Detection of Tuberculosis in Sputum Smear Images by Gaussian Mixture Models

结核病是严重危害人类健康的一类疾病。通过计算机图像处理手段进行自动检测结核菌计数可以大幅提高医生诊断效率。高斯混合模型是单一高斯分布的延伸,是使用多个高斯分布加权来拟合给定的数据样本,通过确定拟合参数确定每个样本的分类概率。该文首先通过向量量化算法对图像预处理,降低所需处理数据量,然后从HSV、CIEl*A*b*、yCbCr颜色空间提取特征分量并送入高斯混合模型进行训练。根据实验结果,高斯混合模型比其他无监督分类算法(如k-MEAnS算法)准确度更高,与有监督的分类算法(如朴素贝叶斯分类算法)相比可以简化训练样本的制作,具有一定优势。Cell recognition plays an important role in medical image-processing.First,we preprocess the images with vector quantization algorithm to reduce the computation.Then we extract different feature channels from HSV,CIEL*a*b* and YCbCr color spaces and put them into a Gaussian mixture model.Gaussian mixture models is a mature method for clustering unknown data.To determine the parameters of GMM,we use expectation maximization algorithm,which uses unlabeled data for model training.The experiment shows GMM finished the initial work of TB detection,while its performance wasn't high enough.基于前节OCT图像的闭角型青光眼诊断及治疗仿真方法研

Tuberculosis Disease Detection through CXR Images based on Deep Neural Network Approach

Tuberculosis (TB) is a disease that, if left untreated for an extended period of time, can ultimately be fatal. Early TB detection can be aided by using a deep learning ensemble. In previous work, ensemble classifiers were only trained on images that shared similar characteristics. It is necessary for an ensemble to produce a diverse set of errors in order for it to be useful; this can be accomplished by making use of a number of different classifiers and/or features. In light of this, a brand-new framework has been constructed in this study for the purpose of segmenting and identifying TB in human Chest X-ray. It was determined that searching traditional web databases for chest X-ray was necessary. At this point, we pass the photos that we have collected over to Swin ResUnet3 so that they may be segmented. After the segmented chest X-ray have been provided to it, the Multi-scale Attention-based Densenet with Extreme Learning Machine (MAD-ELM) model will be applied in the detection stage in order to effectively diagnose tuberculosis from human chest X-ray. This will be done in order to maximize efficiency. Because it increased the variety of errors made by the basic classifiers, the supplied variation of the approach that was proposed was able to detect tuberculosis more effectively. The proposed ensemble method produced results with an accuracy of 94.2 percent, which are comparable to those obtained by past efforts

Network analysis of Diagnostic Medical Device Development for Infectious Diseases Prevalent in South Africa

Infectious diseases are a major health concern in South Africa and many other developing countries. The local development of medical devices for infectious diseases in such settings, utilizing the local knowledge base, has the potential to improve the accuracy of and access to diagnoses and to lead to the devices being more context-appropriate and affordable. The aim of this project was to examine the landscape of diagnostic medical device development targeting infectious diseases prevalent in South Africa for the period 2000-2016, particularly with regard to collaboration between institutions in different sectors and the contributions of different collaborators. Such knowledge would be beneficial to future technological and policy developments aimed at improving access to diagnostic services and treatment in the South African context. Collaboration across four sectors was considered: university, hospital, industry and science councils and facilities. A bibliometric analysis was performed, and publications documenting medical device development for diagnosis of infectious diseases were extracted. Co-authorship of the journal and conference articles was used as a proxy for collaboration across organisations. Affiliation data extracted from the publications were used to generate a collaboration network. Netdraw, a network visualisation tool, was used to visualize the network, and network metrics such as degree centrality, betweenness centrality and closeness centrality, as well as group density measures, were produced using UCINET software. The collaboration network and the network metrics were used to determine which organisations have collaborated and which collaborators have played the most active and influential roles in diagnostic device development. The university sector was found to make the largest contribution to the development of diagnostic medical devices in South Africa, and also played a key role in transmitting information throughout the network due to its high frequency of connections to other organisations. The most prevalent type of inter-sectoral collaboration was between universities and science councils and facilities, while universities were found to collaborate most amongst themselves with regard to intrasectoral collaboration. Foreign organisations played a prominent role in diagnostic device development between 2012 and 2016. Tuberculosis was the most prevalent infectious disease in diagnostic device development research, and computer-aided detection was a common feature of research on diagnostic device development

Klasifikasi Bakteri Tuberkulosis pada Sampel Dahak Menggunakan K-Nearest Neighbor (K-NN) dan Backpropagation

