Introduction: According to the Global TB control report of 2013, "Tuberculosis (TB) remains a major global health problem. In 2012, an estimated 8.6 million people developed TB and 1.3 million died from the disease. Two main sputum smear microscopy techniques are used for TB diagnosis: Fluorescence microscopy and conventional microscopy. Fluorescence microscopy is a more expensive diagnostic method because of the high costs of the microscopy unit and its maintenance. Therefore, conventional microscopy is more appropriate for use in developing countries. Methods: This paper presents a new method for detecting tuberculosis bacillus in conventional sputum smear microscopy. The method consists of two main steps, bacillus segmentation and post-processing. In the fi rst step, the scalar selection technique was used to select input variables for the segmentation classifi ers from four color spaces. Thirty features were used, including the subtractions of the color components of different color spaces. In the post-processing step, three fi lters were used to separate bacilli from artifact: a size fi lter, a geometric fi lter and a Rule-based fi lter that uses the components of the RGB color space. Results: In bacillus identifi cation, an overall sensitivity of 96.80% and an error rate of 3.38% were obtained. An image database with 120-sputum-smear microscopy slices of 12 patients with objects marked as bacillus, agglomerated bacillus and artifact was generated and is now available online. Conclusions: The best results were obtained with a support vector machine in bacillus segmentation associated with the application of the three post-processing fi lters.

In this work, we present an image database for automatic bacilli detection in sputum smear microscopy. The database comprises two parts. The first one, called the autofocus database, contains 1200 images with resolution of 2816 × 2112 pixels. This database was obtained from 12 slides, with 10 fields per slide. Each stack is composed of 10 images, with the fifth image in focus. The second one, called the segmentation and classification database, contains 120 images with resolution of 2816×2112 pixels. This database was obtained from 12 slices, with 10 fields per slice. In both databases, the images were acquired from fields of slides stained with the standard Kinyoun method. In both databases, accordingly to the background content, the images were classified as belonging to high background content or low background content. In all 120 images of segmentation and classification database, the identified objects were enclosed within a geometric shape by a trained technician. A true bacillus was enclosed in a circle. An agglomerated bacillus was enclosed by a rectangle and a doubtful bacillus (the image focus or geometry does not allow a clear identification of the object) was enclosed by a polygon. These marked objects could be used as a gold standard to calculate the accuracy, sensitivity and specificity of bacilli recognition.

This paper presents a new method for segmentation of tuberculosis bacillus in conventional sputum smear microscopy. The method comprises three main steps. In the first step, a scalar selection are made for characteristics from the following color spaces: RGB, HSI, YCbCr and Lab. The features used for pixel classification in the segmentation step were the components and subtraction of components of these color spaces. In the second step, a feedforward neural network pixel classifier, using selected characteristics as inputs, is applied to segment pixels that belong to bacilli from the background. In third step geometric characteristics, especially the eccentricity, and a new proposed color characteristic, the color ratio, are used to noise filtering. The best sensitivity achieved in bacilli detection was 91.5%.

When materials are subjected to irradiation by energetic particles such as fast neutrons, the primary radiation damage event is the formation of vacancies, interstitials, and more complex defects along the trajectory of the recoil lattice atoms. Defects are also formed by purely ionizing radiations such as gamma rays. Several different mechanisms have been proposed to account for this type of defect formation and these are discussed. One of the fundamental problems of radiation damage is to determine the various types of defects formed, their concentration, spatial distribution, rate of formation, and so forth, and how the properties of each type of defect depend on parameters such as the energy of the recoiling atom, the sample temperature, etc. In nonmetals, some of these defects are, or can be, converted into centers that absorb light, or color centers. Each of these centers gives rise to different optical absorption bands, and from absorption measurements the center or defect concentration can be computed. Unfortunately, numerous absorption bands have been found in many crystals that have not been unequivocally attributed to specific defects. In order to use this technique to determine defect concentrations accurately, it is essential to ensure that at least a known fraction of the defects have captured electrons or holes. In many instances the conditions required to do this can be determined by studying the conversion of existing defects into color centers by purely ionizing radiation, such as X-rays. The techniques described in this paper have been used to show that the number of defects produced in sodium chloride by fast reactor neutrons are consistent with existing theories on radiation damage. They have also been used to study the growth and annealing of the defects formed in aluminum oxide, and fused silica by reactor irradiations.