Histogram Thresholding in Image Segmentation: A Joint Level Set Method and Lattice Boltzmann Method Based Approach

Kumar, Ram; Talukdar, Fazal A.; Dey, Nilanjan; Ashour, Amira S.; Santhi, V.; Balas, Valentina E.; Shi, Fuqian

doi:10.1007/978-3-319-38771-0_52

Cited by 13 publications

(9 citation statements)

References 28 publications

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“…The image processing literature confirms that, heuristic algorithm approach is efficient in analysing the traditional and medical images [5][6][7][8][9][10]. Previous works also confirms that, a number of image processing techniques are proposed by the researchers to investigate the cancer with the help of clinical images [11].…”

Section: Introductionmentioning

confidence: 71%

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

S¹,

Madhumathi²,

P³

et al. 2017

IJET

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Section: Introductionmentioning

confidence: 71%

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

S¹,

Madhumathi²,

P³

et al. 2017

IJET

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“…It provides the best recall of ideal value equal one in the case of the dark background with bright objects. Since, segmentation has a significant role in several applications [21][22][23][24][25][26][27][28][29][30], Thus, it is recommended to apply the proposed method in the medical domain applications as a future work.…”

Section: Resultsmentioning

confidence: 99%

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

Hore¹,

Chakraborty²,

Chatterjee³

et al. 2016

IJECE

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<p>Image segmentation is a challenging process in numerous applications. Region growing is one of the segmentation techniques as a basis for the Seeded Region Growing method. A novel real time integrated method was developed in the current work to locate the segmented region of interest of an image based on the Region Growing segmentation method along with the thresholding supported image segmentation. Through the proposed work, a homogeneity based on pixel intensity was suggested as well as the threshold value can be decided via a variety of schemes such as manual selection, Iterative method, Otsu’s method, local thresholding to obtain the best possible threshold. The experimental results were performed on different images obtained from an Alpert dataset. A comparative study was arried out with the human segmented image, threshold based region growing, and the proposed integrated method. The results established that the proposed integrated method outperformed the region growing method in terms of the recall and F-score. Although, it had comparable recall values with that gained by the human segmented images. It was noted that as the image under test had a dark background with the brighter object, thus the proposed method provided the superior recall value compared to the other methods.</p>

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“…It handles segmentation by performing geometric operations to detect contours with topology changes. Examples of works on boundary-based segmentation are [ 54 , 55 ], which used LSM to segment radiographic images. Watershed-based: it is performed on a grayscale image and used mathematical morphology to segment adjacent regions in an image; watershed-based segmentation was used by [ 56 ] on bitewing dental radiographs.…”

Section: Image Representation For Dental Segmentation and Detectionmentioning

confidence: 99%

A Survey of Dental Caries Segmentation and Detection Techniques

Majanga

Viriri

2022

The Scientific World Journal

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Dental caries detection, in the past, has been a challenging task given the amount of information got from various radiographic images. Several methods have been introduced to improve the quality of images for faster caries detection. Deep learning has become the methodology of choice when it comes to analysis of medical images. This survey gives an in-depth look into the use of deep learning for object detection, segmentation, and classification. It further looks into literature on segmentation and detection methods of dental images through deep learning. From the literature studied, we found out that methods were grouped according to the type of dental caries (proximal, enamel), type of X-ray images used (extraoral, intraoral), and segmentation method (threshold-based, cluster-based, boundary-based, and region-based). From the works reviewed, the main focus has been found to be on threshold-based segmentation methods. Most of the reviewed papers have preferred the use of intraoral X-ray images over extraoral X-ray images to perform segmentation on dental images of already isolated parts of the teeth. This paper presents an in-depth analysis of recent research in deep learning for dental caries segmentation and detection. It involves discussing the methods and algorithms used in segmenting and detecting dental caries. It also discusses various existing models used and how they compare with each other in terms of system performance and evaluation. We also discuss the limitations of these methods, as well as future perspectives on how to improve their performance.

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Histogram Thresholding in Image Segmentation: A Joint Level Set Method and Lattice Boltzmann Method Based Approach

Cited by 13 publications

References 28 publications

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

A Soft-computing Assisted Tool to Detect and Analyse Brain Tumor

An Integrated Interactive Technique for Image Segmentation using Stack based Seeded Region Growing and Thresholding

A Survey of Dental Caries Segmentation and Detection Techniques

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