Random walks for image segmentation containing translucent overlapped objects

Mahyari, Tayebeh Lotfi; Dansereau, Richard M.

doi:10.1109/globalsip.2017.8308601

Cited by 5 publications

(15 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mahyari's research [15] segment the object containing translucent overlapped regions in synthetic images using random walker algorithm. They segment the translucent overlapping object based on random walker method.…”

Section: Related Workmentioning

confidence: 99%

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

Adam

Arifin

2020

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

To extract features on dental objects, it is necessary to segment the teeth. Segmentation is separating between the teeth (objects) with another part than teeth (background). The process of segmenting individual teeth has done a lot of the recently research and obtained good results. However, when faced with overlapping teeth, this is quite challenging. Overlapping tooth segmentation using the latest algorithm produces an object that should be segmented into two objects, instantly becoming one object. This is due to the overlapping between two teeth. To separate overlapping teeth, it is necessary to extract the overlapping object first. Level set method is widely used to segment overlap objects, but it has a limitation that needs to define the initial level set method manually by the user. In this study, an automatic initialization strategy is proposed for the level set method to segment overlapping teeth using hierarchical cluster analysis on dental panoramic radiographs images. The proposed strategy was able to initialize overlapping objects properly with accuracy of 73%. Evaluation to measure quality of segmentation result are using misscassification error (ME) and relative foreground area error (RAE). ME and RAE were calculated based on the average results of individual tooth segmentation and obtain 16.41% and 52.14%, respectively. This proposed strategy are expected to be able to help separate the overlapping teeth for human age estimation through dental images in forensic odontology.

show abstract

Section: Related Workmentioning

confidence: 99%

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

Adam

Arifin

2020

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

show abstract

“…Mahyari & Dansereau (2017) conducted the segmentation of object containing translucent overlapped regions in synthetic images using random walker algorithm [12]. In that process, the multi-layer graph is made from 2-dimension images containing translucent overlapping area and generate a Laplacian matrix based on a multi-layer graph.…”

Section: Related Workmentioning

confidence: 99%

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Arifin¹,

Adam²,

Mohammad³

et al. 2019

IJIES

View full text Add to dashboard Cite

Segmentation of single tooth in dental panoramic images is an important process to extract its features and information. However, it might be challenging when the segmentation process faces an overlapping teeth image. In this research, we introduce a new strategy for detecting overlapping area on dental panoramic radiographs automatically. This research proposes automatic thresholding to obtain marking points for the overlapping area and an automatic selection of overlapping area candidates by using the area orientation and the similarity of neighborhood intensity. The experimental results on 44 images show that our proposed strategy can detect overlapping teeth on the dental panoramic radiograph with accuracy, sensitivity, and specificity of 75%, 66.67%, and 85%, respectively. The evaluation conducted on 24 overlapping teeth images shows that the segmentation results of overlapping teeth area have an average misclassification error of 0.31%.

show abstract

“…Initial seeds that are used for the second, third and last columns are shown as red points in the original image. Segmentation results for [1] and [3] with one seed for each segment (second and third columns, respectively), learning-based multi-layer random walker image segmentation method without seeds (fourth column), and learning-based multi-layer random walker image segmentation method with one seed for each segment (last column). .…”

Section: 6mentioning

confidence: 99%

“…Starting with a residual UNet shape CNN (Appendices A.1,A.2), we create a probabilistic map of the nuclei, cytoplasms, and background for the input image [44]. A multi-layer random walker image segmentation method [3,45] is then applied for nuclei-based region growing. The cytoplasm borders are then refined using nuclei-based extracted regions and CNN-based cytoplasm candidates [46].…”

Section: Contributionmentioning

confidence: 99%

See 1 more Smart Citation

Image Separation using Multi-layer Image Segmentation for Translucent Partially Overlapped Objects

Mahyari¹

View full text Add to dashboard Cite

One way to solve under-determined image separation is to use statistical information about the type of data to be decomposed. In this dissertation, we propose a two stage method for cervical cell separation. In the first stage, we propose a CNN-based multi-layer random walker image segmentation method. The results of image segmentation at the first stage are then used as the side information for the cervical cell separation in the second stage.Convolutional neural networks (CNNs) are recently used in computer vision applications such as image segmentation. One of the biggest advantages of CNNs is that they extract important features automatically from the data. However, CNNs usually use a high number of data for training but it is not always possible to find enough training data for some applications. Also, CNNs are good at generalizing the training, but not for finding the accurate edges of cervical cells at the current implementations for cervical cell segmentation.One solution to improve CNN segmentation results is to use a post-processing method as edge refinement. Random walker image segmentation method is a graph-based image segmentation method that is good at region growing; so, it can be used as the refinement step. However, random walker is sensitive to initial setup, so if the seeds are not extracted correctly, the final segmentation results will be poor. In this dissertation we use a CNN-based random walker image segmentation approach for cervical cell segmentation. Different from other methods that use CNN binary segmentation results for fine tuning, CNN probabilistic map is utilized to guide random walker image segmentation method at the refinement step.

show abstract

Random walks for image segmentation containing translucent overlapped objects

Cited by 5 publications

References 10 publications

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

Separation of Overlapping Object Segmentation Using Level Set With Automatic Initalization on Dental Panoramic Radiograph

Detection of Overlapping Teeth on Dental Panoramic Radiograph

Image Separation using Multi-layer Image Segmentation for Translucent Partially Overlapped Objects

Contact Info

Product

Resources

About