Flooding region growing: a new parallel image segmentation model based on membrane computing

Dalvand, Mehran; Fathi, Abdolhossein; Kamran, Arezoo

doi:10.1007/s11554-020-00949-0

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…By focusing on defining automatic seed selection methods for medical imagery segmentation, they introduced different region-growing algorithms for image segmentation with automatic seed selection. As a part of the developments that incorporate the edge and color modalities for region growing, Dalvand et al [12] proposed a tissue-like P system to automatically select the initial seeds in complicated backgrounds. The seeded region growing segmentation method was applied and tested on complicated backgrounds, moreover, CUDA programming language and Graphic Processing Unit (GPU) are utilized to construct the suggested model in parallel.…”

Section: Related Workmentioning

confidence: 99%

Higher Order Textural Statistics for Object Segmentation in Unconstrained Environments

Albalooshi¹

2023

IJCDS

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This paper presents an object segmentation technique that builds on the success of Seeded-Region Growing (SRG) segmentation. SRG methods are typically initialized by a single point or patch in the image that represents the object of interest. Unlike previous approaches which utilize patches of the object of interest to obtain first and second-order characteristics, the author explores the potential of higher-order textural statistical descriptors. The proposed unsupervised approach relies on both the homogeneous and heterogeneous textural characteristics of the selected object region to iteratively expand the boundary to encompass the full object. In addition, the research proposes a dynamic selection criterion for determining segmentation parameters based on patch neighborhood features. The presented experiments are conducted in unconstrained environments wherein a textural description of the object of interest is extracted and the proposed algorithm automatically segments it from the background and other captured objects in the scene. The approach is evaluated using various subsets of the PASCAL Visual Object Classes (VOC) challenge imagery. Through quantitative metrics and analysis, the proposed algorithmic framework outperforms state-of-the-art methods for segmenting objects with non-homogeneous textural descriptors from complex real-world environments.

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Section: Related Workmentioning

confidence: 99%

Higher Order Textural Statistics for Object Segmentation in Unconstrained Environments

Albalooshi¹

2023

IJCDS

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“…Approach to parallel programming model with hybrid model on CPU -GPU (Agulleiro et al, 2012) is a powerful co-processor system because the CPU and GPU have the combined properties of using both types of additional processors allowing for the execution of many large applications for optimal performance. Specifically, OpenMP, CUDA, and MPI libraries are used on CPUs and GPUs (Sirotković et al, 2012;Baker and Balhaf, 2017;Fakhi et al, 2017;Dalvand et al, 2020;Wang N. et al, 2020). Clustering in a multi-core architecture starts with dividing the image data into regions in a grid pattern, and then parallelizes the segmentation over the regions.…”

Section: Introductionmentioning

confidence: 99%

A proposed scenario to improve the Ncut algorithm in segmentation

Tran

Hieu²,

Bao³

2023

Front. Big Data

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In image segmentation, there are many methods to accomplish the result of segmenting an image into k clusters. However, the number of clusters k is always defined before running the process. It is defined by some observation or knowledge based on the application. In this paper, we propose a new scenario in order to define the value k clusters automatically using histogram information. This scenario is applied to Ncut algorithm and speeds up the running time by using CUDA language to parallel computing in GPU. The Ncut is improved in four steps: determination of number of clusters in segmentation, computing the similarity matrix W, computing the similarity matrix's eigenvalues, and grouping on the Fuzzy C-Means (FCM) clustering algorithm. Some experimental results are shown to prove that our scenario is 20 times faster than the Ncut algorithm while keeping the same accuracy.

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“…The other predominantly used segmentation technique is through the region growing ( Dalvand, Fathi & Kamran, 2020 ) strategy that can effectively handle the problem of over and under segmentation often encountered in K-Means-based approaches. The experimental studies on the region growing-based approach are proven to improve the sensitivity and specificity for precise identification of the malignant region in the human brain ( Punitha, Amuthan & Joseph, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

Srinivasu

Balas

2021

PeerJ Computer Science

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In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.

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Flooding region growing: a new parallel image segmentation model based on membrane computing

Cited by 10 publications

References 37 publications

Higher Order Textural Statistics for Object Segmentation in Unconstrained Environments

Higher Order Textural Statistics for Object Segmentation in Unconstrained Environments

A proposed scenario to improve the Ncut algorithm in segmentation

Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

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