A Beginner's Guide to MultiLevel Image Thresholding

Rajinikanth, Venkatesan; Raja, N. Sri Madhava; Dey, Nilanjan

doi:10.1201/9781003049449

Cited by 6 publications

(9 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Grayscale image: Convert brain print image to grayscale image. Histogram equalization: The graph equation is achieved by spreading most of the repeated density values in an inefficient mode (Rajinikanth et al, 2020;Sun et al, 2015).…”

Section: Pre-processingmentioning

confidence: 99%

“…Where, f[x,y] is appear the gray value, and H is a histogram of the image. Resizing: resize Brinprint MRI to 100*100, resize image is an important operation to enable the ML algorithms and CNN model to work with standardized scales before starting to classify the pre-assembled images from the third pre-processing phase (Rajinikanth et al, 2020;Vasuki et al, 2017). for vectors to include better attributes between classes (Santhi et al, 2018).…”

Section: Pre-processingmentioning

confidence: 99%

See 1 more Smart Citation

Human Identification Model Considering Biometrics Features

Radhi

2022

JLMA

View full text Add to dashboard Cite

In the medical field, brain classification is an effective technique for identifying a person through his brain print based on the hidden biometrics of high specificity included in the magnetic resonance images(MRI) of the brain, as this privacy strongly contributes to the issue of verification and identification of the person. In this paper, the brain print is extracted from the MRI obtained from 50 healthy people, which were passed through several pre-processing techniques in order to be used in the classification stage through convolutional neural network model, among those pre-classification stages, data collection after extracting the influential features for each image, which was based on linear discrimination analysis (LDA). The experimental results showed the importance of using LDA for feature extraction and adoption as input for K-NN and CNN classifiers. The classifiers proved successful in the classification if the features extracted with the help of LDA were adopted. Where CNN had the ability to classify with an accuracy of 99%, 82% for K-NN. The final stage in identifying a person through a brain fingerprint relied mainly on the model's success in classifying and predicting the remaining data in the testing stage.

show abstract

Section: Pre-processingmentioning

confidence: 99%

Section: Pre-processingmentioning

confidence: 99%

Human Identification Model Considering Biometrics Features

Radhi

2022

JLMA

View full text Add to dashboard Cite

show abstract

“…When a patient is suspected of having a brain tumor clinically, radiological evaluation is required to determine the location of the tumor, its extent and its relationship to surrounding structures. This information is very important and critical in deciding what therapy to be used by patients, such as surgery, radiation, and chemotherapy (Rajinikanth et al, 2020):…”

Section: Medical Imaging Techniques Detecting Gliomamentioning

confidence: 99%

“…By using CT in neuroradiology, images of the brain can be produced directly. Brain CT, which has become the procedure of choice for evaluation and diagnosis of brain tumors, has replaced invasive procedures such as pneumoencephalography or cerebral angiography (Rajinikanth et al, 2020). Improved image quality, reduced costs, and reduced number of scans to be performed have resulted in significant developments in the use of CT applications.…”

Section: Medical Imaging Techniques Detecting Gliomamentioning

confidence: 99%

Study on Brain Tumor Classification Through MRI Images Using a Deep Convolutional Neural Network

Sharma¹,

Khanna²,

Gambhir³

et al. 2021

International Journal of Information Retrieval Research

View full text Add to dashboard Cite

Brain tumor (Glioma) is one of the deadliest diseases that attack humans, now even men or women aged 20-30 are suffering from this disease. To cure tumor in a person, doctors use MRI machine, because the results of MRI images are proven to provide better image results than CT-Scan images, but sometimes it is difficult to distinguish between the MRI images having tumors with that images not having tumor from MRI image results. It is because of resulting contrast is like any other normal organ. However, using features of image processing techniques like scaling, contrast enhancement and thresh-holding based in Deep Neural Networks the scheme can classify the results more appropriately and with high accuracy. In this paper, this study reveals the nitty-gritty of Brain tumor (Gliomas) and Deep Learning techniques for better inception in the field of computer-vision.

show abstract

“…Image thresholding using the heuristic algorithm is a widely accepted procedure and various techniques to be followed during the threshold selection can be found in the recent research work [15][16][17]. The optimal selection of the threshold depends on; (i) The dimension of the image, (ii) Complexity in the pixel distribution, and the (iii) Nature of image (RGB/Grey).…”

Section: Introductionmentioning

confidence: 99%

Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy

Kadry

Rajinikanth²

2021

Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika

View full text Add to dashboard Cite

In the current era, image evaluations play a foremost role in a variety of domain, where the processing of the digital images is essential to identify the vital information. The image multi-thresholding is a vital image preprocessing field in which the available digital image is enhanced by grouping the similar pixel values. Normally, the digital test images are available in RGB/grey scale format and the appropriate processing methodology is essential to treat the images with a chosen methodology. In the proposed approach, Tsallis Entropy (TE) supported multi-level thresholding is planned for the benchmark grey scale imagery of dimension 512x512x1 pixels using a chosen threshold values (T=2,3,4,5). This work suggests the possible Cost Value (CV) that can be considered during the optimization search and the proposed work is executed by considering the maximization of the TE as the CV. The entire thresholding task is executed using Moth-Flame Algorithm (MFA) and the accomplished results are validated based on the image quality measures of various thresholds. The attained result with MFO is better compared to the result of CS, BFO, PSO and GA.

show abstract

A Beginner's Guide to MultiLevel Image Thresholding

Cited by 6 publications

References 0 publications

Human Identification Model Considering Biometrics Features

Human Identification Model Considering Biometrics Features

Study on Brain Tumor Classification Through MRI Images Using a Deep Convolutional Neural Network

Grey Scale Image Multi-Thresholding Using Moth-Flame Algorithm and Tsallis Entropy

Contact Info

Product

Resources

About