The Image Segmentation Techniques

Gehlot, Shiv; Kumar, John Deva

doi:10.5815/ijigsp.2017.02.02

Cited by 5 publications

(4 citation statements)

References 17 publications

(10 reference statements)

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“…Although RG is a simple and efficient method, the computation time of this method increases if the initial points are not chosen suitably [11,12].…”

Section: A Region Growing Methodsmentioning

confidence: 99%

“…This method computes the area and perimeter of binary patterns based on matching the logical state of image regions to binary patterns. In addition, there are evaluable studies which propose and utilize segmentation techniques in biomedical image In [11], authors emphasize the importance of image segmentation process. They propose two algorithms: normalized self-correlation function (NSCF) based algorithm and piecewise image segmentation (PIS) algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Breast Lesion Segmentation and Area Calculation for MR Images

Çetinel¹,

Gül²

2018

IJIGSP

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In this paper, our goal is to determine the boundaries of lesion and then calculate the area of existing lesion in breast magnetic resonance (MR) images to provide a useful information to the radiologists. For this purpose, at first stage region growing (RG) method and active contour model (Snake) is applied to the images to make the boundaries of lesion visible. RG method is one of the simplest approaches for image segmentation and provides accurate results with lower computation time due to its seed point initialization step. Snake method molds a closed contour to the boundary of a region in an image and is also popular in medical image segmentation studies. In the presented study, both of these methods are utilized to determine the lesion boundaries. After determining the boundaries of lesion accurately in the second stage of the study, bit-quad method is applied to the segmented images. Bit quad method is used to compute the area and perimeter of binary lesion images based on matching the logical state of regions of image to binary patterns. Finally, to evaluate the performance of the proposed study, computer simulations are performed. It is demonstrated via computer simulations that the lesion area and parameter values are very close to real values. By means of this study it is aimed to support radiologists during diagnosis and assessment of breast lesions.

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“…Although RG is a simple and efficient method, the computation time of this method increases if the initial points are not chosen suitably [11,12].…”

Section: A Region Growing Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breast Lesion Segmentation and Area Calculation for MR Images

Çetinel¹,

Gül²

2018

IJIGSP

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“…The overall performance of Sobel and Prewitt operator is same except the kernel [14]. Prewitt have a different convolution kernel with number of orientations limited to 8 [17,19]. In this operator 3x3 neighborhood for 8 directions is calculated and convolution mask with highest component is chosen.…”

Section: Segmentationmentioning

confidence: 99%

Study of Segmentation Techniques for Assessment of Osteoarthritis in Knee X-ray Images

Gornale¹,

Patravali²,

Uppin³

et al. 2019

IJIGSP

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Arthritis is one of the chronic joint disorders that have affected many lives including middle age and older age group. Arthritis exists in many forms and one among them is Osteoarthritis. Osteoarthritis affects the bigger joints like knee, hip, spine, feet etc. Early detection of Osteoarthritis is most essential if not treated properly may result in deformity. The researchers have become more concerned to detect the disorder in the early stage by merging their medical knowledge with machine vision approach in an appropriate way. The objective of this work is to study various segmentation techniques for the detection of Osteoarthritis in the early stage. The different segmentation technique like Sobel and Prewitt edge segmentation, Otsu's method of segmentation and Texture based segmentation are used to carry out the experimentation. The different statistical features are computed, analyzed and classified. The accuracy rate of 91.16% for Sobel method, 96.80% for Otsu's method, 94.92% for texture method and 97.55% for Prewitt method is obtained. The results are more promising and competitive which are validated by medical experts.

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“…Also, traditional image segmentation wastes a lot of time during processing which reduces the performance accuracy of image measurements. In recent times, researchers have moved from traditional methods of Image segmentation to more advanced methods of image segmentation such as edge based detection, thresholding, region growing and deformable model [2]. These methods help to partition images into numerous parts based on some specific features such as intensity value, color, texture and etc.…”

Section: Introductionmentioning

confidence: 99%