Automated colour segmentation of Tuberculosis bacteria thru region growing: A novel approach

Chayadevi, M. L.; Raju, G. T.

doi:10.1109/icadiwt.2014.6814682

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…Canny is used as the basic step of the segmentation part in [12]; In [13], in order to be able to segment large size images of zooplanktons, a segmentation (based on Active Contour) and pre-classification algorithm is used where after acquisition of images. Region based methods: In [14], the segmentation is performed on gray level images using marker controlled watershed method; In [15], after converting the color mode and using morphological operations to denoise, seeded regiongrowing watershed algorithm is applied for segmentation.…”

Section: A Existing Microorganism Segmentation Methodsmentioning

confidence: 99%

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

Zhang,

Li,

Kulwa

et al. 2020

Preprint

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In order to assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multi-scale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely "mU-Net-B3", with a dense Conditional Random Field (CRF) post-processing. The second is a VGG-16 based patch-level segmentation method with a novel "buffer" strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27% and 96.76% to 87.13%, 79.74%, 87.12% and 96.91% respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows a big potential in the EM segmentation field.

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Section: A Existing Microorganism Segmentation Methodsmentioning

confidence: 99%

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

Zhang,

Li,

Kulwa

et al. 2020

Preprint

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show abstract

“…10. A contour based seed region growing watershed technique is used to segment TB bacteria from their background in [65]. Before segmentation, color conversion to Lab color mode and denoising using image filling holes operations are applied.…”

Section: B Region Based Segmentation (Rbs)mentioning

confidence: 99%

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

Kulwa

Zhao

et al. 2019

IEEE Access

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Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium. In such course, it is important to identify, track, analyze, consider the beneficial side and get rid of the negative effects of microorganisms using fast, accurate, and reliable methods. In recent decades, image analysis techniques have been used to address the drawbacks of manual traditional approaches in the identification and analysis of microorganisms. As image segmentation being an important step (technique) in the detection, tracking, monitoring, feature extraction, modeling, and analysis of microorganisms, different methods have been deployed, from classical approaches to current deep neural networks upon different challenges on microorganism images. This survey comprehensively analyses the various studies focused on developing microorganism image segmentation methods in the last 30 years (since 1989). In this survey, segmentation methods are categorized into classical and machine learning methods. Furthermore, these methods are subcategorized into threshold-based, regionbased, and edge-based which belong to classical methods, supervised and unsupervised machine leaningbased methods which belong to machine learning category. A growth trend of different methods and most frequently used methods in each category are meticulously analyzed. A clear explanation of the suitability of these methods for different segmentation challenges encountered on microscopic microorganism images is also enlightened. INDEX TERMSMicroorganism segmentation, content-based microscopic image analysis, feature extraction, microscopic images, classical methods, machine learning.

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“…Canny is used as the basic step of the segmentation part in [12]; in [13], to be able to segment large size images of zoo-planktons, a segmentation (based on Active Contour) and preclassification algorithm is used after the acquisition of images. Region-based methods: In [14], the segmentation is performed on gray-level images using marker controlled watershed method; in [15], after converting the colour mode and using morphological operations to denoise, seeded region-growing watershed algorithm is applied for segmentation.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale CNN‐CRF Framework for Environmental Microorganism Image Segmentation

Jing-hua

Kulwa

et al. 2020

BioMed Research International

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To assist researchers to identify Environmental Microorganisms (EMs) effectively, a Multiscale CNN-CRF (MSCC) framework for the EM image segmentation is proposed in this paper. There are two parts in this framework: The first is a novel pixel-level segmentation approach, using a newly introduced Convolutional Neural Network (CNN), namely, “mU-Net-B3”, with a dense Conditional Random Field (CRF) postprocessing. The second is a VGG-16 based patch-level segmentation method with a novel “buffer” strategy, which further improves the segmentation quality of the details of the EMs. In the experiment, compared with the state-of-the-art methods on 420 EM images, the proposed MSCC method reduces the memory requirement from 355 MB to 103 MB, improves the overall evaluation indexes (Dice, Jaccard, Recall, Accuracy) from 85.24%, 77.42%, 82.27%, and 96.76% to 87.13%, 79.74%, 87.12%, and 96.91%, respectively, and reduces the volume overlap error from 22.58% to 20.26%. Therefore, the MSCC method shows great potential in the EM segmentation field.

show abstract

Automated colour segmentation of Tuberculosis bacteria thru region growing: A novel approach

Cited by 8 publications

References 8 publications

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

A Multi-scale CNN-CRF Framework for Environmental Microorganism Image Segmentation

A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential

A Multiscale CNN‐CRF Framework for Environmental Microorganism Image Segmentation

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