Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Pattern Recognition and Artificial Intelligence

Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.

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Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments

Methods and systems for generating a depth map are provided. The method includes projecting an infrared (IR) dot pattern onto a scene. The method also includes capturing stereo images from each of two or more synchronized IR cameras, detecting a number of dots within the stereo images, computing a number of feature descriptors for the dots in the stereo images, and computing a disparity map between the stereo images. The method further includes generating a depth map for the scene using the disparity map.

Generating a depth map

Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming; a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research.

A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images

Tuberculosis (TB) is a communicable disease for which early diagnosis is critical for disease control. Manual screening for TB identification involves a labor-intensive task with poor sensitivity and specificity. To improve the diagnostic process we develop an automated system for TB identification, which consists of an automatic microscope, an image-based autofocus algorithm and an image-based TB identification algorithm. The system can automatically capture a large number of clear images on sputum sample and process all the images in real time to identify the bacilli and count their number. In order to speed up image acquisition while guaranteeing the image quality, an efficient method for capturing the images is proposed. To obtain fine segmentation results, a two-stage segmentation method based on both the HSV and CIE L*a*b* color space is developed. To identify the TB bacilli, the algorithm uses three shape feature descriptors, which are area, compactness and roughness, and makes the judgment using a decision tree. Experimental results confirmed the superior performance of the proposed algorithm.

Automatic identification of mycobacterium tuberculosis from ZN-stained sputum smear: Algorithm and system design

The invention relates to the technical field of navigation systems, in particular to a blind autonomous navigation method based on the stereoscopic vision and the information fusion. The output part of a left image and right image is connected with an image preprocessing part, the output of the image preprocessing part is connected with an image segmentation part, the image segmentation part is connected with a characteristic extraction part, the output of the characteristic extraction part is connected with a stereographic matching part, the output of the stereographic matching part is connected with a parallax chart obtaining part, the output of the parallax chart obtaining part is connected with a depth image obtaining part and a target detection and marking part, the output of the depth image obtaining part is connected with the target detection and marking part, the output of the target detection and marking part is connected with a target size, distance, direction and calculation participation part, the output of the target size, distance, direction and calculation participation part is connected with a multi-sensor information fusion part, the output of the multi-sensor information fusion part is connected with a voice output part of information, such as obstacle position, size, distance, direction and the like, and the outputs of an ultrasonic wave ranging part, an electronic compass direction finding part and a GPS positioning part are connected with the multi-sensor information fusion part. In the invention, the algorithm has great use value for improving the autonomy and the independent walking ability of the blind.

Blind autonomous navigation method based on stereoscopic vision and information fusion

The invention relates to the technical field of navigation systems, in particular to a portable blindman independent navigation system. The portable blindman independent navigation system is a main glasses body, which comprises a glasses framework, pinhole cameras arranged on both sides of the main glasses framework, a GPS (Global Position System) arranged in the middle of the main glasses body, earphones arranged at the tail ends of the glasses framework and embedded hardware systems arranged in lenses of the main glasses body; wherein the embedded hardware systems arranged in the lenses of the main glasses body comprise extension ports, ARM (Advanced RISC Machines, RISC is short for Reduced Instruction-Set Computer) systems, videos, ARM inner cores, DSP (Digital Signal Processor) inner cores, FPGs and Linux real-time operation systems. In the portable blindman independent navigation system, the embedded systems are used for carrying out three-dimensional information extraction on binocular images acquired by the pinhole cameras, a built-in navigation map and GPS information are used for comparison to acquire relevant information of positions, directions, front barriers and the like and converting results into voice signals to inform blindmen.

Portable blindman independent navigation system

The invention relates to the technical field of automatic focusing of microscopes, in particular to an image processing based fast automatic focusing method of a microscope. In the method, the automatic focusing process is divided into a coarse focusing stage and a fine focusing stage; different focusing evaluation functions and different extremum searching strategies are adopted on different stages, specifically, on the coarse focusing stage, the gray scale different evaluation function and a fast hill-climbing backtracking search algorithm are adopted, and decision-making factors are introduced to prevent the search algorithm from falling into local extrema; and on the fine focusing stage, the Laplace operator based evaluation function and the small-step searching strategy are adopted. The method provided by the invention better solves the problem that the traditional algorithm is slow in focusing speed, easily falls into local extrema and is not high in focusing accuracy, and has strong real-time property and high reliability and practicability.

Image processing based fast automatic focusing method of microscope

The invention relates to the field of medical image processing and mode recognition, in particular to a tubercle bacillus target recognizing and counting algorithm based on diverse characteristics. The algorithm comprises the following steps of: image preprocessing: carrying out image reinforcement and constructing median filter and Gaussian filter on a tubercle bacillus microimage; color image partition: carrying out fixed threshold partition based on HSV (Hue-Saturation-Value) color space on a preprocessed image and then carrying out adaptive threshold partition which is based on CIE L*a*b* color space and keeps a geometric shape of a target; communication block morphological analysis and target recognition: carrying out communication block analysis on the partitioned image; and tubercle bacillus target counting: estimating the quantity of tubercle bacillus targets in the image by utilizing a histogram statistics and multistrategy calculation method. The invention can effectively extract the bacillus targets in the tubercle bacillus microimage subjected to acid-fast stain from background and impurities and carry out accurate counting, thereby realizing the automation and the intellectualization of the detection of tubercle bacilli.

Tubercle bacillus target recognizing and counting algorithm based on diverse characteristics

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