Active Contour Based Segmentation Techniques for Medical Image Analysis

Hemalatha, R.; Thamizhvani, T. R.; Dhivya, A Josephin Arockia; Joseph, Josline Elsa; Babu, Bincy; Chandrasekaran, R. M.

doi:10.5772/intechopen.74576

Cited by 86 publications

(55 citation statements)

References 33 publications

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“…Active contour model [13] is an algorithm for segmentation using the energy restraint of the image to separate certain regions of interest. It is applied to various medical applications as computerized tomography, Magnetic Resonance Imaging, fundus images, and cardiac image.…”

Section: Methodsmentioning

confidence: 99%

Optic Disc and Optic Cup Segmentation Methodology for Glaucoma Detection

Aouf¹,

Ali²,

Kareem³

2020

International Journal of Engineering Research and Technology

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A retinal image is an essential tool for ophthalmology diagnosis. It is necessary for the early detection of many retinal diseases including glaucoma. Glaucoma acquires a second place in the leading causes of blindness worldwide. It damages the optic nerve which is responsible for connecting 1.2 million different nerve fibers to the brain. Once the optic nerve is destroyed it can not be treated but its early diagnosis and treatment can prevent the vision loss. The detection of glaucoma is usually performed by measuring the ratio between the optic disc (OD) and the optic cup (OC). The average ratio between the OD and the OC is 0.3. If it is higher than 0.3, then it is an indication of glaucoma. This paper aims to present an image processing algorithm for automatic identification of glaucoma by using active contour (snake) model to contour the OD and the OC boundaries that will help calculate the cup to disk ratio (CDR). The proposed model will help ophthalmologists to monitor any changes occur in the optic nerve.

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Section: Methodsmentioning

confidence: 99%

Optic Disc and Optic Cup Segmentation Methodology for Glaucoma Detection

Aouf¹,

Ali²,

Kareem³

2020

International Journal of Engineering Research and Technology

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“…In healthcare, segmentation of images from different regions of a human body is done to diagnose and detect health conditions. Tumors, cancer cells, inflations, blood clotting, cysts and lesions can be segmented from the medical images [32].…”

Section: Defect Localization and Segmentation Through Active Contoursmentioning

confidence: 99%

Nondestructive inspection of aerospace composite laminate using thermal image processing

2020

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In this work, a novel image segmentation method in thermographic images using the computer vision algorithm, active contours, is presented. These studies were done on thermal images obtained from pulse thermography. Many aerospace components are made of composite laminates. De-bonds/de-laminations set in during the manufacturing or in-service stages of these materials. Identification of de-bonds in composite laminates is a difficult task and faces many challenges when it is done through the processing of thermographic images. The first challenge is to get an accurate seed point to be used in active contours. In this study, the seed points for active contours were obtained from the moving averages of the pixel values taken along the region of interest lines. It is shown that the active contours with the help of seed points identified through this method are able to distinguish the de-bonds. The experimental results, supported by segmentation metrics, suggest that the segmentation method used here outperforms some earlier results which were based on the combination of Gabor filters, watershed models and divide-and-conquer methods such as subdivision of images.

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“…The Gaussian noise manipulates all the pixel values. The Gaussian Probability density function is used to value each pixel in the image [14].The outcome pixels of the gaussian filter is smoothed pixels according to the power-of-two Gaussian coefficients. The smoothing function can be expressed as in equation 1…”

Section: Gaussian Filtermentioning

confidence: 99%

Research on SVM and KNN Classifiers for Skin Cancer Detection

Murugan,

Nair,

Kumar

2019

IJEAT

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Generally, a not unusual skin ailment in human disorder. In laptop imaginative and prescient applications, coloration is a sturdy indication for this sickness. This machine identifies pores and skin cancer based totally on the picture of the pores and skin. Initially, the skin image is filtered using filters and segmented Gausian the use of energetic contour segmentation. Segmented pix are fed as an input to the feature extraction. Pictures extracted classified the use of class strategies such as Support Vector Machine classifiers(SVM) and k Nearest Neighbor(kNN) classifiers. SVM classifier provided better results than kNN classifier

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Active Contour Based Segmentation Techniques for Medical Image Analysis

Cited by 86 publications

References 33 publications

Optic Disc and Optic Cup Segmentation Methodology for Glaucoma Detection

Optic Disc and Optic Cup Segmentation Methodology for Glaucoma Detection

Nondestructive inspection of aerospace composite laminate using thermal image processing

Research on SVM and KNN Classifiers for Skin Cancer Detection

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