Appending active contour model on digital panoramic dental X-rays images for segmentation of maxillofacial region

Divya, K. Veena; Jatti, Anand; Meharaj, P Sabah; Joshi, Revan Kumar

doi:10.1109/iecbes.2016.7843491

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…If the entropy of the images is measured, the preprocessed image typically has low information content compare to the original image. Therefore, the aim of preprocessing is to reduce the noise and improve the quality of image highlighting few important features which is necessary for further processing in segmentation and recognition tasks [4]. Image preprocessing is classified into two categories such as image enhancement and image restoration techniques.…”

Section: Preprocessingmentioning

confidence: 99%

Computer Aided Classification using Support Vector Machines in Detecting Cysts of Jaws

K¹,

Jatti²,

P³

et al. 2017

Adv. sci. technol. eng. syst. j.

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Section: Preprocessingmentioning

confidence: 99%

Computer Aided Classification using Support Vector Machines in Detecting Cysts of Jaws

K¹,

Jatti²,

P³

et al. 2017

Adv. sci. technol. eng. syst. j.

Self Cite

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“…Kernel methods calculate principal components through feature space nonlinear mapping and are finally located in a nonlinearly distorted space [9]. The data mining techniques are used to evaluate the performance of comparing and analyzing mined features on linear and nonlinear feature space, such as linear regression, which is a technique to discover the relationship between a series of independent and dependent variables by a suitable equation [10].…”

Section: Introductionmentioning

confidence: 99%

Enhanced feature selection algorithm for pneumonia detection

Abdullah¹,

Abedi²,

Hadi³

2023

PEN

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Pneumonia is a type of lung disease that can be detected using X-ray images. The analysis of chest X-ray images is an active research area in medical image analysis and computer-aided radiology. This research aims to improve the accuracy and efficiency of radiologists' work by providing a technique for identifying and categorizing diseases. More attention should be given to applying machine learning approaches to develop a robust chest X-ray image classification method. The typical method for detecting Pneumonia is through chest X-ray images but analyzing these images can be complex and requires the expertise of a radiographer. This paper demonstrates the feasibility of detecting the disease using chest X-ray images as datasets and a Support Vector Machine combined with a Naive Bayesian classifier, with PCA and GA as feature selection methods. The selected features are essential for training many classifiers. The proposed system achieved an accuracy of 92.26%, using 91% of the principal component. The study's result suggests that using PCA and GA for feature selection in chest X-ray image classification can achieve a good accuracy of 97.44%. Further research is needed to explore the use of other data mining models and care components to improve the accuracy and effectiveness of the system.

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A Survey on Image Processing Techniques for Detection of Cavities in Dental X-ray Images

Geethasree¹,

Sri²,

Sravani³

et al. 2023

Lecture Notes in Electrical Engineering

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Appending active contour model on digital panoramic dental X-rays images for segmentation of maxillofacial region

Cited by 6 publications

References 2 publications

Computer Aided Classification using Support Vector Machines in Detecting Cysts of Jaws

Computer Aided Classification using Support Vector Machines in Detecting Cysts of Jaws

Enhanced feature selection algorithm for pneumonia detection

A Survey on Image Processing Techniques for Detection of Cavities in Dental X-ray Images

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