consuming job. The performance of the proposed research work is satisfactory; the average accuracy is above 92%.

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“…The SR methods using machine learning require huge amount of low and corresponding high resolution image pair as a training set. But in reality, this huge number of image pair is not available in medical domain specially in dental radiography [28]- [30]. Hence, conventional ML methods cannot fit directly for high resolution image reconstruction.…”

Section: B Motivationmentioning

confidence: 99%

A Granular Level Feature Extraction Approach to Construct HR Image for Forensic Biometrics Using Small Training DataSet

2020

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In forensic biometrics old x-ray images are often used for identification and verification. The number of homicide cases has increased manyfold along with population growth. Forensic odontology is a less expensive method to solve these cases. In forensic odontology, the old dental x-ray images of a victim are used as ante mortem data to compare the pattern for identification. The success of this post mortem biometrics process totally depends on the brain perception of forensic odontologist. Low resolution (LR) image may create human brain perception error at the time of making decision both in case of disease diagnosis and forensic biometrics. In such context, a software solution could help to reconstruct high resolution (HR) image from LR medical x-ray image. However, methods like convolution neural network (CNN) require high volume of training images to reconstruct HR images. Unfortunately, the available medical x-ray image repository does not offer a large volume of training dataset. This work aims to overcome this data related issue and presents a granular level feature based HR grayscale medical xray image reconstruction mechanism from LR image. This method uses machine learning for HR image reconstruction. The proposed granular level feature extraction method generates adequate amount of training set from limited amount of training image sets. These granular level features contain the influence of neighboring points separately and its direction. These features are highly immune to noises and preserve important properties like edges. Polynomial regression model is used for HR value reconstruction. This image reconstruction provides satisfactory results for image database like dental radiographs datasets and the average structural similarity index (SSIM) metric reaches 0.9326.

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Automatic System for Plasmodium Species Identification from Microscopic Images of Blood-Smear Samples

Cited by 11 publications

References 45 publications

A Comprehensive Review on Medical Imaging Technologies to Detect Brain Stroke

A Comprehensive Review on Medical Imaging Technologies to Detect Brain Stroke

Neutrosophic Set-Based Caries Lesion Detection Method to Avoid Perception Error

A Granular Level Feature Extraction Approach to Construct HR Image for Forensic Biometrics Using Small Training DataSet

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