Extraction of Bacterial Clusters from Digital Microscopic Images through Statistical and Neural Network Approaches

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

doi:10.1007/978-81-322-0740-5_133

Cited by 9 publications

(4 citation statements)

References 7 publications

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“…The dataset used in [10] contains 150 colour images with positive and negative examples of leukocyte and epithelial cells. Another dataset presented in [9] contains 320 stained slide images not only using the Gram stain technique but also other different staining techniques. The problem addressed in [9] is the automatic detection of microbes and the extraction of bacterial clusters.…”

Section: A Novel Fine-grained Gram Stain Datasetmentioning

confidence: 99%

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SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

Zhang

Carvajal

Smith

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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This work tackles the automatic fine-grained slide quality assessment problem for digitized direct smears test using the Gram staining protocol. Automatic quality assessment can provide useful information for the pathologists and the whole digital pathology workflow. For instance, if the system found a slide to have a low staining quality, it could send a request to the automatic slide preparation system to remake the slide. If the system detects severe damage in the slides, it could notify the experts that manual microscope reading may be required. In order to address the quality assessment problem, we propose a deep neural network based framework to automatically assess the slide quality in a semantic way. Specifically, the first step of our framework is to perform dense fine-grained region classification on the whole slide and calculate the region distribution histogram. Next, our framework will generate assessments of the slide quality from various perspectives: staining quality, information density, damage level and which regions are more valuable for subsequent high-magnification analysis. To make the information more accessible, we present our results in the form of a heat map and text summaries. Additionally, in order to stimulate research in this direction, we propose a novel dataset for slide quality assessment. Experiments show that the proposed framework outperforms recent related works.

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Section: A Novel Fine-grained Gram Stain Datasetmentioning

confidence: 99%

“…In the scope of Gram stain test, most related works [9][10] [11] only aim to detect and count the existence of different cells and bacteria. Despite the fact that progress has been made, these works assume that the areas of interest have been pre-selected by experts.…”

Section: Automation In Microscopic Medical Image Analysismentioning

confidence: 99%

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

Zhang

Carvajal

Smith

et al. 2018

2018 24th International Conference on Pattern Recognition (ICPR)

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“…Chayadevi et al . () employd K‐means and neural network‐based clustering technique SOM‐Kmeans to classification and clusters extraction from bacterial images. Makkapati et al .…”

Section: Introductionmentioning

confidence: 99%

“…Forero et al (2004) used shape and colour features and decision based on classification tree to identification of tuberculosis bacteria. Chayadevi et al (2013) employd Kmeans and neural network-based clustering technique SOM-Kmeans to classification and clusters extraction from bacterial images. Makkapati et al (2009) did segmentation and classification of tuberculosis bacilli from ZN-stained sputum smear images using thresholding the hue component by choosing an appropriate range adaptively based on the input image.…”

Section: Introductionmentioning

confidence: 99%

Automatic diagnosis of vulvovaginal candidiasis from Pap smear images

Momenzadeh

Sehhati

Dehnavi

et al. 2017

Journal of Microscopy

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Vulvovaginal candidiasis (VVC) is the most common genital infections that are seen every day in clinics. This infection is due to excessive growth of Candida that are normally present in the vagina in small numbers. Diagnosis of VVC is routinely done by direct microscopy of Pap smear samples and searching for the Candida in the Pap smear glass slides. This manual method is subjective, time consuming, labour-intensive and tedious. This study presents a computer-aided diagnostic (CAD) method to improve human diagnosis of VVC. The proposed CAD method reduces the diagnostic time and also can be worked as a second objective opinion for pathologists. Our main objective is detection and extraction of mycelium and conidium of Candida fungus from microscopic images of Pap smear samples. In this regard, the proposed method is composed of three main phases, namely preprocessing, segmentation, feature extraction and classification. At the first phase, bottom-hat filtering is used for elimination of the cervical cells and separating the background. Then decorrelation stretching and colour K-means clustering are used for Candida segmentation. Finally the extracted features used by a decision tree classifier to detect Candida from other parts of smear. The proposed method was evaluated on 200 Pap smear images and showed specificity of 99.83% and 99.62% and sensitivity of 92.18% and 94.53% for detection of mycelium and conidium, respectively.

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Harnessing deep learning for faster water quality assessment: identifying bacterial contaminants in real time

Khokhar,

Shah,

Saleem

et al. 2024

Vis Comput

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Water is essential for human survival. Humans can live without food for a few days but without water, a person can barely survive for 3–5 days. Various parts of the world, particularly under-developed countries, have regions where clean water is scarce, and humans living in such conditions have no access to clean water. Our solution provides information on whether water is contaminated or not. Moreover, it overcomes the delay time in getting the result of water contamination using traditional methods of up to 5–6 hrs. Our proposed method detects the colonies of the bacteria that are taken from the water sample (after gram staining) and then classifies the type of bacteria to whom it belongs and how much quantity of each bacterium causes infection to the human body. Bacteria detection is performed by a novel deep learning-based model with user-specified parameters. To improve our ability to detect dangerous bacteria including E. coli, yeast, and particles, we perform tests using datasets from a variety of researchers. On the test benchmark, the fine-tuned proposed model achieves 84.56% accuracy and provides the level of contamination in water.

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Extraction of Bacterial Clusters from Digital Microscopic Images through Statistical and Neural Network Approaches

Cited by 9 publications

References 7 publications

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

SlideNet: Fast and Accurate Slide Quality Assessment Based on Deep Neural Networks

Automatic diagnosis of vulvovaginal candidiasis from Pap smear images

Harnessing deep learning for faster water quality assessment: identifying bacterial contaminants in real time

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