Penelitian ini bertujuan untuk melakukan klasifikasi bakteri tuberkulosis berdasarkan fitur bentuk bakteri. Citra preparat digital dikonversi dari kanal warna Red, Green, Blue (RGB) ke Hue, Saturation, Value (HSV), kemudian dilakukan operasi morfologi untuk memperbaiki bentuk bakteri. Bakteri dipotong secara otomatis dari gambar digital preparat menggunakan Region of Interest (ROI), potongan bakteri hasil ROI kemudian di skeletonizing untuk mendapatkan bentuk bakteri dengan lebar satu piksel. Langkah selanjutnya dilokalisasi untuk memisahkan bagian yang bukan termasuk bakteri tuberkulosis. Fitur yang digunakan antara lain panjang bakteri, endpoint dengan menerapkan ridge ending dari minutiae, dan percabangan bakteri dengan menerapkan bifurcation dari minutiae. Fitur-fitur tersebut menjadi masukan pada proses klasifikasi. K-NN mampu mengklasifikasi bakteri tunggal dengan akurasi 88.16% dan bakteri rangkap sebesar 88.16%. Backpropagation mampu mengklasifikasi antara bakteri tunggal dengan akurasi 87.28% dan bakteri rangkap dengan akurasi 87.28%. K-Nearest Neighbor (K-NN) mampu mengklasifikasi kelompok preparat kuning dengan akurasi 93.22%, kelompok preparat hijau dengan akurasi 92%, kelompok preparat biru dengan akurasi 90.63% dan kelompok preparat gelap dengan akurasi 75%. Sementara backpropagation mampu mengklasifikasi kelompok preparat kuning dengan akurasi 91.53%, kelompok preparat hijau dengan akurasi 92%, kelompok preparat biru dengan akurasi 92.71%, kelompok gelap dengan akurasi 68.75%. Metode K-NN lebih unggul dalam akurasi klasifikasi pada kelompok preparat kuning, dan kelompok preparat gelap. Dan metode backpropagation lebih unggul pada kelompok preparat biru. Sedangkan dalam kelompok preparat hijau K-NN dan backpropagation memiliki akurasi klasifikasi sama sebesar 92%.Metode K-NN lebih unggul dalam mengklasifikasi jenis bakteri tunggal dan rangkap dari pada metode backpropagation. Sistem ini mampu digunakan sebagai alat bantu bagi dokter dan analis medis untuk mempercepat proses penghitungan bakteri tuberkulosis dan diagnosa pasien tuberkulosis pada bidang kesehatan. ======================================================================================================== This study aims to classify tuberculosis bacteria based on the features of bacterial forms. Digital image preparations are converted from RGB color channels to Hue, Saturation, Value (HSV), then morphologic surgery to repair bacterial forms. The bacteria is automatically cut from the digital image of the preparat using Region of Interest (ROI), piece of bacteria resulting from ROI then skeletonizing to obtain bacterial form with a width of one pixel. The next step is localized to separate parts that do not belong to tuberculosis bacteria. Features used include bacterial length, endpoints by applying ridge ending of minutiae, and branch of bacteria by applying bifurcation of minutiae. These features become input to the classification process. K-Nearest Neighbor (K-NN) is able to classify single bacteria with 88.16% and multiple bacteria with 88.16% accuracy. Backpropagation is able to classify between single bacteria with 87.28% and multiple bacteria with 87.28% accuracy. K-NN was able to classify yellow preparat groups with 93.22% accuracy, green preparat group with 92% accuracy, blue preparat group with 90.63% accuracy, and dark preparat group with 75% accuracy. Backpropagation was able to classify yellow preparat groups with 91.53% accuracy, green preparat group with 92% accuracy, blue preparat group with 92.71% accuracy, dark preparat group with 68.75% accuracy. The K-NN method is better in the classification accuracy of the yellow preparat group, and the dark preparat group. And the backpropagation method is better to the blue preparat group. While in the group of green preparat K-NN and backpropagation have the same classification accuracy of 92%. K-NN method is better in classifying single and multiple bacteria types than the backpropagation method.This system can be used as a tool for doctors and medical analysts to speed up the process of calculating tuberculosis bacteria and diagnosis of tuberculosis patients in the health field

Microscopio automatizado: conteo de bacilos de tuberculosis

La prueba baciloscópica de la tuberculosis es la forma de diagnóstico microscópico más utilizado para combatir la enfermedad en los países pobres o subdesarrollados debido a su bajo costo y rapidez. Sin embargo, la realización de esta prueba es un proceso tedioso, extenuante y requiere de un especialista debidamente capacitado. Por ello, en el presente trabajo se presenta un algoritmo automatizado para la detección y conteo de bacilos de tuberculosis presentes en imágenes de muestras de esputo mediante la utilización de técnicas de procesamiento de imágenes digitales. Se analizaron diferentes espacios de color para hallar aquella capa o canal de color que posea un mayor contraste entre las intensidades de color de los píxeles de los bacilos y del fondo. Para esto se hizo un análisis de los histogramas mediante las gráficas de las características operativas del receptor. Para la segmentación de los bacilos, el presente trabajo desarrolló una técnica de umbralización adaptativa utilizando el método de Otsu para hallar el óptimo valor umbral. Luego, los objetos detectados son clasificados como bacilos o no-bacilos mediante un árbol de clasificación utilizando características de área y excentricidad. El algoritmo desarrollado presenta niveles de sensibilidad, especificidad y exactitud mayores a 90% y tiene un tiempo de ejecución de aproximadamente 9 segundos por campo (15 minutos para 100 campos). Cabe resaltar que, a diferencia de investigaciones previas, la presente tesis buscó desarrollar un algoritmo tanto de segmentación de los bacilos, como de su clasificación, e implementarlo en un microscopio automatizado para el diagnóstico automático de la enfermedad en tiempo real. Con esta finalidad, se implementó el algoritmo desarrollado con el programa Matlab® en un lenguaje de programación C++, obteniendo un programa capaz de interactuar con otros programas como el del control de la cámara digital. Se espera que este trabajo sirva de base para próximos estudios orientados a automatizar el proceso de diagnóstico de la enfermedad de una manera más óptima y veloz.Tesi

Hardware and software integration and testing for the automation of bright-field microscopy for tuberculosis detection

Automated microscopy for the detection of tuberculosis (TB) in sputum smears would reduce the load on technicians, especially in countries with a high TB burden. This dissertation reports on the development and testing of an automated system built around a conventional microscope for the detection of TB in Ziehl-Neelsen (ZN) stained sputum smears. Microscope auto-focusing, image analysis and stage movement were integrated. Images were captured at 40x magnification

Classification of Mycobacterium tuberculosis in Images of ZN-Stained Sputum Smears

Screening for tuberculosis (TB) in low- and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl-Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice