Automatic detection and classification of tuberculosis bacilli from camera-enabled smartphone microscopic images

Shah, Mohammad Imran; Mishra, Shailendra; Sarkar, Malay; Sudarshan, Sanjana

doi:10.1109/pdgc.2016.7913161

Cited by 13 publications

(11 citation statements)

References 7 publications

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“…Bacilli segmentation and classification was performed using a watershed segmentation method on ZNSM-iDB datasets. 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.…”

Section: Bacilli Segmentation and Classificationmentioning

confidence: 96%

“…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. In an another study, images were divided into four groups based on the infection level (Table 2), 35 and sensitivity and specificity of the watershed segmentation method for classifying an image as TB positive or negative were determined for each group.…”

Section: Data Validationmentioning

confidence: 99%

“…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. The performance of this segmentation method is related to the level of infection where a higher level of infection yields a better performance.…”

Section: Bacilli Segmentation and Classificationmentioning

confidence: 99%

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

et al. 2017

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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.

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Section: Bacilli Segmentation and Classificationmentioning

confidence: 96%

Section: Data Validationmentioning

confidence: 99%

Section: Bacilli Segmentation and Classificationmentioning

confidence: 99%

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

et al. 2017

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“…In [70], a detection algorithm is proposed to segment and classify TB bacilli from microscopic images captured by smart phone camera. In the algorithm, RGB to gray level conversion takes place, followed by contrast enhancement, then thresholding and morphological operations are applied, after this, the artefacts are removed based on shape and size (to remain with bacilli only), then watershed method is applied for separation of overlapping (touching) bacilli, finally counting and labelling of bacilli is done.…”

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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“…Song et al (2017) and Momenzadeh, Vard, Talebi, Mehri Dehnavi, & Rabbani (2018) proposed the computer-aided diagnostic systems for microscopic images using segmentation and classification methods. Zhai, Liu, Zhou, & Liu (2010) and Shah, Mishra, Sarkar, & Sudarshan (2016) proposed a fully automated M. tuberculosis identification system, consisting of image capturing, microscopy system setting, and identification methods. In general, a flowchart of these conventional methods is summarized in Figure 3a The support vector machine (SVM) has been a powerful supervised machine learning method (Cortes & Vapnik, 1995).…”

Section: Introductionmentioning

confidence: 99%

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

Kuok

Horng

Liao

et al. 2019

Microscopy Res & Technique

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Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time‐consuming and tedious. Therefore, an effective computer‐aided image identification system is needed to provide timely assistance in diagnosis. The current identification system usually suffers from complex color variations of the images, resulting in plentiful of false object detection. To overcome the dilemma, we propose a two‐stage Mycobacterium tuberculosis identification system, consisting of candidate detection and classification using convolution neural networks (CNNs). The refined Faster region‐based CNN was used to distinguish candidates of M. tuberculosis and the actual ones were classified by utilizing CNN‐based classifier. We first compared three different CNNs, including ensemble CNN, single‐member CNN, and deep CNN. The experimental results showed that both ensemble and deep CNNs were on par with similar identification performance when analyzing more than 19,000 images. A much better recall value was achieved by using our proposed system in comparison with conventional pixel‐based support vector machine method for M. tuberculosis bacilli detection.

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Automatic detection and classification of tuberculosis bacilli from camera-enabled smartphone microscopic images

Cited by 13 publications

References 7 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

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

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

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