Automatic detection and classification of tuberculosis bacilli from ZN-stained sputum smear images using watershed segmentation

Shah, Mohammad Imran; Mishra, Shailendra; Sarkar, Malay; Rout, Chittaranjan

doi:10.1049/cp.2016.1459

Cited by 10 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…33,34 The sensitivity and specificity of this method for classifying a medium density background image as tuberculosis positive or negative were 100% and 93%, respectively, while for a high-density background the sensitivity remained unchanged, but specificity was reduced to 72% due to over-staining and artifacts. 33 Similarly, the sensitivity and specificity of this segmentation method for classifying a smartphone enabled microscopic images (medium to high-density background) as tuberculosis bacilli positive or negative were 93.3% and 87%, respectively. 34 The sensitivity, specificity, and precision rate of watershed segmentation on different infection levels (Scanty, 1+, 2+, and 3+) are depicted in Table 3.…”

Section: Bacilli Segmentation and Classificationmentioning

confidence: 94%

See 1 more Smart Citation

Ziehl–Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis

et al. 2017

View full text Add to dashboard Cite

Ziehl-Neelsen stained microscopy is a crucial bacteriological test for tuberculosis detection, but its sensitivity is poor. According to the World Health Organization (WHO) recommendation, 300 viewfields should be analyzed to augment sensitivity, but only a few viewfields are examined due to patient load. Therefore, tuberculosis diagnosis through automated capture of the focused image (autofocusing), stitching of viewfields to form mosaics (autostitching), and automatic bacilli segmentation (grading) can significantly improve the sensitivity. However, the lack of unified datasets impedes the development of robust algorithms in these three domains. Therefore, the Ziehl-Neelsen sputum smear microscopy image database (ZNSM iDB) has been developed, and is freely available. This database contains seven categories of diverse datasets acquired from three different bright-field microscopes. Datasets related to autofocusing, autostitching, and manually segmenting bacilli can be used for developing algorithms, whereas the other four datasets are provided to streamline the sensitivity and specificity. All three categories of datasets were validated using different automated algorithms. As images available in this database have distinctive presentations with high noise and artifacts, this referral resource can also be used for the validation of robust detection algorithms. The ZNSM-iDB also assists for the development of methods in automated microscopy.

show abstract

Section: Bacilli Segmentation and Classificationmentioning

confidence: 94%

“…The shape and size of the objects were determined to filter the true bacilli. 33 Similarly, the watershed algorithm was implemented on 30 randomly extracted images from the smartphone enabled microscope (MS-3) to segment bacilli. 34 In both studies, sensitivity and specificity of the watershed algorithm were calculated.…”

Section: Data Validationmentioning

confidence: 99%

Ziehl–Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis

et al. 2017

View full text Add to dashboard Cite

show abstract

“…Alguns slides podem ser difíceis de se analisar porque alguns componentes não bacterianos se assemelham às células Mtb [Zachariou et al 2022]. Dessa forma, a sensibilidade do resultado depende da experiência do especialista [Shah et al 2016]. Diante disso, a automatizac ¸ão da análise das imagens de lâminas para a detecc ¸ão e contagem dos bacilos pode auxiliar o bacteriologista no processo diagnóstico, tornando-o mais ágil, menos cansativo e menos propenso a erros.…”

Section: Introduc ¸ãOunclassified

EfficientBacillus: uma arquitetura profunda para detecção dos bacilos de Koch

Santos,

Brito,

Carvalho Filho

et al. 2023

Anais Do XXIII Simpósio Brasileiro De Computação Aplicada À Saúde (SBCAS 2023)

View full text Add to dashboard Cite

A tuberculose é uma infecção bacteriana causada pelo bacilo de Koch e é transmitida pelo ar. A doença afeta principalmente os pulmões e é considerada a segunda principal causa de morte por infecção no mundo. Apesar disso, a tuberculose tem cura e o diagnóstico precoce é de suma importância para o sucesso do tratamento e para evitar a disseminação da doença. Tradicionalmente, a baciloscopia de esfregaço de escarro tem sido o principal método para o diagnóstico e monitoramento do tratamento da tuberculose. Nesse contexto, diversas abordagens computacionais têm sido desenvolvidas para auxiliar no diagnóstico da tuberculose, através da análise das imagens de baciloscopia. Neste trabalho, propomos a utilização da EfficientDet, explorando cada um dos seus backbones na tarefa de identificação dos bacilos. Também avaliamos 4 representações de cor diferentes e aplicamos uma validação cruzada com k-fold = 5. Os resultados mostraram-se promissores, com IoU de 0,523, recall de 0,925, precision de 0,694 e f1-score de 0,774. Os resultados alcançados evidenciaram o potencial do método na detecção dos bacilos, podendo auxiliar no diagnóstico da tuberculose.

show abstract

“…Based on the table above, the success of the colour space method in detecting TB bacteria was able to identify two groups of High-Density Background and Low-Density Background images. While the watershed method can segment bacteria even though their positions are randomly distributed, this method has a sensitivity of 90.3% and a precision of 70% [4][5] [6][7] [8].So the watershed method will be tested with the colour space method in preprocessing and the watershed method in bacterial segmentation because the watershed method can be detection random object and identification object.…”

Section: Introductionmentioning

confidence: 99%

TBC Bacteria Detection in Microscopic Image With Watershed Countur Method

Dawwas¹

2022

CEPAT

View full text Add to dashboard Cite

Tuberculosis (TB) is an infectious disease that can be detected using a sputum sample. TB cases in Indonesia have spread throughout the region; the highest cases are in West Java. This problem makes the government do some handling and prevention of TB disease. The Bandung City Health Office (DKKB) conducted a cross-test to diagnose TB using a sputum sample. So in this study, a TB bacteria detection system, namely Mycobacterium Tuberculosis (MTB), will be made in sputum samples and their number to diagnose TB. Detection and calculation of the number of MTB are done by processing the image on the sputum sample using the watershed contour detection method. In this study, sputum sample data were obtained from DKKB. The acquisition of microscopic images at each point of the field of view is carried out using an SLR camera connected directly to the microscope to replace the function of the ocular lens. In this study, the microscopic sputum sample images were classified into positive and negative using the watershed and colorspace methods and were tested on a total of 90 microscopic images. From the system testing results, the system accuracy level is 100%, and the system precision is 100% for the detection of TB diagnosis. The system processing time averaged 5.811 seconds for 90 images used.

show abstract

Automatic detection and classification of tuberculosis bacilli from ZN-stained sputum smear images using watershed segmentation

Cited by 10 publications

References 0 publications

Ziehl–Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis

Ziehl–Neelsen sputum smear microscopy image database: a resource to facilitate automated bacilli detection for tuberculosis diagnosis

EfficientBacillus: uma arquitetura profunda para detecção dos bacilos de Koch

TBC Bacteria Detection in Microscopic Image With Watershed Countur Method

Contact Info

Product

Resources

About