Tuberculosis bacteria counting using watershed segmentation technique

Tuberculosis (TB) is the second biggest killer disease after HIV. Therefore, early detection is vital to prevent its outbreak. This paper looked at an automated TB bacteria counting using Image Processing technique and Matlab Graphical User Interface (GUI) for analysing the results. The image processing algorithms used in this project involved Image Acquisition, Image Pre-processing and Image Segmentation. In order to separate any overlap between the TB bacteria, Watershed Segmentation techniques was proposed and implemented. There are two techniques in Watershed Segmentation which is Watershed Distance Transform Segmentation and Marker Based Watershed Segmentation. Marker Based Watershed Segmentation had 81.08 % accuracy compared with Distance Transform with an accuracy of 59.06%. These accuracies were benchmarked with manual inspection. It was observed that Distance Transform Watershed Segmentation has disadvantages over segmentation and produce inaccurate results. Automatic counting of TB bacteria algorithms have also been proven to be less time consuming, contains less human error and consumes less man-power

Segmentation of candidate bacillus objects in images of Ziehl-Neelsen-stained sputum smears using deformable models

Includes abstract.Includes bibliographical references (leaves 83-88).Automated microscopy for the detection of tuberculosis (TB) in sputum smears seeks to address the strain on technicians and to achieve faster diagnosis in order to cope with the rising number of TB cases. Image processing techniques provide a useful alternative to the conventional, manual analysis of sputum smears for diagnosis. In the project described here, the use of parametric and geometric deformable models was explored for segmentation of TB bacilli in images of Ziehl-Neelsen-stained sputum smears for automated TB diagnosis. The goal of segmentation is to produce candidate bacillus objects for input into a classifier

Method validation of High Performance Liquid Chromatography for determination of mycolic acid profile of Mycobacterium tuberculosis

This study developed the High Performance Liquid Chromatography (HPLC) for INH resistant M. tuberculosis (MTB) isolate identification based on a chromatogram profile of mycolic acids (MAs). The aim of this study was to validate the HPLC for determining the characteristic profile of MAs chromatogram of the INH resistant MTB. The optimum derivatization process obtained was as follows: the minimum biomass weight was 25 mg. The experimental temperature was performed in (80-90)o C using a water bath for minimum 30 minutes in order to complete the MAs derivatization. Reagent volume used in the range of (200-500 μL) were not influenced the MAs chromatogram profile. The optimum condition of HPLC was as follows: mobile phase was methanol:isopropanol (60:40) for 3 minutes, followed by gradient elution (4:96) in 50 minutes. Thereafter, the mobile phase composition change gradually for 40 minutes to a final composition of (60:40). The sample volume was 20 μL and the mobile phase flow rate was 1 mL/minute. The result of this study showed that the MAs chromatogram profile of INH resistant MTB looked like H37Rv MTB strain. The chromatogramp profile was a cluster with 6 characteristic peaks at the end of the analysis. The other short chain carbon fatty acids were eluted in the first 15 minutes

Development and Investigation of a rabbit model of tuberculosis tissue destruction

Tuberculosis kills more people than any other bacterial disease. The characteristic tissue destruction that occurs during infection contributes to morbidity, mortality, and transmission. Tissue damage limits antibiotic effectiveness, and generates regions of immune privilege. Therefore, therapies that target tissue destruction may improve treatment outcomes. Currently, modelling tissue destruction in vivo requires the infection of large animals for prolonged periods. These models are highly variable in outcome. This makes experimentation challenging, and limits there use in testing therapeutic strategies, which in turn limits the progression of potential therapies to clinical trials. This thesis outlines the development of a highly consistent rabbit model of cavitary tuberculosis, in which novel therapies can be investigated, using small groups of animals. A method to assess pathology in vivo by breath-hold computed tomography was also developed. Matrix metalloproteinase-1 is confirmed as a potential mediator of tissue destruction, and cathepsin K is newly identified as a potential mediator of tissue destruction. These collagenases are targetable with the drugs Cipemastat and Odanacatib respectively, both of which are safe in man. This thesis provides a novel system for trialling treatments for tuberculosis in the context of human-like, tissue-destructive pathology. This may facilitate the selection of both antibiotic and non-antibiotic treatment strategies for tuberculosis. This model will allow for a better understanding of the physical, chemical, molecular, genetic and immunological characteristics and determinants of cavitary tuberculosis.Open Acces

Development and clinical translation of optical and software methods for endomicroscopic imaging

Endomicroscopy is an emerging technology that aims to improve clinical diagnostics by allowing for in vivo microscopy in difficult to reach areas of the body. This is most commonly achieved by using coherent fibre bundles to relay light for illumination and imaging to and from the area under investigation. Endomicroscopy’s attraction for researchers and clinicians is two-fold: on the one hand, its use can reduce the invasiveness of a diagnostic procedure by removing the need for biopsies; On the other hand, it allows for structural and functional in vivo imaging. Endomicroscopic images acquired through optical fibre bundles exhibit artefacts that deteriorate image quality and contrast. This thesis aims to improve an existing endomicroscopy imaging system by exploring two methods that mitigate these artefacts. The first, software-based method takes several processing steps from literature and implements them in an existing endomicroscopy device with a focus on real-time application to enable clinical use, after image quality was found to be inadequate without further processing. A contribution to the field is that two different approaches are implemented and compared in quantitative and qualitative means that have not been compared directly in this manner before. This first attempt at improving endomicroscopy image quality relies solely on digital image processing methods and is developed with a strong focus on real-time applicability in clinical use. Both approaches are compared on pre-clinical and clinical human imaging data. The second method targets the effect of inter-core coupling, which reduces contrast in fibre images. A parallelised confocal imaging method is developed in which a sequence of images is acquired while selectively illuminating groups of fibre cores through the use of a spatial light modulator. A bespoke algorithm creates a composite image in a final processing step. In doing so, unwanted light is detected and removed from the final image. This method is shown to reduce the negative impact of inter-core coupling on image contrast on small imaging targets, while no benefit was found in large, scattering samples