Automatic Detection Of Mycobacterium Tuberculosis Using Microscopic Images

Tuberculosis (TB) is a serious communicable chronic disease which is casued bythe Mycobacterium Tuberculosis. The conventional method employed for TB diagnosisinvolves a labor-intensive task with poor sensitivity and requires highly trained experts.To overcome these shortcomings, this dissertation employed image process and patternrecognition techniques for automatic TB diagnosis. Two key technologies employed forautomatic TB indentification, including the microscopy autofocusing technique and theobject detection technique, were studied. The technical contribution of this dissertationis summarized as follows:First, in microscopy autofocusing the following contribution is made:(1) Wedeveloped six quantative performance metrics of focus function, including the width ofsloped part of focus curve, the steepness, the variance of flat part of the focus curve,et.al. These metrics provide the quantative standards for focus function design andperformance evaluation;(2) To overcome the shortcoming that the traditionalautofocusing algorithms may fail to find the optimal focal plane under thecircumstances with low image content densities, this dissertation proposed a contentbased focus measure for guiding automatic search of the optimal focal plane. Theexperimental results show that performance of the proposed method is far superior tothe traditional methods: the autofocusing success rate of the proposed method is largerthan90%under the circumstances with low image content density while the traditionalmethod only gains a success rate of24%;(3) A continuous autofocusing method, inwhich the z-stage keeps moving during the search process and the image exposure andthe subsequent focus measure calculation are carried out in parallel with the z-stagemovement, was proposed. This dissertation established a continuous autofocusingmodel and the relationship of the motor speed, the frame rate, and the depth of field ofthe optical system, which is essentially important for achieving continuous autofocusing.Moreover, a heuristic initial search direction selection method was proposed, whichdramatically improve the speed of autofocusing in muli-fileds scanning system.Experimental investigation has been conducted based on our automatic microscopysystem. The experimental results show that the proposed method gains a success rate of96.7%and the autofocusing speed is2.2times faster than the tranditional “stop-and-go”method. These results confirm the superior performance of the proposed method overthe traditional methods.In automatic detection of Mycobacterium Tuberculosis (TB) in microscopicimages, we have made the following contribution:(1) A color model and a deformableshape model of the TB object were established. Moreover, a landmark automaticannotation method which is based on the morphologyical skelecton was proposed and a skelecton based simplified deformable shape model was established. The skelectonbased shape model outperforms the tranditional coutour based shape model not only forits low complexity but also for its capability that makes the landmark be auto-annotated,which makes the shape parameter auto-extraction and recognition possible;(2) A coarseto fine, multi-stage, multi-level image segmentation framework was established to dealwith the complicated situation in practical image segmentaion task. Moreover, aclassification and recognition algorithm based on the shape feature descriptors and thedecision tree classifier was proposed.In order to demonstrate the performance of the proposed algorithms, two types ofexperiments had been condoncted, including the typical samples experiment using7typical TB images and the statistical experiments using22836TB images randomlyselected from larger number of TB specimens. Experimental results show that theproposed algorithm can accommodate the complex variety of specimens and the imagebackground. The statistical experiments result show that the sensitivity (true posistiverate) and the specificity (true negative rate) are95.2%and91%respectively. This is agood score considering that the samples used in the experiments are randomly selectedfrom TB specimens whose image quality may be influenced by many factors. In futurework, we will do futher effort to improve the performance especially the specificity ofthe algorithm.