Learning-Based multilabel random walks for image segmentation containing translucent overlapped objects

Mahyari, Tayebeh Lotfi; Dansereau, Richard M.

doi:10.1109/tsp.2017.8076059

Cited by 2 publications

(13 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Having the probabilistic image maps for image I from the residual CNN as well as the confident pixels as seeds {C l , C b } for the nuclei location layer output from (4) and (5), we first create an image graph G = (V ,  ) for the input I [33]. Then, we use a method similar to what we proposed in [34] to obtain Pr…”

Section: Nuclei-seeded Multi-layer Random Walker Image Segmentationmentioning

confidence: 99%

Multi‐layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods

Mahyari

Dansereau

2022

IET Image Processing

Self Cite

View full text Add to dashboard Cite

A method for overlapping cell image segmentation is presented with a focus on multilayer image processing in a three-phase scheme. In the first phase, a convolutional neural network is developed to provide a coarse cell segmentation with multiple output layers to identify cell cytoplasm, locations of cell nuclei, and the background, all as probabilistic image maps for the layer outputs. In the second phase, the probabilistic image maps from the convolutional neural network are used to identify locations of cell nuclei and cell cytoplasm. Then, multi-layer random walker image segmentation is used with cell nuclei as hard initial seeds and the cytoplasm estimates as soft seeds in a diffusion graph-based segmentation of the cells. With rough cell segmentation from both the trained convolutional neural network and the multi-layer random walker graph-based technique, a third phase combines and refines the cell segmentation using the Hungarian algorithm to optimise the assignment of individual pixel locations for the final cell segmentation. We evaluate the proposed method on cervical cell images generated from the International Symposium on Biomedical Imaging 2014 dataset with results that give a Dice similarity coefficient of 97.2% (compared to 93.2% for competitors) when trained on the generated dataset.

show abstract

Section: Nuclei-seeded Multi-layer Random Walker Image Segmentationmentioning

confidence: 99%

Multi‐layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods

Mahyari

Dansereau

2022

IET Image Processing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Therefore, we extend our work to remove the need for the user-defined seeds. We present a novel supervised image segmentation method that uses data priors instead of seeds [45]. In the training phase, for each selected pixel on nonoverlapped regions, we create a 5 × 5 window centered at that pixel and generate a 25D vector of image intensity values.…”

Section: Discussionmentioning

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%

“…5.3 uses residual connections to allow a deeper network that will still converge when trained. The proposed residual CNN is a modified version of the CNN used in our previous work in [45] and is augmented to produce the probabilistic image maps.…”

Section: Proposed Residual Cnnmentioning

confidence: 99%

“…. , N , B b we then select the seeds and apply a multi-layer random walker image segmentation algorithm [45] to extract the approximate cytoplasm candidates. The details are explained in the following paragraphs.…”

Section: Nuclei and Approximate Cytoplasm Detectionmentioning

confidence: 99%

See 2 more Smart Citations

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

Learning-Based multilabel random walks for image segmentation containing translucent overlapped objects

Cited by 2 publications

References 13 publications

Multi‐layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods

Multi‐layer random walker image segmentation for overlapped cervical cells using probabilistic deep learning methods

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

Contact Info

Product

Resources

About